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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kenichinakanishi/houseplant_classifier/blob/master/Part_4_TorchVision_Models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V3qZVyWrAmwO"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dn8UTbPgmYp6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "390873e8-e4bc-4721-c96e-e6a3d466c121"
      },
      "source": [
        "# Check GPU on Colabs\n",
        "# NVIDIA-SMI 455.32.00 Driver Version: 418.67 CUDA Version: 10.1 Tesla V100-SXM2\n",
        "! nvidia-smi "
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Wed Dec  2 01:07:22 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 455.38       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   36C    P0    28W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fkt6m5UAoafd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8bae2bd2-52fa-4726-a9a5-f5957aff7bfe"
      },
      "source": [
        "# Run once per session\n",
        "!pip install fastai==2.1.2 -q\n",
        "!pip install fastcore==1.3.1 -q\n",
        "!pip install torch==1.7.0 -q\n",
        "from fastai.basics import *\n",
        "from fastai.vision.all import *\n",
        "from fastai.callback.all import *\n",
        "from fastai.callback.cutmix import *\n",
        "from fastai.distributed import *\n",
        "from fastprogress import fastprogress\n",
        "from torchvision.models import *\n",
        "from fastcore.script import *\n",
        "fastprogress.MAX_COLS = 80"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 194kB 6.7MB/s \n",
            "\u001b[K     |████████████████████████████████| 61kB 5.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 51kB 2.9MB/s \n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBT0lMmQV6MG"
      },
      "source": [
        "# Import source of efficientnet models and weights\n",
        "!pip install geffnet -q\n",
        "import geffnet\n",
        "\n",
        "! pip install pytorchcv torch>=0.4.0 -q\n",
        "from pytorchcv.model_provider import get_model as ptcv_get_model"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZXrTipw8mXN-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "caf5d5bf-bfb2-4687-d8f9-036de13723a3"
      },
      "source": [
        "torch.cuda.is_available()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tm8JPQO-q6Mh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5948f579-c186-41a4-d42d-9c7a6528e9c5"
      },
      "source": [
        "# memory footprint support libraries/code\n",
        "!ln -sf /opt/bin/nvidia-smi /usr/bin/nvidia-smi\n",
        "!pip install gputil -q\n",
        "!pip install psutil -q\n",
        "!pip install humanize -q\n",
        "import psutil\n",
        "import humanize\n",
        "import os\n",
        "import GPUtil as GPU\n",
        "GPUs = GPU.getGPUs()\n",
        "# XXX: only one GPU on Colab and isn’t guaranteed\n",
        "gpu = GPUs[0]\n",
        "def printm():\n",
        " process = psutil.Process(os.getpid())\n",
        " print(\"Gen RAM Free: \" + humanize.naturalsize( psutil.virtual_memory().available ), \" | Proc size: \" + humanize.naturalsize( process.memory_info().rss))\n",
        " print(\"GPU RAM Free: {0:.0f}MB | Used: {1:.0f}MB | Util {2:3.0f}% | Total {3:.0f}MB\".format(gpu.memoryFree, gpu.memoryUsed, gpu.memoryUtil*100, gpu.memoryTotal))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  Building wheel for gputil (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "94WwyVlMm79h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1860459-04eb-4caf-ef06-b59c63286a05"
      },
      "source": [
        "printm()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Gen RAM Free: 26.3 GB  | Proc size: 307.5 MB\n",
            "GPU RAM Free: 16270MB | Used: 10MB | Util   0% | Total 16280MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2IZ50gVCzM8S"
      },
      "source": [
        "# Download and Unzip the File"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t1vslpDaoVgV"
      },
      "source": [
        "The use of images directly from a google drive is very slow.\n",
        "This is because even though the drive appears to be direcly attached to our colab instance, in actuality we are dynamically downloading and accessing each image one by one - leading to extremely long training times. \n",
        "\n",
        "At this point, I've gone to the directory the image files were downloaded to, zipped them up and placed that master zip file into my google drive for importing and processing. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BC9ft3Alg6hC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63fcd9ec-ea2f-493f-8b7a-7aeb9eb1f9e1"
      },
      "source": [
        "# If your zip file is on your google drive - download it into the current kernel\n",
        "!cp '/content/drive/My Drive/Houseplant Classifier/plant_images_database_manualcleanedlosses10.zip' 'plant_images_database_manualcleanedlosses10.zip'\n",
        "# This code snippet unzips a zip file to a specified destination\n",
        "!unzip -q \"/content/plant_images_database_manualcleanedlosses10.zip\" -d \"/content\";"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
            "plant_images_database_manualcleanedlosses10/Citrus ×aurantium/00000001.jpg:  mismatching \"local\" filename (plant_images_database_manualcleanedlosses10/Citrus ├Чaurantium/00000001.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_images_database_manualcleanedlosses10/Citrus ×aurantium/00000002.jpg:  mismatching \"local\" filename (plant_images_database_manualcleanedlosses10/Citrus ├Чaurantium/00000002.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_images_database_manualcleanedlosses10/Citrus ×aurantium/00000003.jpg:  mismatching \"local\" filename (plant_images_database_manualcleanedlosses10/Citrus ├Чaurantium/00000003.jpg),\n",
            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "plant_images_database_manualcleanedlosses10/Citrus ×aurantium/00000423.jpg:  mismatching \"local\" filename (plant_images_database_manualcleanedlosses10/Citrus ├Чaurantium/00000423.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_images_database_manualcleanedlosses10/Citrus ×aurantium/00000424.jpg:  mismatching \"local\" filename (plant_images_database_manualcleanedlosses10/Citrus ├Чaurantium/00000424.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_images_database_manualcleanedlosses10/Citrus ×aurantium/00000427.jpg:  mismatching \"local\" filename (plant_images_database_manualcleanedlosses10/Citrus ├Чaurantium/00000427.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_images_database_manualcleanedlosses10/Citrus ×aurantium/00000428.jpg:  mismatching \"local\" filename (plant_images_database_manualcleanedlosses10/Citrus ├Чaurantium/00000428.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_images_database_manualcleanedlosses10/Citrus ×aurantium/00000429.jpg:  mismatching \"local\" filename (plant_images_database_manualcleanedlosses10/Citrus ├Чaurantium/00000429.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_images_database_manualcleanedlosses10/Citrus ×aurantium/00000434.jpg:  mismatching \"local\" filename (plant_images_database_manualcleanedlosses10/Citrus ├Чaurantium/00000434.jpg),\n",
            "         continuing with \"central\" filename version\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d79jZGw1tD4c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45868df6-5737-4a61-85dc-e3c6f612e96c"
      },
      "source": [
        "# If your zip file is on your google drive - download it into the current kernel\n",
        "!cp '/content/drive/My Drive/Houseplant Classifier/plant_images_database.zip' 'plant_images_database.zip'\n",
        "# This code snippet unzips a zip file to a specified destination\n",
        "!unzip -q \"/content/plant_images_database.zip\" -d \"/content\";"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000004.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000004.jpg),\n",
            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "plant_image_database/Citrus ×aurantium/00000022.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000022.jpg),\n",
            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000025.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000025.jpg),\n",
            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000048.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000048.jpg),\n",
            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000050.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000050.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000051.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000051.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000052.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000052.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000053.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000053.jpg),\n",
            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000113.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000113.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000114.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000114.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000115.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000115.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000116.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000116.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000117.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000117.jpg),\n",
            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000120.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000120.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000121.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000121.jpg),\n",
            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
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            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000426.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000426.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000427.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000427.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000428.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000428.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000429.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000429.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000430.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000430.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000431.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000431.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000432.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000432.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000434.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000434.jpg),\n",
            "         continuing with \"central\" filename version\n",
            "plant_image_database/Citrus ×aurantium/00000435.jpg:  mismatching \"local\" filename (plant_image_database/Citrus ├Чaurantium/00000435.jpg),\n",
            "         continuing with \"central\" filename version\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v_Dj5SOBoZOg"
      },
      "source": [
        "# Set Up Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q82kWwuf43V8"
      },
      "source": [
        "def random_seed(seed_value, use_cuda):\n",
        "  '''\n",
        "  Sets the random seed for numpy, pytorch, python.random and pytorch GPU vars.\n",
        "  '''\n",
        "  np.random.seed(seed_value) # Numpy vars\n",
        "  torch.manual_seed(seed_value) # PyTorch vars\n",
        "  random.seed(seed_value) # Python\n",
        "  if use_cuda: # GPU vars\n",
        "      torch.cuda.manual_seed(seed_value)\n",
        "      torch.cuda.manual_seed_all(seed_value) \n",
        "      torch.backends.cudnn.deterministic = True \n",
        "      torch.backends.cudnn.benchmark = False\n",
        "  print(f'Random state set:{seed_value}, cuda used: {use_cuda}')"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LhM663Bb4dGh"
      },
      "source": [
        "@call_parse\n",
        "def create_path_df(\n",
        "      path: Param(\"Path of images to use\", str), \n",
        "      pct_images: Param(\"Fraction of images to use\", float)=1,\n",
        "      shuffle: Param(\"Shuffle prior to splitting\", bool)=False,\n",
        "      ):\n",
        "  '''\n",
        "  Creates a dataframe with respective classes and path to images.\n",
        "  Can return a subset of those images based on the pct_images.\n",
        "  By default will return the earliest files in the path.\n",
        "\n",
        "  Returns:\n",
        "  ----------\n",
        "  DataFrame\n",
        "    Returns a dataframe with Class and Path columns. \n",
        "  '''\n",
        "  from sklearn.utils import shuffle as shuff\n",
        "  data = []\n",
        "  for folder in sorted(os.listdir(path)):\n",
        "      num_files = len(sorted(os.listdir(path+'/'+folder)))\n",
        "      cut_point = int(num_files*pct_images)\n",
        "      # Takes the first pct_images from each folder\n",
        "      if shuffle == True:\n",
        "        for file in shuff(os.listdir(path+'/'+folder))[:cut_point]:\n",
        "          data.append((folder, path+folder+'/'+file))\n",
        "      else:\n",
        "        for file in sorted(os.listdir(path+'/'+folder))[:cut_point]:\n",
        "          data.append((folder, path+folder+'/'+file))\n",
        "  df = pd.DataFrame(data, columns=['Class', 'Path'])\n",
        "  print(f'Creating path:label dataframe from {path}, using {pct_images*100:.2f}% of the images ({len(df)} images.) Shuffle = {shuffle}.')\n",
        "  return df"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W4Gs3_iZ5cxH"
      },
      "source": [
        "@call_parse\n",
        "def stratified_split(\n",
        "      dataframe: Param(\"DataFrame created from create_path_df, containing Class and Path columns.\"), \n",
        "      n_splits: Param(\"Number of folds in split\", int)=5,\n",
        "      shuffle: Param(\"Shuffle prior to splitting\", bool)=True,\n",
        "      fold: Param(\"Which fold to use\", int)=0,\n",
        "      random_state: Param(\"Fix randomness if shuffle==True\", int)=101,\n",
        "      ):\n",
        "  ''' \n",
        "  Prepares a stratified k-fold split from a dataframe with 'Class' and 'Path' by adding an 'is_valid' column.\n",
        "\n",
        "  Returns:\n",
        "  ----------\n",
        "  DataFrame\n",
        "    Returns the requested stratified k-fold split by adding an 'is_valid' column. \n",
        "  '''\n",
        "  from sklearn.model_selection import StratifiedKFold\n",
        "  # Create stratified split using sklearn\n",
        "  print(f'Preparing stratified {n_splits} fold split, random state is {random_state}. Returning fold {fold}.')\n",
        "  kf = StratifiedKFold(n_splits=n_splits, shuffle=shuffle, random_state = random_state)\n",
        "  X, y = dataframe['Path'], dataframe['Class']\n",
        "  splits = list(kf.split(X, y))\n",
        "  # Prepare the selected fold\n",
        "  fold = fold\n",
        "  train_df = dataframe.loc[splits[fold][0]]\n",
        "  val_df = dataframe.loc[splits[fold][1]]\n",
        "  train_df['is_valid'] = False\n",
        "  val_df['is_valid'] = True\n",
        "  df_cnn = train_df.append(val_df)\n",
        "  # Return the selected fold\n",
        "  return df_cnn"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VhvudRgz9m4n"
      },
      "source": [
        "@call_parse\n",
        "def get_dataloader(\n",
        "      img_path: Param(\"Path of images to use\", str),\n",
        "      split_path: Param(\"Path of images to use to pre-define split\", str)=None,\n",
        "      img_size:  Param(\"Size in pixels\", int)=224,\n",
        "      pct_images: Param(\"Fraction of images to use\", float)=1,\n",
        "      bs: Param(\"Batch size\", int)=64,\n",
        "      deterministic: Param(\"Deterministic learner\", bool)=True,\n",
        "      randomerasing: Param(\"Random Erasing\", str)=None,\n",
        "      n_splits: Param(\"Number of folds in split\", int)=5,\n",
        "      shuffle: Param(\"Shuffle prior to splitting\", bool)=True,\n",
        "      fold: Param(\"Which fold to use\", int)=0,\n",
        "      ):\n",
        "  \"\"\" \n",
        "  Takes in image path and creates a dataloader. \n",
        "  If a split_path is defined, uses the entirety of that path to generate a master split, onto which the img_path is joined to defined a pseudo split.  \n",
        "      \n",
        "  Returns:\n",
        "  ----------\n",
        "  Dataloader\n",
        "    dls = data.dataloaders(df_cnn_mini, bs=bs)\n",
        "  \"\"\"\n",
        "\n",
        "  # Get file path and labels from the dataframe we set up.\n",
        "  get_x = lambda x: x['Path']\n",
        "  get_y = lambda y: y['Class']\n",
        "  # Define splitting function that reads from a dataframe column.\n",
        "  def stratifiedsplitter(df):      \n",
        "      train = df.index[~df['is_valid']].tolist()\n",
        "      valid = df.index[df['is_valid']].tolist()\n",
        "      return train,valid   \n",
        "  # Prepare transforms\n",
        "    # Image is larger initially (512 px) for later augmentations.\n",
        "  item_tfms=[RandomResizedCrop(512, min_scale = 0.7)]\n",
        "    # Batch transformations include a random resized crop to img_size px. \n",
        "  batch_tfms=[*aug_transforms(size=img_size), Normalize.from_stats(*imagenet_stats)]\n",
        "  # Add in randomerasing if necessary.\n",
        "  if randomerasing != None:\n",
        "    batch_tfms=[*aug_transforms(size=img_size), Normalize.from_stats(*imagenet_stats), randomerasing]\n",
        "  # Prepare DataBlock\n",
        "  data = DataBlock(blocks=(ImageBlock, CategoryBlock),\n",
        "                    splitter=stratifiedsplitter,            # Stratified train/validation split\n",
        "                    get_x=get_x,                            # Get the image path from dataframe\n",
        "                    get_y=get_y,                            # Label from dataframe\n",
        "                    item_tfms=item_tfms,                    # Crop images to a square\n",
        "                    batch_tfms=batch_tfms)                  # Normalize the images based on the imagenet stats\n",
        "  if split_path != None:\n",
        "    print('Split Path defined, preparing pseudo stratified split.')\n",
        "    # Create split_df - a dataframe that defines a master split, based on split_path\n",
        "    df = create_path_df(split_path, pct_images=1)\n",
        "    split_df = stratified_split(df, n_splits = 5, shuffle = True, random_state = 101, fold = 0)\n",
        "    split_df['Path']= split_df['Path'].apply(lambda x:img_path+'/'.join(x.split('/')[-2:]))\n",
        "    # Inner join this reduced dataframe to the previous splits to maintain the same training images\n",
        "    df_mini = create_path_df(img_path, pct_images=pct_images)\n",
        "    df_cnn_mini= df_mini.merge(split_df, how = 'inner', on = ['Class','Path'])\n",
        "  else: \n",
        "    # Create a dataframe based on main image path with a (possibly) reduced number of images\n",
        "    df = create_path_df(img_path, pct_images=pct_images)\n",
        "    df_cnn_mini = stratified_split(df, n_splits = 5, shuffle = True, random_state = 101, fold = 0)\n",
        "  # Print some information for a sanity check\n",
        "  dls = data.dataloaders(df_cnn_mini, bs=bs)\n",
        "  print(f'We have {dls.c} classes, with a training set of size {len(dls.train_ds)}, and a validation set of size {len(dls.valid_ds)}')\n",
        "  if split_path != None:\n",
        "    print(f\"The main split has {split_df['is_valid'].describe()['freq']/len(split_df['is_valid'])*100:.2f}% training data with {len(split_df.groupby('Class').count())} classes, the reduced split has {df_cnn_mini['is_valid'].describe()['freq']/len(df_cnn_mini['is_valid'])*100:.2f} % training data, with {len(df_cnn_mini.groupby('Class').count())} classes.\")\n",
        "  return dls"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aHYF5S5uN_mO"
      },
      "source": [
        "@call_parse\n",
        "def create_simple_cnn_learner(\n",
        "        size:  Param(\"Size\", int)=224,\n",
        "        bs:    Param(\"Batch size\", int)=64,\n",
        "        arch:  Param(\"Architecture\", str)='resnet34',\n",
        "        fp16:  Param(\"Use mixed precision training\", int)=0,\n",
        "        deterministic: Param(\"Deterministic learner\", bool)=True,\n",
        "        pretrained: Param(\"Pretrained architecture\", bool)=True,\n",
        "        pct_images: Param(\"Percent of images to sue\", float)=1,\n",
        "        img_path: Param(\"Path of images to use\", str)='/content/plant_images_database_manualcleanedlosses10/',\n",
        "        split_path: Param(\"Path of images to use to pre-define split\", str)=None,\n",
        "        model_dir: Param(\"Path to save model\", str)='/content/drive/My Drive/Houseplant Classifier/Models/',\n",
        "        log_name: Param(\"Name of csv training log\", str)='history.csv',\n",
        "        ):\n",
        "    # Select optimization function\n",
        "    opt_func = partial(Adam, mom=0.9, sqr_mom=0.99, eps=1e-05, wd=0.01, decouple_wd=True)\n",
        "    # Set Random Seed\n",
        "    if deterministic == True:\n",
        "      random_seed(101,True)\n",
        "    # Get Dataloader\n",
        "    dls = get_dataloader(img_size = size, bs = bs, img_path=img_path, split_path=split_path, pct_images=pct_images)\n",
        "    # Setup activation function and pooling\n",
        "    model = globals()[arch] \n",
        "    # Setup callbacks\n",
        "    callbacks = [CSVLogger(fname=log_name, append=True), ShowGraphCallback()]\n",
        "    # Setup Learner\n",
        "    learn = cnn_learner(dls, \n",
        "                        model, \n",
        "                        pretrained=pretrained,\n",
        "                        opt_func=opt_func,\n",
        "                        metrics=[accuracy,top_k_accuracy], \n",
        "                        model_dir = model_dir,\n",
        "                        cbs=callbacks,\n",
        "                        )\n",
        "    if fp16: learn = learn.to_fp16()\n",
        "\n",
        "    return learn"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntwC_n6nL3dU"
      },
      "source": [
        "@call_parse\n",
        "def create_cnn_learner(\n",
        "        size:  Param(\"Size\", int)=224,\n",
        "        bs:    Param(\"Batch size\", int)=64,\n",
        "        opt:   Param(\"Optimizer (adam,rms,sgd,ranger)\", str)='adam',\n",
        "        arch:  Param(\"Architecture\", str)='resnet34',\n",
        "        custom_head:  Param(\"Use of custom head\", nn.Sequential)=None,\n",
        "        fp16:  Param(\"Use mixed precision training\", int)=0,\n",
        "        deterministic: Param(\"Deterministic learner\", bool)=True,\n",
        "        pretrained: Param(\"Pretrained architecture\", bool)=True,\n",
        "        pct_images: Param(\"Percent of images to sue\", float)=1,\n",
        "        img_path: Param(\"Path of images to use\", str)='/content/plant_images_database_manualcleanedlosses10/',\n",
        "        split_path: Param(\"Path of images to use to pre-define split\", str)=None,\n",
        "        model_dir: Param(\"Path to save model\", str)='/content/drive/My Drive/Houseplant Classifier/Models/',\n",
        "        log_name: Param(\"Name of csv training log\", str)='history.csv',\n",
        "        loss_func: Param(\"Learner loss function\", str)=None,\n",
        "        mixup: Param(\"Mixup\", str)=None,\n",
        "        cutmix: Param(\"Cutmix\", str)=None,\n",
        "        randomerasing: Param(\"Random Erasing\", str)=None,\n",
        "        ):\n",
        "    # Select optimization function\n",
        "    if   opt=='adam'  : opt_func = partial(Adam, mom=0.9, sqr_mom=0.99, eps=1e-05, wd=0.01, decouple_wd=True)\n",
        "    elif opt=='rms'   : opt_func = partial(RMSprop, sqr_mom=0.99, mom=0.0, wd=0.0, decouple_wd=True)\n",
        "    elif opt=='sgd'   : opt_func = partial(SGD, mom=0.0, wd=0.0, decouple_wd=True)\n",
        "    elif opt=='ranger': opt_func = partial(ranger, mom=0.95, wd=0.01, eps=1e-06, sqr_mom=0.99, beta=0.0, decouple_wd=True)\n",
        "    # Get DataLoader\n",
        "    if deterministic == True:\n",
        "      random_seed(101,True)\n",
        "    # Implement RandomErasing\n",
        "    if randomerasing != None:\n",
        "      dls = get_dataloader(img_size = size, bs = bs, img_path=img_path, split_path=split_path, pct_images=pct_images, randomerasing=randomerasing)\n",
        "    else: \n",
        "      dls = get_dataloader(img_size = size, bs = bs, img_path=img_path, split_path=split_path, pct_images=pct_images)\n",
        "    # Setup activation function and pooling\n",
        "    model = globals()[arch] \n",
        "    # Setup callbacks\n",
        "    callbacks = [CSVLogger(fname=log_name, append=True), ShowGraphCallback()]\n",
        "    if mixup != None:\n",
        "      mixup = mixup\n",
        "      callbacks = [CSVLogger(fname=log_name, append=True), ShowGraphCallback(), mixup]\n",
        "    elif cutmix != None:\n",
        "      cutmix = cutmix\n",
        "      callbacks = [CSVLogger(fname=log_name, append=True), ShowGraphCallback(), cutmix]\n",
        "    # Setup Learner\n",
        "    learn = cnn_learner(dls, \n",
        "                        model, \n",
        "                        pretrained=pretrained,\n",
        "                        config = {'custom_head':custom_head},\n",
        "                        opt_func=opt_func,\n",
        "                        metrics=[accuracy,top_k_accuracy], \n",
        "                        model_dir = model_dir,\n",
        "                        loss_func = loss_func,\n",
        "                        cbs=callbacks,\n",
        "                        )\n",
        "    if fp16: learn = learn.to_fp16()\n",
        "\n",
        "    return learn"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZrcbadZg46D_"
      },
      "source": [
        "def enumerate_params(learner):\n",
        "  '''\n",
        "  Enumerates the unfrozen and frozen parameters for a given learner when called.\n",
        "  '''\n",
        "  total_params = sum(p.numel() for p in learner.model.parameters())\n",
        "  print(f'{total_params:,} total parameters.')\n",
        "  total_trainable_params = sum(\n",
        "  p.numel() for p in learner.model.parameters() if p.requires_grad)\n",
        "  print(f'{total_trainable_params:,} training parameters.')"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MZmKErJlu6hb"
      },
      "source": [
        "# Enable Model Modification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "naHzjCE3u60r"
      },
      "source": [
        "def change_act(model, pre_act, post_act):\n",
        "  # We want to go down each 'level' of the model and search for each activation, changing them to the activation we want to use.\n",
        "    # We can access the named_children of the model and use that to index our way into the appropriate layers\n",
        "    # pre_act is a class\n",
        "    # EfficientNet - geffnet.activations_me.SwishMe\n",
        "    # ResNet - torch.nn.modules.activation.ReLU\n",
        "    # post_act examples - Mish()\n",
        "  count = 0\n",
        "  block_list = list(dict(model.named_children()).keys())\n",
        "  for block in block_list:\n",
        "    layer0_list = list(dict(model._modules[block].named_children()).keys())\n",
        "    model._modules[block]\n",
        "    if isinstance(model._modules[block], pre_act):\n",
        "      model._modules[block] = post_act\n",
        "      count += 1\n",
        "    for layer0 in layer0_list:\n",
        "      layer1_list = list(dict(model._modules[block]._modules[layer0].named_children()).keys())\n",
        "      model._modules[block]._modules[layer0]\n",
        "      if isinstance(model._modules[block]._modules[layer0], pre_act):\n",
        "        model._modules[block]._modules[layer0] = post_act\n",
        "        count += 1\n",
        "      for layer1 in layer1_list:\n",
        "        layer2_list = list(dict(model._modules[block]._modules[layer0]._modules[layer1].named_children()).keys())\n",
        "        model._modules[block]._modules[layer0]._modules[layer1]\n",
        "        if isinstance(model._modules[block]._modules[layer0]._modules[layer1], pre_act):\n",
        "          model._modules[block]._modules[layer0]._modules[layer1] = post_act\n",
        "          count += 1\n",
        "        for layer2 in layer2_list:\n",
        "          layer3_list = list(dict(model._modules[block]._modules[layer0]._modules[layer1]._modules[layer2].named_children()).keys())\n",
        "          model._modules[block]._modules[layer0]._modules[layer1]._modules[layer2]\n",
        "          if isinstance(model._modules[block]._modules[layer0]._modules[layer1]._modules[layer2], pre_act):\n",
        "            model._modules[block]._modules[layer0]._modules[layer1]._modules[layer2] = post_act\n",
        "            count += 1\n",
        "          for layer3 in layer3_list:\n",
        "            layer4_list = list(dict(model._modules[block]._modules[layer0]._modules[layer1]._modules[layer2]._modules[layer3].named_children()).keys())\n",
        "            model._modules[block]._modules[layer0]._modules[layer1]._modules[layer2]._modules[layer3]\n",
        "            if isinstance(model._modules[block]._modules[layer0]._modules[layer1]._modules[layer2]._modules[layer3], pre_act):\n",
        "              model._modules[block]._modules[layer0]._modules[layer1]._modules[layer2]._modules[layer3] = post_act\n",
        "              count += 1\n",
        "\n",
        "  print(f'Changing activation function from {pre_act} to {post_act}. {count} instances changed. ')\n",
        "  return nn.Sequential(*model.children())"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TUVgZTvFQ3Li"
      },
      "source": [
        "# Model Exploration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "362oZv8jQ4lM"
      },
      "source": [
        "def arch_summary(arch):\n",
        "    model = arch(False)\n",
        "    tot = 0\n",
        "    for i, l in enumerate(model.children()):\n",
        "        n_layers = len(flatten_model(l))\n",
        "        tot += n_layers\n",
        "        print(f'({i}) {l.__class__.__name__:<12}: {n_layers:<4}layers (total: {tot})')"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NlVP6r4WdY5K"
      },
      "source": [
        "# PyTorch and fast.ai Network Architectures"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zqzkE__6bc-t"
      },
      "source": [
        "## Quick ResNets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sp7mxzhKKVtI"
      },
      "source": [
        "### Quick ResNet18"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195,
          "referenced_widgets": [
            "cee7e6e01ddd46eaae237cf1ddedec94",
            "d07a82a10ed24688b88ef112e65aa4e9",
            "55aca69bbbdc4e28b4b50b02cd1cebef",
            "68e55d46cfdc4d3ba1470914ed3aac08",
            "ac4a6742bdc14dcba5b407915e2f1cdc",
            "769f2a8be9e948478a2e35f24a433f31",
            "4b6584906be048b8a7492c8577894d3c",
            "e8657ddcb4d041d7976dc9e5c948a3f7"
          ]
        },
        "id": "qQRjG0ZQKVtI",
        "outputId": "96e692f7-2a17-4c33-811a-edf52f663288"
      },
      "source": [
        "learn = create_cnn_learner(pct_images=0.25, \n",
        "                           deterministic=True, \n",
        "                           size=224, \n",
        "                           arch='resnet18',\n",
        "                           img_path ='/content/plant_images_database_manualcleanedlosses10/', \n",
        "                           split_path = '/content/plant_image_database/')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random state set:101, cuda used: True\n",
            "Split Path defined, preparing pseudo stratified split.\n",
            "Creating path:label dataframe from /content/plant_image_database/, using 100.00% of the images (77700 images.) Shuffle = False.\n",
            "Preparing stratified 5 fold split, random state is 101. Returning fold 0.\n",
            "Creating path:label dataframe from /content/plant_images_database_manualcleanedlosses10/, using 25.00% of the images (16940 images.) Shuffle = False.\n",
            "We have 517 classes, with a training set of size 13555, and a validation set of size 3385\n",
            "The main split has 80.00% training data with 518 classes, the reduced split has 80.02 % training data, with 517 classes.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet18-5c106cde.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-5c106cde.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cee7e6e01ddd46eaae237cf1ddedec94",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=46827520.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "3mGHcsuDKVtJ",
        "outputId": "ef6f56b7-4420-4bec-f36d-b351c20ec602"
      },
      "source": [
        "print(f'Freezing model body')\n",
        "learn.freeze()\n",
        "enumerate_params(learn)\n",
        "learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Freezing model body\n",
            "11,968,576 total parameters.\n",
            "801,664 training parameters.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=0.005754399299621582, lr_steep=0.013182567432522774)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "asGmH4bUKVtJ",
        "outputId": "35965e91-2ada-4396-c3da-4d4811ddb352"
      },
      "source": [
        "learn.fit_one_cycle(10, 1e-02)\n",
        "learn.save('stage-1-quickresnet18')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_k_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>5.376186</td>\n",
              "      <td>3.871335</td>\n",
              "      <td>0.218612</td>\n",
              "      <td>0.444904</td>\n",
              "      <td>01:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>4.203279</td>\n",
              "      <td>3.662590</td>\n",
              "      <td>0.240473</td>\n",
              "      <td>0.496307</td>\n",
              "      <td>01:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>3.374423</td>\n",
              "      <td>3.024303</td>\n",
              "      <td>0.332053</td>\n",
              "      <td>0.595864</td>\n",
              "      <td>01:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>3.031234</td>\n",
              "      <td>2.798667</td>\n",
              "      <td>0.354801</td>\n",
              "      <td>0.646677</td>\n",
              "      <td>01:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.666882</td>\n",
              "      <td>2.504205</td>\n",
              "      <td>0.411817</td>\n",
              "      <td>0.699852</td>\n",
              "      <td>01:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.319155</td>\n",
              "      <td>2.315259</td>\n",
              "      <td>0.462629</td>\n",
              "      <td>0.727031</td>\n",
              "      <td>01:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.973224</td>\n",
              "      <td>2.094513</td>\n",
              "      <td>0.501920</td>\n",
              "      <td>0.765731</td>\n",
              "      <td>01:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.608434</td>\n",
              "      <td>1.935286</td>\n",
              "      <td>0.530281</td>\n",
              "      <td>0.797932</td>\n",
              "      <td>01:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.398510</td>\n",
              "      <td>1.883317</td>\n",
              "      <td>0.553914</td>\n",
              "      <td>0.802954</td>\n",
              "      <td>01:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.202481</td>\n",
              "      <td>1.872230</td>\n",
              "      <td>0.555096</td>\n",
              "      <td>0.805908</td>\n",
              "      <td>01:03</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/content/drive/My Drive/Houseplant Classifier/Models/stage-1-quickresnet18.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "VGASYRIpKVtJ",
        "outputId": "790444f9-4cf5-4819-906e-fee005b028c3"
      },
      "source": [
        "print(f'Unfreezing model body')\n",
        "learn.unfreeze()\n",
        "enumerate_params(learn)\n",
        "learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unfreezing model body\n",
            "11,968,576 total parameters.\n",
            "11,968,576 training parameters.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=1.9054606673307718e-05, lr_steep=7.585775847473997e-07)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "BvmTMYCTKVtJ",
        "outputId": "47844eeb-1bd3-456c-eebb-fbda9dfbd305"
      },
      "source": [
        "learn.fit_one_cycle(10, lr_max=slice(1e-5, 1e-4))\n",
        "learn.save('stage-1-quickresnet18')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_k_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.253325</td>\n",
              "      <td>1.872273</td>\n",
              "      <td>0.550074</td>\n",
              "      <td>0.804727</td>\n",
              "      <td>01:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.225031</td>\n",
              "      <td>1.856861</td>\n",
              "      <td>0.557755</td>\n",
              "      <td>0.807090</td>\n",
              "      <td>01:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.224061</td>\n",
              "      <td>1.837848</td>\n",
              "      <td>0.558936</td>\n",
              "      <td>0.809749</td>\n",
              "      <td>01:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.142093</td>\n",
              "      <td>1.822689</td>\n",
              "      <td>0.561595</td>\n",
              "      <td>0.814771</td>\n",
              "      <td>01:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.083736</td>\n",
              "      <td>1.804391</td>\n",
              "      <td>0.566617</td>\n",
              "      <td>0.813589</td>\n",
              "      <td>01:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.039487</td>\n",
              "      <td>1.778013</td>\n",
              "      <td>0.571640</td>\n",
              "      <td>0.817430</td>\n",
              "      <td>01:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.952541</td>\n",
              "      <td>1.773764</td>\n",
              "      <td>0.569572</td>\n",
              "      <td>0.818316</td>\n",
              "      <td>01:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.944047</td>\n",
              "      <td>1.761585</td>\n",
              "      <td>0.576366</td>\n",
              "      <td>0.820679</td>\n",
              "      <td>01:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.908789</td>\n",
              "      <td>1.754593</td>\n",
              "      <td>0.572526</td>\n",
              "      <td>0.821566</td>\n",
              "      <td>01:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.904825</td>\n",
              "      <td>1.756453</td>\n",
              "      <td>0.575480</td>\n",
              "      <td>0.823043</td>\n",
              "      <td>01:04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/content/drive/My Drive/Houseplant Classifier/Models/stage-1-quickresnet18.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yria6Xw9YSsV"
      },
      "source": [
        "### Quick ResNet34"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B98QGFQxYZQT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195,
          "referenced_widgets": [
            "f905c1296d5843c2a727cfc0db1cd6ae",
            "ba8b18a2a1bf4c3e9ba39469fa47f647",
            "1018ef7278f84a47b67485e25fe81901",
            "a66cd6e2e4784709b330b64da5d95be7",
            "caf9d5fd2a644916a8def17ed58499ad",
            "768093a9a5534a18b4e056397a80ad2f",
            "ab40aaf7acbd44e4a1b0a7ff4bb33485",
            "4030ea99c5904f3aae0b0a9fb882bb0a"
          ]
        },
        "outputId": "24d1e692-7b28-426b-885d-4afeeadc86bf"
      },
      "source": [
        "learn = create_cnn_learner(pct_images=0.25, \n",
        "                           deterministic=True, \n",
        "                           size=224, \n",
        "                           img_path ='/content/plant_images_database_manualcleanedlosses10/', \n",
        "                           split_path = '/content/plant_image_database/',\n",
        "                           log_name = 'quickbaseline.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random state set:101, cuda used: True\n",
            "Split Path defined, preparing pseudo stratified split.\n",
            "Creating path:label dataframe from /content/plant_image_database/, using 100.00% of the images (77700 images.) Shuffle = False.\n",
            "Preparing stratified 5 fold split, random state is 101. Returning fold 0.\n",
            "Creating path:label dataframe from /content/plant_images_database_manualcleanedlosses10/, using 25.00% of the images (16940 images.) Shuffle = False.\n",
            "We have 517 classes, with a training set of size 13555, and a validation set of size 3385\n",
            "The main split has 80.00% training data with 518 classes, the reduced split has 80.02 % training data, with 517 classes.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet34-333f7ec4.pth\" to /root/.cache/torch/hub/checkpoints/resnet34-333f7ec4.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f905c1296d5843c2a727cfc0db1cd6ae",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=87306240.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNwYjhQFYbrN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "outputId": "1e809433-6e3f-4911-9e50-abdb0c7a531e"
      },
      "source": [
        "print(f'Freezing model body')\n",
        "learn.freeze()\n",
        "enumerate_params(learn)\n",
        "learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Freezing model body\n",
            "22,076,736 total parameters.\n",
            "809,088 training parameters.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=0.004786301031708717, lr_steep=0.009120108559727669)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x7Y44fx609N0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "outputId": "5ffa4cff-dfee-41d1-fc0c-825bf77451fb"
      },
      "source": [
        "learn.fit_one_cycle(10, 1e-02)\n",
        "learn.save('stage-1-quickbaseline')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_k_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>5.380674</td>\n",
              "      <td>3.862144</td>\n",
              "      <td>0.215953</td>\n",
              "      <td>0.441654</td>\n",
              "      <td>01:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>4.164528</td>\n",
              "      <td>3.812981</td>\n",
              "      <td>0.214771</td>\n",
              "      <td>0.472378</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>3.354764</td>\n",
              "      <td>3.130136</td>\n",
              "      <td>0.312260</td>\n",
              "      <td>0.587888</td>\n",
              "      <td>01:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.974951</td>\n",
              "      <td>2.825325</td>\n",
              "      <td>0.353028</td>\n",
              "      <td>0.641064</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.588697</td>\n",
              "      <td>2.559198</td>\n",
              "      <td>0.407976</td>\n",
              "      <td>0.688626</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.224540</td>\n",
              "      <td>2.248531</td>\n",
              "      <td>0.468242</td>\n",
              "      <td>0.744756</td>\n",
              "      <td>01:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.860929</td>\n",
              "      <td>1.997348</td>\n",
              "      <td>0.512260</td>\n",
              "      <td>0.789069</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.524130</td>\n",
              "      <td>1.861627</td>\n",
              "      <td>0.552142</td>\n",
              "      <td>0.813589</td>\n",
              "      <td>01:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.315566</td>\n",
              "      <td>1.790887</td>\n",
              "      <td>0.565140</td>\n",
              "      <td>0.818316</td>\n",
              "      <td>01:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.108867</td>\n",
              "      <td>1.784061</td>\n",
              "      <td>0.572230</td>\n",
              "      <td>0.823929</td>\n",
              "      <td>01:06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/content/drive/My Drive/Houseplant Classifier/Models/stage-1-quickbaseline.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RAzF_41TZUlM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "9aa61cd1-4509-47e8-83e6-aae9e2a7fbec"
      },
      "source": [
        "print(f'Unfreezing model body')\n",
        "learn.unfreeze()\n",
        "enumerate_params(learn)\n",
        "learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unfreezing model body\n",
            "22,076,736 total parameters.\n",
            "22,076,736 training parameters.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=4.365158383734525e-06, lr_steep=1.3182567499825382e-06)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sckIX6D4CEZGh6CcvvEZrJMqNF04Pu5QhQUEgIhnlyPF27nnpdd599gROHz/gAyPTgoJARDLKnc+/RnNHlC9eNivsUoYMBYGIZIy6pjZ+9sfXuWb+RGaNU2/gBAWBiGSMHz+3m7ZIlC+oN/AmCgIRyQiHGlu59+W9XLdgEjPKisMuZ0hREIhIRrjj2dfoiDq3qDfwFgoCEUl7NQ2t3LdyL+9bOInTxmTWxPR9oSAQkbT3/T/sJBpzbrlUvYHuKAhEJK3trm3igVWVfHTJVKaOHhZ2OUOSgkBE0tp3nniFwtxsHSnUCwWBiKStVXuO8PttNXzm7dMZU5wfdjlDloJARNKSu/OtFdsZP7xA1xQ6CQWBiKSlxzdXs6HyGF9612wK87LDLmdIUxCISNppi0T51ydfZc74Et5/juYbOBkFgYiknV+8vI99R5r52tIzyM7K7NnH+kJBICJppb6lg+//YScXzhzDxbPGhF1OSlAQiEha+eEzu6hv6eBrS+dk/FzEfaUgEJG0UXmkmZ++9DrvP2cycyeOCLuclKEgEJG08W+/e5WsLPjyu2aHXUpKURCISFrYUHmM5RsPcNNF05kwojDsclKKgkBEUp67863HtzOmOI+/fPuMsMtJOQoCEUl5v9taw6rXj/B/Lp9NcX5O2OWkHAWBiKS09kiMbz+xnZlji7m+YkrY5aQkBYGIpLS7XtzD64eb+dpVc8jJ1ibtVOhfTURS1u7aJr739A6umDuOS+eMDbuclKUgEJGUFIs5//fXmyjIyeIfrz1LJ48NgIJARFLSvS/vZfXrR/nbq89k7PCCsMtJaQoCEUk5lUea+c6Tr3Dx7DI+cK6uLjpQCgIRSSnuzteXbcaAb71XQ0LJoCAQkZTyq7X7eWFnHX991Rwmj9Rk9MmgIBCRlFHX1MY/P76dReUj+fiSaWGXkzYCCwIzu9vMDpnZlh5ev8TM6s1sQ+L2d0HVIiLp4Z8f305ze4R/ed/ZZGnCmaQJ8lzse4AfAD/vZZkX3P3qAGsQkTTx4s46lq2v4pZLZzJzbEnY5aSVwHoE7v48cCSo9xeRzNHaEeVvHt1M+ehhfO4dM8MuJ+2EvY/gbWa20cyeMLO5IdciIkPUD5/ZxeuHm/mn686mIDc77HLSTpiX6VsHTHP3JjNbCjwKzOpuQTO7GbgZYOrUqYNXoYiEbtehRn703G7eu3ASF2oO4kCE1iNw9wZ3b0rcXwHkmlm3n7K73+nuFe5eUVZWNqh1ikh4YjHn68u2MCwvh2+8+4ywy0lboQWBmY23xJkgZrY4UcvhsOoRkaHn7pf2sGrPEb521RzGFOeHXU7aCmxoyMweAC4BxpjZfuA2IBfA3e8APgB81swiQAvwYXf3oOoRkdSypaqe7zz5CpefOY7rF2megSAFFgTu/pGTvP4D4oeXioi8yfG2CLc8sJ7RRfn86/vn6TISAdOcbiIy5Ny2fCuvHz7OAzedx8iivLDLSXthHz4qIvImj22o4uG1+7nlHTM5b/rosMvJCAoCERky9h4+zjeWbeHcaSP5wmXdHk0uAVAQiMiQsOtQEx/9yUqyDP7fhxdo/uFBpH0EIhK69fuO8ul7VpOdZdx/03m6vPQgUxCISKieffUQn/3FOspK8rn3xsVMG10UdkkZR0EgIqF5ZN1+vvrwJk4fX8I9n1pMWYlOGguDgkBEBl1bJMq3Ht/Oz/60l/NnjObHf3EuJQW5YZeVsRQEIjKo9h4+zufuX8eWqgb+14Wn8dUr55CXox3DYVIQiMigWbG5mr9+eBNZWcZPPlHB5WeOC7skQUEgIoNg7d6j/Nczu/jDK4dYMKWUH3x0oY4MGkIUBCISCHfn5deO8INndvLSrsOMHJbLV644nZsumq6hoCFGQSAiSdXSHuU3mw5w38p9bKw8xpjifL6x9Aw+umQqRfna5AxF+lREJCm2VzfwwKp9LFtfRWNrhBllRfzDtXP5UMUUTS85xCkIAhCLOWbo0rmS1tydLVUNPLm1mie3HGR37XHycrJYetZ4PrpkGovKR+pvIEUoCJKktSPK8ztqeWLLQZ7eVkNF+Uh+9PFzk/pNKBKNkWVGVpb+uCQ8ja0d/PSl13lwdSVVx1rIzjLOmz6KG84v5z3zJuqy0SlIQTBAB+tb+fYT23lqWw3H26OMKMzl/Jmj+f22Gj59z2r++4YKhuX175/Z3altbGPnoSa2VzfwysFGtlc3sLOmiawsmDm2mNljS5g1roTZ44opH1PElJHDtANOAtXSHuXnf3qdO57bzdHmDi6eXcat75zFO88Yp41/ilMQDMCuQ4184q5VHGvp4Jr5E1l69gTeNmM0udlZLFu/ny8/tJFP3r2auz+1iOIedpIdbmpje3V8Q/9qTSO7a5vYdaiJxtbIG8uUleQzZ3wJn7ygnGjM2VHTyEu763hkfdUby2QZTBpZyLRRRRTmZeMeDxQHhhfkUFE+iiWnjWLm2GJ11+WkYjGn7ngb1cdaqa5vYdehJu75417qmtp4++wyvnT5bOZPKQ27TEkSS7VpgisqKnzNmjVhl8HavUf49D1ryM3O4p5PLeKsSSPessxvNh7g1gc3MH/yCO759GIM2FhZz7p9R1m/7yhbDzRwqLHtjeXHFOczc2wRs8aWMHNsMTPHFnP6+JIeJ+2ub+5gV20jr9c1s/fwcfYcbmbf4eO0ReJDSPH9FHCooe2N9YwqyqNi2kimlxUzqbSASSMLmVhaSHF+DtGYE4k5kajTEY3RFonR1hGlNRKlrSNGQW42wwtzGFGYy/DCXEoL89QLSTHuTkNLhJaOKB3RGO3RGO2RGFVHW3i1ppFXD8Zve+qO0x6Nvel3l5w2ir+64nQWlY8KqXoZCDNb6+4V3b6mIOi/p7fV8PkH1jF+eAE///QSpo7u+cSYJ7dUc8sD6ynKz6G+pYMT/9yzxhZz9qQRnDFheOJWwugeNvgD5e7sPdzMqj1HWLnnCOv2HWX/0WY6ogP77M1gwvACpo4extRR8duEEYWMHZ7PuOEFjCspoCg/m46o0xGL0RGJ0RF12iMx2qNR2iLxjdCY4nwmlRZq30eSNbR2sOtQEzsONvJKYgP/ak0jR4639/g7k0oLmTM+/kVk0shCJowoZMKIAiaWFjJKwz8pTUGQRI9tqOJLD23krInDueuTi3r8tt7Zs68e4sHVlcweV8I500ayYEopIwrDvcBWLObUNrVRdayFqqMttLRHyck2crKzyMkycrKM/NxsCnKyyM/NJj8ni9aOKPUtHdS3dNDQ0kFtUzuVR5rZl7jVdurd9FdRXjazxpVw+rgSpowqxB06Yk40Fg+PprYIja0RGls7aGyNUJCbxbzJpcyfXMqCKaWMG57PgfpW1u09ytq9R1lfeYy8bOPsSaXMnzKCeZNLKR89LG2Hxdyd9ZXHeHLLQbYdaGDnoUZqGv78eRTmZjN7fAlzxsU38kX5OeRmG3k5WeRmZzFueD6zx5Xowm9pTEGQJM3tES78zjOUjx7GvTcu0ckxXbS0RznU2EpNQxs1Da3UNLTS3B4lNzvrjY1OTlYWeTlZ5OfEf+ZlZ3GwofWNIYmu31izE6FUnJ9DSUEOJQW5lBTEe1evHmwkEov//y3Ky+Z4exSIb/TmTR5BJOZsqaqnLRIf4ijOz2FiaQETRhS+8fP08SWJICkI5N+kuT3CrkNNHKxvTRzxBVlmZGcZ2YmfOdlGlhn5OdkU5+dQlJ9NUX4O+TlZuEN7NEZHNEYk6sTc4+9jBgZVR1v4zaYD/GbjAfYfbSEvO4s5E+Ib+xNDjLPHFTNl5DD1uDJcb0GgLVk/3L9yH0eOt/OTT1QoBLpRmJfNtNFFA5pYxN1pi8TeCIDevsG3dkTZeqCBTfuPsbu2iZllxZw7bRRzJpSQm5jmMBKNsaOmiU37j7G9uoHq+laq61vZeqCeuqY/B86EEQXMn1zKrHHFFORmU5ibHf+Zl8XIYXmMKc6nrCSfUUV5NLdF4+PpNY28erCByiMt5GZnUZh3ogeVxcH6VnbUNFF5tJlT/a5lRp9+NzvLuHDmGG5952zeNXccw/WtXvpJPYI+au2IctG/PsOsscXcf9N5g75+Sb7WjijbqhvYsO8YGyqPsXH/MfYebu7Xe5QU5FA+uohIzGntiNLaEaWlI8rYkvz44b1jSzh9fDGTSofhODGHaCz+zT4ac2KJHfRRd9o6ojS1RTneFqGpLfLGcF1udrzndKLn4B5/H0+s/7I5YwPbvyTpQz2CJHhwdSW1jW3c/uGFYZciSVKQm805U0dyztSRbzx3okcS36jHaG6PcLS5ndrGduqa2qhraqMgN5vTx5cwZ3wJ44cXpO1+B8kcCoI+aI/EuOO53SwqH8l503XoXDozMwoSw0IimUIHgXfS2hFld23TW57/9br9VNe38vlLZ+nbn4ikHQVBgrvzxV+u57L/eI4vPbSBuqb4oXeRaIwfPruL+ZNHcPGsMSFXKSKSfBoaSli2vorfba3hwplj+M3GAzy9rYavXjmH/JwsKo+0cNvVc9UbEJG0pCAAqutbuG35VhaVj+Rnn17Mnrom/vbRrfzNo1vIMjhjwnAuO2Ns2GWKiAQi44eG3J2vPryJSNT59w/OJzvLmDm2hPtvWsJ/Xr+AmWOL+dpVc9QbEJG0lfE9gvtW7uOFnXX843VnvelEKDPjuoWTuG7hpBCrExEJXmA9AjO728wOmdmWHl43M7vdzHaZ2SYzOyeoWnqy9/BxvrViOxfNGsPHl0wd7NWLiAwJQQ4N3QNc2cvrVwGzErebgR8FWMtbRGPOX/1qI9lZxnfeP09DPyKSsQILAnd/HjjSyyLXAj/3uJeBUjObEFQ9Xf3wmV2sfv0of3/NXCaWFg7WakVEhpwwdxZPAio7Pd6feO4tzOxmM1tjZmtqa2sHvOK1e4/yn/+zk2vmT+S92gcgIhkuJY4acvc73b3C3SvKysoG9F6NrR3c+uB6Jowo4J/ee5aGhEQk44V51FAVMKXT48mJ5wJ122NbqTrawkN/+TZdrldEhHB7BMuBTySOHjoPqHf36iBX+NiGKh5ZX8UXLptFheZdFREBAuwRmNkDwCXAGDPbD9wG5AK4+x3ACmApsAtoBj4VVC0AlUea+ZtlW6iYNpLPv2NmkKsSEUkpgQWBu3/kJK878Lmg1t/VjppGhuVn873rF5CTnRK7RkREBkXGnFl82RnjeG7mGF1nXkSki4z6aqwQEBF5q4wKAhEReSsFgYhIhlMQiIhkOAWBiEiGUxCIiGQ4BYGISIZTEIiIZDiLn+CbOsysFtibeDgCqO/0cufH3d0fA9QNsISu6zyV5bp7rbe2dH3c0/2Btk9tG3jbuj6XKW3r/DiZbeupjv4s09NrA/1/mWptK3X37i/f7O4pewPu7Olxd/eBNcle56ks191rvbWlL21LRvvUtoG3rR/tSau2dX6czLb1tX39bVtv9ff1s0vltnW9pfrQ0G96edzT/WSv81SW6+613trS9bHa1n+D1bauz2VK2zo/Tmbb+vp+/W1bd88P1f+XQbTtTVJuaGggzGyNu1eEXUdQ0rl9altqUttSQ6r3CPrrzrALCFg6t09tS01qWwrIqB6BiIi8Vab1CEREpAsFgYhIhlMQiIhkOAVBgpldZGZ3mNl/m9kfw64nmcwsy8z+2cy+b2Y3hF1PMpnZJWb2QuKzuyTsepLNzIrMbI2ZXR12LclmZmckPreHzeyzYdeTTGZ2nZn9xMweNLN3hV3PyaRFEJjZ3WZ2yMy2dHn+SjN71cx2mdn/7e093P0Fd/8M8FvgZ0HW2x/JaBtwLTAZ6AD2B1VrfyWpbQ40AQWkX9sA/hp4KJgqT12S/ua2J/7mPgRcEGS9/ZGktj3q7jcBnwGuD7LeZEiLo4bM7GLiG4Ofu/tZieeygR3A5cQ3EKuBjwDZwL90eYtPu/uhxO89BNzo7o2DVH6vktG2xO2ou//YzB529w8MVv29SVLb6tw9ZmbjgO+6+8cGq/7eJKlt84HRxEOuzt1/OzjVn1yy/ubM7Brgs8C97n7/YNXfmyRvT/4DuM/d1w1S+ackLSavd/fnzay8y9OLgV3u/hqAmf0SuNbd/wXotpttZlOB+qESApCctpnZftkdNTgAAAQwSURBVKA98TAaXLX9k6zPLeEokB9EnaciSZ/bJUARcCbQYmYr3D0WZN19lazPzt2XA8vN7HFgSARBkj47A74NPDHUQwDSJAh6MAmo7PR4P7DkJL9zI/DTwCpKnv627RHg+2Z2EfB8kIUlQb/aZmbvA64ASoEfBFvagPWrbe7+DQAz+ySJnk+g1Q1cfz+7S4D3EQ/wFYFWNnD9/Zu7BXgnMMLMZrr7HUEWN1DpHAT95u63hV1DENy9mXjIpR13f4R40KUtd78n7BqC4O7PAs+GXEYg3P124Paw6+irtNhZ3IMqYEqnx5MTz6UDtS01pXPbIL3bl85tS+sgWA3MMrPTzCwP+DCwPOSakkVtS03p3DZI7/alc9vSIwjM7AHgT8DpZrbfzG509wjweeB3wHbgIXffGmadp0JtU9uGonRuXzq3rSdpcfioiIicurToEYiIyKlTEIiIZDgFgYhIhlMQiIhkOAWBiEiGUxCIiGQ4BYGkBTNrGuT1JWXOCovPp1BvZhvM7BUz+/c+/M51ZnZmMtYvAgoCkW6ZWa/X4XL385O4uhfcfQGwELjazE52bf7riF+RVCQpFASStsxshpk9aWZrLT6L2ZzE8+8xs5Vmtt7Mnk7MZYCZfdPM7jWzl4B7E4/vNrNnzew1M/tCp/duSvy8JPH6w4lv9PclLkGMmS1NPLfWzG43s17nE3D3FmAD8StdYmY3mdlqM9toZr82s2Fmdj5wDfBviV7EjJ7aKdJXCgJJZ3cCt7j7ucBfAT9MPP8icJ67LwR+CXy10++cCbzT3T+SeDyH+GWuFwO3mVluN+tZCNya+N3pwAVmVgD8GLgqsf6ykxVrZiOBWfz5UuGPuPsid59P/LIGN7r7H4lf4+Yr7r7A3Xf30k6RPtFlqCUtmVkxcD7wq8QXdPjzxDWTgQfNbAKQB+zp9KvLE9/MT3jc3duANjM7BIzjrVNirnL3/Yn1bgDKic9w9Zq7n3jvB4Cbeyj3IjPbSDwE/tPdDyaeP8vM/on4XAvFxK9z0592ivSJgkDSVRZwLDH23tX3iU9ruTwxOco3O712vMuybZ3uR+n+b6Yvy/TmBXe/2sxOA142s4fcfQNwD3Cdu29MTE5zSTe/21s7RfpEQ0OSlty9AdhjZh+E+NSBZjY/8fII/nwt+RsCKuFVYHqnKQ9POoF5ovfwbeIT1gOUANWJ4ajOczE3Jl47WTtF+kRBIOliWOKSwSduXyK+8bwxMeyyFbg2sew3iQ+lrAXqgigmMbz0v4EnE+tpBOr78Kt3ABcnAuRvgZXAS8ArnZb5JfCVxM7uGfTcTpE+0WWoRQJiZsXu3pQ4iui/gJ3u/r2w6xLpSj0CkeDclNh5vJX4cNSPQ65HpFvqEYiIZDj1CEREMpyCQEQkwykIREQynIJARCTDKQhERDKcgkBEJMP9f9WBtLRp8am2AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kstFqRqvZXTp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "outputId": "e412abb5-5960-4bb6-c1f6-912b5d47845f"
      },
      "source": [
        "learn.fit_one_cycle(10, lr_max=slice(1e-5, 1e-4))\n",
        "learn.save('stage-2-quickbaseline')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_k_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.169512</td>\n",
              "      <td>1.779867</td>\n",
              "      <td>0.571640</td>\n",
              "      <td>0.819793</td>\n",
              "      <td>01:09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.170363</td>\n",
              "      <td>1.783885</td>\n",
              "      <td>0.569867</td>\n",
              "      <td>0.821270</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.154361</td>\n",
              "      <td>1.760865</td>\n",
              "      <td>0.573412</td>\n",
              "      <td>0.825406</td>\n",
              "      <td>01:09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.079567</td>\n",
              "      <td>1.747816</td>\n",
              "      <td>0.580207</td>\n",
              "      <td>0.825702</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.999833</td>\n",
              "      <td>1.719440</td>\n",
              "      <td>0.581388</td>\n",
              "      <td>0.827474</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.910574</td>\n",
              "      <td>1.690382</td>\n",
              "      <td>0.584343</td>\n",
              "      <td>0.831019</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.852735</td>\n",
              "      <td>1.679179</td>\n",
              "      <td>0.594682</td>\n",
              "      <td>0.835451</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.820240</td>\n",
              "      <td>1.662708</td>\n",
              "      <td>0.598818</td>\n",
              "      <td>0.839586</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.768614</td>\n",
              "      <td>1.655415</td>\n",
              "      <td>0.597341</td>\n",
              "      <td>0.839586</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.762076</td>\n",
              "      <td>1.656761</td>\n",
              "      <td>0.597046</td>\n",
              "      <td>0.840177</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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N/I8uP4uM9I48dfMIAPr/bB5/X7yF4rJj1/bv3F/MGT+dy72v1G7Rv5+1l/Spc+j74Fx+MiuTnfuLSZ86h3+t2NXo+xeRts3C1UqL697Pdf/Wn/j33RcyzJdcQ+1oRSVVVZAQG0361Dk15Zt+PQFPdBT/XrWb79fpvqjbdeGcY9riLfzu3S8a3M/EId0Y0SeVM7q050tndakp35JfxLjfLw64zt9vHcnlg7uRd7iUMU8srPfB9sw3RtC/axIl5ZWc1TWJMx/yXjx02aCu/PKawXRPSQC83TUDfv5uve3fmNGbV/0SdNavxpMQG82aXQVc89RHDb6XP954Dl8d3qwrT0WkFZnZyoYaziE5KXo8osyAlulyqais4sVPdvDoO+sByHzkspplg3sk44n2toqvHd6TbfuO8Of3N9UsL6+sqlkOMGPZzoDJ/M83DeM+X8t37ud7arpBtj9+BZvzinjwjUyWbz9YU3/84G789ebhzFqZTW5hKZcN6gpAl6R4Nj42gdlrcmoNhXDn//us3j6/df5pPHzVYKKirKYs3hPNxscm0P9nta8WfbVOa/vtzBwSY6O5Z4Z3H2ekteOeL5/J/a/W7ja6/9U1jOzTkT6dwneyWESOT9ha6AOHDHMlV/yaGbefxwVndg7ptl9euoOH3qw/UkHn9rEs/OFYkuLrnwSdk5nL3TM+Y9yALvRMTeD6kb2Z9OynFB31dmH8ZPwAhvXuwIxlO7nj4tM5u2cKALsLSrjw8f82Gs+4AV146pYRTV7Jsj7nEL07JjDkkf8EXL7m4ctISQh8Are4rIIoM1bvKuCfn2yv+YD55MEvc/5va8c34exu/O3mEURHGVVVjj8t2Mjw01KZ/H/LAbh+ZC+evD7oERxEpBU11kIPW0IfNHS4K574GC/+zygu6Z9Wb3luYUlNt0JjDpeWsyX/CGektSPBE83Riiomv7CcZdvq39z6yYNfbnCbB46UMeLR9wIue+d7Y2oSeCDOOVbtKuBrfn3c4O1WuWxQV8ysgTUbduRoBW+vyWH/kTIWZO3lF1cOYnif1KDWdc5RXumI9fXPP7N4C4/P2wDARf0689Jt5wVcL//wUcY+uZAjZZXcN64f93+lf7PjFpGW1SYT+uChw92RiY/x0MSBfGP0acTGRLEgay93vLSSS/qnsXhjPn+44Ry+NqJ2f65zjuyDJTXXj9/63FI+2LSv3vbTOyXy9vfGMH/dXn74rzV8ZVBXnv1m4+dr56/bwx0vraxV9n+Tz63VL96YL/YcpnfHBLbkHWFQj2Sio5qfyFvKf9btYcpLK/ngx19q9Nr7/53/BX9buBmgRb49iciJaZMJ/exzhruiCY8B3hN9vTsm8tyH2+rVW/vLy2kfF1PTt/3CR9t45O31zPzOaJxz3PyPpQG3//p3L2Dkad4WbXllFVFmQSXYqirHv1fv5oqh3YmLaf415pHgjc+yeeA1b9/6o9eeza2jTwtzRCJSrU0m9CHDRrjD4x+tme/cPpZ9RWX16n15QBf+uyEPgP+5sC/Pf1Q/6f/77gtJjo8hJcGDJyaK5AB95NI8Ix59jwNHvL+PlT+7lE7t40K6/WXbDvDfDXn8ZPxZx9UlJXKqaiyhh+0BF3X/hesm8wHdkgBqkjkQMJlX1z09rT2d2scpmYfI298bUzM98rEFtZZ9tHkfb67KrlV24EgZX5/2MfPX7Wl0uwePlLFhzyFu+PsnPLN4C7+dt4Hl2w8EvOlLRJonbC30YcNHuoLLf1WrLDE2ml6pCTx18whS28Uy7/Ncfv7Wunrrbnh0PPGeaBas38vm/CLuvOSM1gr7lOKco++DcwH49gXpfOfi0+nZIaHmmv7LBnVl4Rd5TBrVh5yCUhZk7QVg2i0jmDCke8Btfvl/F7F135EG9/nIVYP49oV9Q/xORCJHm+xyGTFypDvwldoJfc69Yxjco/bVJFVVDjPYf6SMFz/ezl1jzzyu8VPk+NS9OerNuy6odcdqQx699mwuPKMTPVMTiIuJxjlHWWUVZ/3s2E1QPVLiySms/6Dwv04azlXn9AjNGxCJMG0yoWdkZLh9l/6yVtnnj1wW8BpxCa+nF22ud3PVS7eNIqeghJ0Hipm2aAtVDmbfcyFzPs/l74u3BtzOd8eewbRFWxjQLYlX7ziflAQPVVWO/7d0B899uI1Hrh5ccy380p+Oo2tyfIu/N5GTTZtN6GN++Czjz+7Onf/Pe6mgRgxsu8orq+j30LG7Uev+rioqq4jx3WH7jw+28ticrAa39fLt53FhA5dD/vX9Tfz+vY2c2aU9Cx64JASRi0SWNnlSFOBPNw1n/Nnd6NkhgbSk0F5FIaHliY5i068nkJLg4by+Hestj/EbLuH2i05nzr1jeO5b3r+5jNNS6dw+tmb5uen11692h+98yOa8Iv69aneowhc5JYS1hb5ihXek3epRDk/V675PJkVHK4iJsqAfyHHgSBmJsdHERBkvfbqDGzJ60y6u8SGE9hSWMvq37wPw8ysHcduYYydJnXO8tmIXI09L5cwuScf/RkROUm22hV4tLiZayfwk0T4upllPV+rYLpZ4TzQx0VFMvrBvk8kcoFtKPA9fNQiAR99Zz0ebvXcCz/s81zvk7+ufc+kflgQc3kHkVNYmWugigXywKZ9bn/M+gORHl5/Fk/Prj3qZFB9Dzw4JvHHXBSTGxlBaXsmewlLSO7dr7XBFWsUJnRQ1s+eBK4E859zZAZYb8GdgIlAMfNs5V3/s1zqU0CUYy7Yd4Ia/f1Izn94pkXn3Xcy3/m9Zoy30u8aewQ8uO6tNjacjEgon2uXyAjC+keUTgH6+1xRgWnMDFGnIqL4d+eXVgwFI8ESz8IdjSYiN5tlbGx9o7elFWzjjp3P5xweBL6FsTGs+FlEklILqcjGzdOCdBlrofwcWOedm+ua/AMY653Ib26Za6HKiSssrOXK0gpioKKKjjZc/3cGSTfls2ltEnu+ZrXExUWx4dHyT48XsOlBMu7gYfv7WWuZk5jK8TwfO6prEY9eeXesKHpFwa+knFvUE/B+Pk+0rq5fQzWwK3lY8ffr0CcGu5VQW74mudYL2jkvOqLnsMftgMfM+38Ov52Yxa2U28Z5oLurXmQ6JsfW2s6/oKBf9bmGtslU7C1i1s4BXlu9i7r0XMahHcsu+GZEQaNWmh3NuunMuwzmXkZZW/6EWIqHSKzWRm8/rgyfa+NGsTL43cxV3vLSSsooqFm/MJ33qHP743kb6PzSPjDqDj40bUHv8+4l/+aA1Qxc5bqFooe8GevvN9/KViYRVu7gYLhvcjTmZ3i+LS7cdqPXsVf9nyQL85/6LOTOtPWbeRwvmFJRy18sr2VdUxrqcwnrjDIm0NaFooc8Gvmleo4HCpvrPRVrL418bwht3XcDLtwd+7F7HdrF8ZVBXlj90Kf27JhEVZZgZvVITGdW3I2/d4x1GeLmueZeTQJMtdDObCYwFOptZNvAw4AFwzj0DzMV7yeJmvJctTm6pYEWaKynewwjfs1iX/XQcy7Yf4LJB3YiNieJQaTlJcTGNnjDt2SGB7inxrNhxUMP6SpvXZEJ3zk1qYrkD7g5ZRCItpEtyPFcOPTYsb7APQxl5WirvZOayeON8pt0yksS4aD7YuI97x52ppy1JmxKKPnSRiHZe3468k5nL4dIKvvHcsWfYJifEMNnXav/vhr1ER0VxXt+O7Cs6SmpiLJXO6Qla0qqU0EWacMO5vdlXVMa7a/fwxd7DNeVPL9rC10b04uWlO+qNF19twQMXaxAxaTUay0UkSJVVjnfX7iE10cPbmbnMXLYzqPXevOsCXvx4Oz+ZMIDuKQlszivi9c+ymXLR6aS2q39dPEBZRRWVVU5P55J62uwDLpTQ5WS1r+horevXX7ptFNFRxsBuyWTlHuLjLftZsimfzOzCmjrn9O5Acrz3S/EHm7wjSP7o8rP4YFM+a3YVMrRXCku3HaBdbDQ9OiSwKa+Id743hrN76nJJOUYJXaQFVFRWUVHlGh1O+PYXl7MgK++E9nPHJadzxZDuDO3V4YS2I5FBCV0kzIrLKrh35moWZO0F4JGrBjGkVwrXTfOOJNmzQwK7C0pqrXN653Zs3XekZn5IzxRuv6gvV5/TQ1fXnMKU0EXaiKMVlewrKqNHSjzOwW/mZjGqb0cuG9yNoxWVVFVBQmw0ew+V0j4uhuGPvkdZRe3RH4f2SuGN716gQcNOUUroIicp5xzZB0vqDR4G8NbdF3JOb3XDnGqU0EUixLtr9/DTNz/nwJGyWuV/unEYH27ex0X9Otd0yfzgtTW8/lk24B2npn9X7+WTVVWOGct28sS8DVzUvzN/mzSCKD0I5KShhC4SYTIee499RWVNV/Sz7Kfj6JIczwOvruaNVbXHz9v06wl4oqPIKShh6bb9vJ+Vxx9uGEZsjLp12pqWHg9dRFrZsp9eyoHiMsorq5j7+R4efWd9wHrXDOvBW6tzAPjlO+vJKShh1c4CAO7+0hk8tXALAK8s20luYSlPL9pSs27Gaakav+Ykoxa6SIRwzjFrZTbnpnekU/tYPNFRNZdU3jT9Ez7demzEyE8fHEe3lHgKS8o555f/aXS7r04ZzXmnd2rR2CV4J/pMURE5CZgZ12f0Jr1zO5LiPbWuj39wwsCa6QHdkuiWEg9ASkLtsWZe/+4FbH/8Ct7/wSU1ZTdO/5T0qXN47sNtLfwO5ESphS5yithdUEJlpSPOE0XX5Pia8o17D/OzN9fywv+cS2Js7V7Yt9fk8L2Zq2rmF/1wLOmd27VazFKfWugiQs8OCfTplFgrmQP075rEa3eeXy+ZA1x1Tg/eu//imvn7X1sd9P6cczyzeAsL1u897pizDxazOe8wFZVVHDhSxq4DxSzZmE9FZVXTK5+CgjopambjgT8D0cA/nHOP11neB3gR6OCrM9U5NzfEsYpIGPTrmsT2x6/gmcVbeHzeBnbuL6ZPp0SWbt1PQUk5/zv/C2bdeQEpiR4+2JTPj/6VyWt3nM/Sbft5fN4GAGZ+ZzSTnv2U60f2Yky/zlwzrCdlFVVc9dcPueDMTjx81WByC0s4/7f/JS0pjvzDRxuN6YGv9Ofecf1a4+2fVJrscjGzaGAj8BUgG1gOTHLOrferMx1Y5ZybZmaDgLnOufTGtqsuF5GTy879xVz85ELuG9ePW87rw6jfvF9rebwnitLy4FrO374gnfzDR5nzufdplW/edQFfffrjoGMZ0jOFt783JvjgI8iJXrY4CtjsnNvq29grwDWA/3VSDkj2TacAOccfroi0RX06JZKWFMef399U6wHbg3sksy7nUMBkfmaX9lzcL43nP/KeUK0es+aFj7fXquefzL9/aT8qKh0Th3RnUI9kVu44SEyU0TU5npzCEt78bDcvfbqDZxZv4c5LzuD+V1dztKKSp28Z2TJv/CQSTAv968B459ztvvlbgfOcc/f41ekO/AdIBdoBlzrnVgbY1hRgCkCfPn1G7tixI1TvQ0Rawesrs/nBv9YA8LXhPfnDjcMAmL9uD89/uI14TzTPf/tcNuw5xJ7CUsYN7ArA2t2F9O+aRGxMFO9k5nDPDO+J1iuGdGdzXlHNg0P++4NLOD2tfaMx7D1Uynl1vh0APHXzCK4Y2j1k77WtOqE7RYNM6A/4tvV7MzsfeA442znX4PcvdbmInJxW7TzI7oKSWs9nPRHFZRW8k5nL10f0CnoIguc+3NbgzVQrf3YpndrHhSS2tuhEr3LZDfT2m+/lK/N3G/AagHPuEyAe6Nz8UEWkrRveJzVkyRwgMTaGGzJ6N2s8mZtH9WHikG7Mu+8itv5mIlMnDKhZ9sS7G+rVP3K0gk17D/NOZg6FxeWUVVSxYP1eDpeWA5B/+Ci/nZvF6l3eu2hnr8nhg035J/jOTkxuYQnpU+eQPnUO0xZtIZhLzINpocfgPSk6Dm8iXw7c7Jxb51dnHvCqc+4FMxsIvA/0dI1sXC10EQkV5xxHyioZ9/tF7D10lKkTBnDHxafXjBt/z4zPeCczt956HRI9FBSX1yqbNKpPzeMFX5h8LmPP6sLijfm8tXo3N4/qQ0Z6xybj+Ty7kLczc5h8YTpRZqQkHLvRK+9wKbNWZnPTuX3o6HsE4T8ENywAAAy/SURBVFMLN/Pq8l3c86Uz+fHrmQG3eUNGL37z1SF4YqJPbHAuM5sI/AnvJYnPO+d+bWa/AlY452b7rmx5FmiP9wTpj51zjd5PrIQuIqH24aZ9fOO5pYD3BGz1+POh9NdJwxncI5m+ndvVfGC88NE2hvRKYXdBKff63Yjlb8EDlxDviWLME/WHQg4kwRPNs9/M4FfvrGPj3qKa8h1PXKnRFkXk1LBwQx6TX1her/yDH3+J1Hax/HL2Om4ZfRqDuidz/2urOa1jIvdd2o+4mGi27zvC2P9dBMCDEwbw23n1u2+qTZ0wgPez9rJ8+8GQxF19/f2Ui0/n+pG96N0xsaZVvzmviLteXsnugyWsf3SCErqInDrez9rLbS+uYFD3ZAZ2T+bx64bgCfIJT7mFJeQWljKiTyp/XrCJPy7YWDP08Hm/WcDeQ43f9HTfuH7sP3KU28ecTreUeDzRUVz9tw9Zl3Oops6HP/kS972ymi8P6EKfjolcMaQ7Dnhv/V6+NCCNuJiGn1Or8dBFRI6Dc46ioxUkxR8bxKyguIynF21h+pKtALx2x/mkJHjo16U9ZjT4vNd/frKddzJzmfmd0USfwANFlNBFRELshY+2MeK0VIb2at3HAOoBFyIiIdYWH/6h0RZFRCKEErqISIRQQhcRiRBK6CIiEUIJXUQkQiihi4hECCV0EZEIoYQuIhIhlNBFRCKEErqISIRQQhcRiRBK6CIiESKohG5m483sCzPbbGZTG6hzg5mtN7N1ZjYjtGGKiEhTmhxt0cyigaeArwDZwHIzm+2cW+9Xpx/wIHChc+6gmXVpqYBFRCSwYFroo4DNzrmtzrky4BXgmjp1vgM85Zw7COCcywttmCIi0pRgEnpPYJfffLavzF9/oL+ZfWRmn5rZ+EAbMrMpZrbCzFbk5+cfX8QiIhJQqE6KxgD9gLHAJOBZM6v3GA/n3HTnXIZzLiMtLS1EuxYREQguoe8GevvN9/KV+csGZjvnyp1z24CNeBO8iIi0kmAS+nKgn5n1NbNY4CZgdp06/8bbOsfMOuPtgtkawjhFRKQJTSZ051wFcA8wH8gCXnPOrTOzX5nZ1b5q84H9ZrYeWAj8yDm3v6WCFhGR+sw5F5YdZ2RkuBUrVoRl3yIiJyszW+mcywi0THeKiohECCV0EZEIoYQuIhIhlNBFRCKEErqISIRQQhcRiRBK6CIiEUIJXUQkQiihi4hECCV0EZEIoYQuIhIhlNBFRCKEErqISIRQQhcRiRBK6CIiESKohG5m483sCzPbbGZTG6l3nZk5Mws4Vq+IiLScJhO6mUUDTwETgEHAJDMbFKBeEnAfsDTUQYqISNOCaaGPAjY757Y658qAV4BrAtR7FHgCKA1hfCIiEqRgEnpPYJfffLavrIaZjQB6O+fmNLYhM5tiZivMbEV+fn6zgxURkYad8ElRM4sC/gD8oKm6zrnpzrkM51xGWlraie5aRET8BJPQdwO9/eZ7+cqqJQFnA4vMbDswGpitE6MiIq0rmIS+HOhnZn3NLBa4CZhdvdA5V+ic6+ycS3fOpQOfAlc751a0SMQiIhJQkwndOVcB3APMB7KA15xz68zsV2Z2dUsHKCIiwYkJppJzbi4wt07ZLxqoO/bEwxIRkebSnaIiIhFCCV1EJEIooYuIRAgldBGRCKGELiISIZTQRUQihBK6iEiEUEIXEYkQSugiIhFCCV1EJEIooYuIRAgldBGRCKGELiISIZTQRUQihBK6iEiECCqhm9l4M/vCzDab2dQAyx8ws/Vmlmlm75vZaaEPVUREGtNkQjezaOApYAIwCJhkZoPqVFsFZDjnhgKzgN+FOlAREWlcMC30UcBm59xW51wZ8ApwjX8F59xC51yxb/ZTvA+SFhGRVhRMQu8J7PKbz/aVNeQ2YF6gBWY2xcxWmNmK/Pz84KMUEZEmhfSkqJl9A8gAngy03Dk33TmX4ZzLSEtLC+WuRUROecE8JHo30NtvvpevrBYzuxR4CLjEOXc0NOGJiEiwgmmhLwf6mVlfM4sFbgJm+1cws+HA34GrnXN5oQ9TRESa0mRCd85VAPcA84Es4DXn3Doz+5WZXe2r9iTQHviXma02s9kNbE5ERFpIMF0uOOfmAnPrlP3Cb/rSEMclIiLNpDtFRUQihBK6iEiEUEIXEYkQSugiIhFCCV1EJEIooYuIRAgldBGRCKGELiISIZTQRUQihBK6iEiEUEIXEYkQSugiIhFCCV1EJEIooYuIRAgldBGRCKGELiISIYJK6GY23sy+MLPNZjY1wPI4M3vVt3ypmaWHOlAREWlckwndzKKBp4AJwCBgkpkNqlPtNuCgc+5M4I/AE6EOVEREGhdMC30UsNk5t9U5Vwa8AlxTp841wIu+6VnAODOz0IUpIiJNCeaZoj2BXX7z2cB5DdVxzlWYWSHQCdjnX8nMpgBTfLNHzWzt8QTdSjpTJ/42pi3H15ZjA8V3ItpybHBqxHdaQwuCekh0qDjnpgPTAcxshXMuozX33xyK7/i15dhA8Z2IthwbKL5gulx2A7395nv5ygLWMbMYIAXYH4oARUQkOMEk9OVAPzPra2axwE3A7Dp1ZgPf8k1/Hfivc86FLkwREWlKk10uvj7xe4D5QDTwvHNunZn9CljhnJsNPAe8ZGabgQN4k35Tpp9A3K1B8R2/thwbKL4T0ZZjg1M8PlNDWkQkMuhOURGRCKGELiISIcKS0JsaSqAV9t/bzBaa2XozW2dm9/nKHzGz3Wa22vea6LfOg754vzCzy1shxu1m9rkvjhW+so5m9p6ZbfL9TPWVm5n9xRdfppmNaOHYzvI7RqvN7JCZfT9cx8/MnjezPP/7Go7nWJnZt3z1N5nZtwLtK4TxPWlmG3wxvGlmHXzl6WZW4ncMn/FbZ6Tvb2Kz7z2E5Oa9BuJr9u+yJf6vG4jtVb+4tpvZal95OI5dQ7kkPH9/zrlWfeE9sboFOB2IBdYAg1o5hu7ACN90ErAR77AGjwA/DFB/kC/OOKCvL/7oFo5xO9C5TtnvgKm+6anAE77picA8wIDRwNJW/n3uwXuzQ1iOH3AxMAJYe7zHCugIbPX9TPVNp7ZgfJcBMb7pJ/ziS/evV2c7y3wxm+89TGjB+Jr1u2yp/+tAsdVZ/nvgF2E8dg3lkrD8/YWjhR7MUAItyjmX65z7zDd9GMjCe7drQ64BXnHOHXXObQM2430frc1/iIUXgWv9yv/pvD4FOphZ91aKaRywxTm3o5E6LXr8nHNL8F5dVXefzTlWlwPvOecOOOcOAu8B41sqPufcf5xzFb7ZT/He39EgX4zJzrlPnTcD/NPvPYU8vkY09Ltskf/rxmLztbJvAGY2to0WPnYN5ZKw/P2FI6EHGkqgsWTaosw7MuRwYKmv6B7fV6Hnq78mEZ6YHfAfM1tp3iETALo653J903uArmGMr9pN1P6HaivHr7nHKpzH8H/wttqq9TWzVWa22Mwu8pX19MXUmvE153cZjuN3EbDXObfJryxsx65OLgnL398pfVLUzNoDrwPfd84dAqYBZwDDgFy8X+fCZYxzbgTeUS7vNrOL/Rf6WhphvebUvDeaXQ38y1fUlo5fjbZwrBpiZg8BFcDLvqJcoI9zbjjwADDDzJLDEFqb/F3WMYnajYmwHbsAuaRGa/79hSOhBzOUQIszMw/eX8DLzrk3AJxze51zlc65KuBZjnULtHrMzrndvp95wJu+WPZWd6X4fuaFKz6fCcBnzrm9vljbzPGj+ceq1WM0s28DVwK3+P7p8XVl7PdNr8TbL93fF4t/t0yLxnccv8tWPX7mHWLka8CrfjGH5dgFyiWE6e8vHAk9mKEEWpSv7+05IMs59we/cv9+568C1WfWZwM3mfdBHn2BfnhPsrRUfO3MLKl6Gu8JtLXUHmLhW8BbfvF903cGfTRQ6Pd1ryXVaiG1lePnt8/mHKv5wGVmlurrXrjMV9YizGw88GPgaudcsV95mnmfQYCZnY73WG31xXjIzEb7/n6/6feeWiK+5v4uW/v/+lJgg3OupislHMeuoVxCuP7+QnGmt7kvvGd6N+L9BH0oDPsfg/crUCaw2veaCLwEfO4rnw1091vnIV+8XxCiM+SNxHc63qsE1gDrqo8R3iGJ3wc2AQuAjr5yw/sQki2++DNa4Ri2wzsAW4pfWViOH94PlVygHG/f423Hc6zw9mVv9r0mt3B8m/H2mVb//T3jq3ud73e+GvgMuMpvOxl4E+sW4G/47vRuofia/btsif/rQLH5yl8A7qxTNxzHrqFcEpa/P936LyISIU7pk6IiIpFECV1EJEIooYuIRAgldBGRCKGELiISIZTQRUQihBK6iEiE+P/NPV+j8/aLgwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/content/drive/My Drive/Houseplant Classifier/Models/stage-2-quickbaseline.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y9KCqjqc0rbo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "outputId": "daaa2425-e13d-42d7-9ecc-0efb74ead35f"
      },
      "source": [
        "learn.fit_one_cycle(10, lr_max=slice(1e-5, 1e-4))\n",
        "learn.save('stage-3-quickbaseline')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_k_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.789546</td>\n",
              "      <td>1.651657</td>\n",
              "      <td>0.595864</td>\n",
              "      <td>0.840177</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.822559</td>\n",
              "      <td>1.686037</td>\n",
              "      <td>0.591728</td>\n",
              "      <td>0.837814</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.830991</td>\n",
              "      <td>1.679663</td>\n",
              "      <td>0.593205</td>\n",
              "      <td>0.833087</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.796793</td>\n",
              "      <td>1.672298</td>\n",
              "      <td>0.597932</td>\n",
              "      <td>0.836041</td>\n",
              "      <td>01:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.744889</td>\n",
              "      <td>1.660933</td>\n",
              "      <td>0.598523</td>\n",
              "      <td>0.835451</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.709819</td>\n",
              "      <td>1.647272</td>\n",
              "      <td>0.600591</td>\n",
              "      <td>0.843722</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.655534</td>\n",
              "      <td>1.638553</td>\n",
              "      <td>0.600295</td>\n",
              "      <td>0.845199</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.598438</td>\n",
              "      <td>1.632686</td>\n",
              "      <td>0.605022</td>\n",
              "      <td>0.845790</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.588223</td>\n",
              "      <td>1.625510</td>\n",
              "      <td>0.605318</td>\n",
              "      <td>0.844904</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.579358</td>\n",
              "      <td>1.630201</td>\n",
              "      <td>0.607681</td>\n",
              "      <td>0.845199</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/content/drive/My Drive/Houseplant Classifier/Models/stage-3-quickbaseline.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FID7XDVL0r6L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "outputId": "08f1b5b8-42e7-4378-f673-91c7f7f94958"
      },
      "source": [
        "learn.fit_one_cycle(10, lr_max=slice(1e-5, 1e-4))\n",
        "learn.save('stage-4-quickbaseline')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_k_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.581178</td>\n",
              "      <td>1.629941</td>\n",
              "      <td>0.602068</td>\n",
              "      <td>0.844609</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.611442</td>\n",
              "      <td>1.659820</td>\n",
              "      <td>0.600886</td>\n",
              "      <td>0.840473</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.665524</td>\n",
              "      <td>1.693376</td>\n",
              "      <td>0.591433</td>\n",
              "      <td>0.838405</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.617467</td>\n",
              "      <td>1.680925</td>\n",
              "      <td>0.592614</td>\n",
              "      <td>0.839586</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.578127</td>\n",
              "      <td>1.658810</td>\n",
              "      <td>0.604136</td>\n",
              "      <td>0.847858</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.510131</td>\n",
              "      <td>1.644334</td>\n",
              "      <td>0.604136</td>\n",
              "      <td>0.842245</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.472106</td>\n",
              "      <td>1.641933</td>\n",
              "      <td>0.604727</td>\n",
              "      <td>0.846972</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.442126</td>\n",
              "      <td>1.625005</td>\n",
              "      <td>0.607386</td>\n",
              "      <td>0.850222</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.424646</td>\n",
              "      <td>1.624912</td>\n",
              "      <td>0.609749</td>\n",
              "      <td>0.848744</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.420558</td>\n",
              "      <td>1.619842</td>\n",
              "      <td>0.610931</td>\n",
              "      <td>0.847563</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/content/drive/My Drive/Houseplant Classifier/Models/stage-4-quickbaseline.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yM-L6qeKxNyz"
      },
      "source": [
        "### Quick Resnet50"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tbxxmj6ExQtD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195,
          "referenced_widgets": [
            "30ce680fbe41450994a2c0b0fccee4e8",
            "7661efa93f8442118e81f2e6acbe92b1",
            "c2ffc0faa794413789d87336ca4c3ad7",
            "8a9b3aa0e7174f47aa2afa91e2316d46",
            "4e5f1fea739b4524b1bb4df143d6cb81",
            "8b0172e5b23c423388e692dc987c9e7e",
            "aef4f73d588c4bdbb35412c249251d85",
            "3260f3cf518349c694156b800d26e0a3"
          ]
        },
        "outputId": "90a1466d-17b6-40c1-ec3e-411790269e54"
      },
      "source": [
        "learn = create_cnn_learner(pct_images=0.25, \n",
        "                           deterministic=True, \n",
        "                           size=224, \n",
        "                           img_path ='/content/plant_images_database_manualcleanedlosses10/', \n",
        "                           split_path = '/content/plant_image_database/',\n",
        "                           log_name = 'quickresnet50.csv', \n",
        "                           arch='resnet50')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random state set:101, cuda used: True\n",
            "Split Path defined, preparing pseudo stratified split.\n",
            "Creating path:label dataframe from /content/plant_image_database/, using 100.00% of the images (77700 images.) Shuffle = False.\n",
            "Preparing stratified 5 fold split, random state is 101. Returning fold 0.\n",
            "Creating path:label dataframe from /content/plant_images_database_manualcleanedlosses10/, using 25.00% of the images (16940 images.) Shuffle = False.\n",
            "We have 517 classes, with a training set of size 13555, and a validation set of size 3385\n",
            "The main split has 80.00% training data with 518 classes, the reduced split has 80.02 % training data, with 517 classes.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet50-19c8e357.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-19c8e357.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "30ce680fbe41450994a2c0b0fccee4e8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=102502400.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ibn-IIusxZpO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "1491785b-d330-4089-b587-63aaa26cf799"
      },
      "source": [
        "print(f'Freezing model body')\n",
        "learn.freeze()\n",
        "enumerate_params(learn)\n",
        "learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Freezing model body\n",
            "25,879,104 total parameters.\n",
            "2,424,192 training parameters.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=0.004786301031708717, lr_steep=0.010964781977236271)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jcjmz2E5xb_d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "outputId": "2039bde1-b39b-4353-9882-d320356c872e"
      },
      "source": [
        "learn.fit_one_cycle(10, 1e-02)\n",
        "learn.save('stage-1-quick-resnet50')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_k_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>4.841085</td>\n",
              "      <td>3.629766</td>\n",
              "      <td>0.254653</td>\n",
              "      <td>0.501329</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>4.102129</td>\n",
              "      <td>3.951435</td>\n",
              "      <td>0.209453</td>\n",
              "      <td>0.459380</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>3.266984</td>\n",
              "      <td>3.014024</td>\n",
              "      <td>0.332939</td>\n",
              "      <td>0.606499</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.838333</td>\n",
              "      <td>3.158679</td>\n",
              "      <td>0.304284</td>\n",
              "      <td>0.593501</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.489719</td>\n",
              "      <td>2.522846</td>\n",
              "      <td>0.404136</td>\n",
              "      <td>0.703397</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.056407</td>\n",
              "      <td>2.154622</td>\n",
              "      <td>0.481832</td>\n",
              "      <td>0.761300</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.621702</td>\n",
              "      <td>1.876814</td>\n",
              "      <td>0.532053</td>\n",
              "      <td>0.802068</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.248830</td>\n",
              "      <td>1.730972</td>\n",
              "      <td>0.570162</td>\n",
              "      <td>0.831905</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.998498</td>\n",
              "      <td>1.649505</td>\n",
              "      <td>0.594978</td>\n",
              "      <td>0.835155</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.845647</td>\n",
              "      <td>1.636713</td>\n",
              "      <td>0.597932</td>\n",
              "      <td>0.837223</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/content/drive/My Drive/Houseplant Classifier/Models/stage-1-quick-resnet50.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-W7jxn6xgwm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "d430b586-862f-4ab6-c71e-e9724c392c13"
      },
      "source": [
        "print(f'Unfreezing model body')\n",
        "learn.unfreeze()\n",
        "enumerate_params(learn)\n",
        "learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unfreezing model body\n",
            "25,879,104 total parameters.\n",
            "25,879,104 training parameters.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=4.365158383734525e-06, lr_steep=9.12010818865383e-07)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qslUuOTHxhFX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "outputId": "def28a71-4649-4c2c-f994-9a4851b63ec3"
      },
      "source": [
        "learn.fit_one_cycle(10, lr_max=slice(1e-5, 1e-4))\n",
        "learn.save('stage-2-quick-resnet50')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_k_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.861795</td>\n",
              "      <td>1.638012</td>\n",
              "      <td>0.593205</td>\n",
              "      <td>0.842836</td>\n",
              "      <td>01:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.877957</td>\n",
              "      <td>1.643481</td>\n",
              "      <td>0.593501</td>\n",
              "      <td>0.844609</td>\n",
              "      <td>01:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.872858</td>\n",
              "      <td>1.640017</td>\n",
              "      <td>0.591728</td>\n",
              "      <td>0.844904</td>\n",
              "      <td>01:21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.818900</td>\n",
              "      <td>1.614497</td>\n",
              "      <td>0.602068</td>\n",
              "      <td>0.845495</td>\n",
              "      <td>01:21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.737809</td>\n",
              "      <td>1.597066</td>\n",
              "      <td>0.605318</td>\n",
              "      <td>0.849926</td>\n",
              "      <td>01:21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.672943</td>\n",
              "      <td>1.575761</td>\n",
              "      <td>0.608863</td>\n",
              "      <td>0.853176</td>\n",
              "      <td>01:21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.623661</td>\n",
              "      <td>1.571752</td>\n",
              "      <td>0.611817</td>\n",
              "      <td>0.854357</td>\n",
              "      <td>01:21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.568811</td>\n",
              "      <td>1.560181</td>\n",
              "      <td>0.615066</td>\n",
              "      <td>0.854653</td>\n",
              "      <td>01:21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.539619</td>\n",
              "      <td>1.553146</td>\n",
              "      <td>0.613294</td>\n",
              "      <td>0.855539</td>\n",
              "      <td>01:21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.527328</td>\n",
              "      <td>1.552151</td>\n",
              "      <td>0.613589</td>\n",
              "      <td>0.858198</td>\n",
              "      <td>01:21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/content/drive/My Drive/Houseplant Classifier/Models/stage-2-quick-resnet50.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Yxv3OiHsNLu"
      },
      "source": [
        "### Quick Resnet101"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JM5L0_YqysuA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202,
          "referenced_widgets": [
            "994a37c61c6940ce944e86d84cb733d0",
            "4b05eb2a453645299df5c710a9473c9f",
            "7f9a8a08590f49bfb9876894efeac631",
            "a61df80a742f47588588d4b831e2038e",
            "5d316a9117ac4d4f9bc2601f3d6c7a0e",
            "9241dcdd7631452991b1dd19f13e99b2",
            "f446329970734b9cb5bb31d59478bcdc",
            "b781e314d37d48ff908c9d9eac6a59cd"
          ]
        },
        "outputId": "30f5ba5c-e9c4-4984-8692-56aa9fef51e3"
      },
      "source": [
        "learn = create_cnn_learner(pct_images=0.25, \n",
        "                           deterministic=True, \n",
        "                           size=224, \n",
        "                           img_path ='/content/plant_images_database_manualcleanedlosses10/', \n",
        "                           split_path = '/content/plant_image_database/',\n",
        "                           log_name = 'quickresnet101.csv', \n",
        "                           arch='resnet101')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random state set:101, cuda used: True\n",
            "Split Path defined, preparing pseudo stratified split.\n",
            "Creating path:label dataframe from /content/plant_image_database/, using 100.00% of the images (77700 images.) Shuffle = False.\n",
            "Preparing stratified 5 fold split, random state is 101. Returning fold 0.\n",
            "Creating path:label dataframe from /content/plant_images_database_manualcleanedlosses10/, using 25.00% of the images (16940 images.) Shuffle = False.\n",
            "We have 517 classes, with a training set of size 13555, and a validation set of size 3385\n",
            "The main split has 80.00% training data with 518 classes, the reduced split has 80.02 % training data, with 517 classes.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet101-5d3b4d8f.pth\" to /root/.cache/torch/hub/checkpoints/resnet101-5d3b4d8f.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "994a37c61c6940ce944e86d84cb733d0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=178728960.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0YTc9UAsywtK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "8bee8916-365b-4e14-f709-098554712c59"
      },
      "source": [
        "print(f'Freezing model body')\n",
        "learn.freeze()\n",
        "enumerate_params(learn)\n",
        "learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Freezing model body\n",
            "44,871,232 total parameters.\n",
            "2,476,416 training parameters.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=0.004786301031708717, lr_steep=9.12010818865383e-07)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r5AZpFn8zQVF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "outputId": "67b55e2a-7478-4a1d-9e38-9e18efbf8162"
      },
      "source": [
        "learn.fit_one_cycle(10, 1e-02)\n",
        "learn.save('stage-1-quick-resnet101')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_k_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>4.763552</td>\n",
              "      <td>3.647169</td>\n",
              "      <td>0.238109</td>\n",
              "      <td>0.481241</td>\n",
              "      <td>01:36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>4.157120</td>\n",
              "      <td>4.194188</td>\n",
              "      <td>0.193796</td>\n",
              "      <td>0.440177</td>\n",
              "      <td>01:35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>3.299127</td>\n",
              "      <td>3.108125</td>\n",
              "      <td>0.298966</td>\n",
              "      <td>0.584047</td>\n",
              "      <td>01:35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.888129</td>\n",
              "      <td>2.976003</td>\n",
              "      <td>0.328508</td>\n",
              "      <td>0.624225</td>\n",
              "      <td>01:35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.510884</td>\n",
              "      <td>2.631183</td>\n",
              "      <td>0.392614</td>\n",
              "      <td>0.684490</td>\n",
              "      <td>01:35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.051521</td>\n",
              "      <td>2.157021</td>\n",
              "      <td>0.491876</td>\n",
              "      <td>0.762777</td>\n",
              "      <td>01:35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.656881</td>\n",
              "      <td>1.986550</td>\n",
              "      <td>0.516691</td>\n",
              "      <td>0.793796</td>\n",
              "      <td>01:35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.265091</td>\n",
              "      <td>1.732461</td>\n",
              "      <td>0.575185</td>\n",
              "      <td>0.831315</td>\n",
              "      <td>01:35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.026240</td>\n",
              "      <td>1.644695</td>\n",
              "      <td>0.602659</td>\n",
              "      <td>0.844904</td>\n",
              "      <td>01:36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.842179</td>\n",
              "      <td>1.630958</td>\n",
              "      <td>0.601773</td>\n",
              "      <td>0.845790</td>\n",
              "      <td>01:35</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/content/drive/My Drive/Houseplant Classifier/Models/stage-1-quick-resnet101.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mrzbxWtzzYqk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "a39f191c-71ed-448a-f7d8-09b89908fbb8"
      },
      "source": [
        "print(f'Unfreezing model body')\n",
        "learn.unfreeze()\n",
        "enumerate_params(learn)\n",
        "learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unfreezing model body\n",
            "44,871,232 total parameters.\n",
            "44,871,232 training parameters.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=4.365158383734525e-06, lr_steep=7.585775847473997e-07)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rZ82+ap558zAAZpCVkkhOWiI5qYlkpSaSk5ZEfmYys/IzmTU2k5ljMkhP1q9BpDtS18qKBzZQ09TBI19YTOEUDQCHkn4DJKIEAo6a5g52Hq7nF68cYOvB44zLTuGH183jxsKCk/6F75zjaH0b28rq2FPRSF1LB/WtndS1dFLf2kn58VZeqmulvSvw7msm5KRy5thMCqecwcUz85gzLkt/ZUaQ8uMtrHhgA3XNnTxyy2I+MOkMv0uKOubcyDovrLCw0G3ZssXvMuQU1Ld28vuNB1lfVENCnJEQH0divJEQF0dCvNHY1kVlYzuVDW1UNbbTFej5TI7PTuH25TP4xBAC4FR0BxxltS28c6yR/cca2XesiT1HG9hf2QT09DtfOCOXi2bmcvGsPPKzYucm5pHmUE1PCDS2dfLYF89jQUGO3yWNWGa21TlX2N86tQjEM4frWnnw9RKe2HSI5o5uZo/LIiHO6OwO0BVwdHUH6Ox2ZCQnMCYrmZljcsnPSiY/K4Xx2alcNCvXkz7++DhjSm46U3LT+cjc925LWNnQxusHqlm7v+exetsRzODimXl86tyJXDonn0RNRwyb3Uca+OLvNtPS2c3jty5h3oTsk79ITotaBHJK6ls72X2kgV1H6tl9pIH2rgBjgl/e+VnJjMlMITE+jlWbDvGnbUdwwNULxnHrxdOYO37k/CI759hb0cjzO47y1NZyjta3kZuRxMfPKeCThROZlpcx6OvbOrt5bV8VL+ysYN+xRkZnJJOXkcyYrGTGZCaTl5lM4eRRjM1Wa6O39q5uXtx1jMc2HGRTSS2j0pN47JbzmDM+y+/SRrzBWgQKgihXUd/GzsP1fPDMvNP+a3brwVp++3oJOw7XU1b73v2IxmalkJYUz7GGNpo7uv/mNWlJ8Xzq3El84cIpI/4iX90Bx2v7Klm1qYy/7q2kO+CYOSaDmfkZzMjLYPqYDGaMyWBsVgrri2t4fmcFr+ytpKWjm+zURBZOzKGupYPKhnaqm97r+kqIM66cP44vXDiVRROjo8ujsqGNbeX1bC+vo7qpg1n5Gcwel8XscVkDXucnEHAcqm3hic1lPLWljJrmDiaNSuOm8yZxY+FERqUnhfmniE4Kghj01qHjPPhGKc/vOEpXwLFoYg73rjibiaOG/qXc1tnNz17ex8o1xeRmJLN46ijmjs9i7vhs5o7PIjfjvZN2mtq7qGxo41hDO3UtHSydPpqctOj7Ba5saOMPbx5m68FaDlQ2cai2hUCfX6HcjCQ+PHcsV8wby5Jpo/8mgAMBx/GWDo7Wt/HHtw7zP5vLaGzv4gOTcvjChVO5fO7YEXM2rHOO3UcbePWdKt4uq2NHeT0VDT0ndsXHGRnJCdS3dr67/YScVGaPyyQpIY7a5o7go5PjLR10BxxxBpfOzufTSyZz0YxcDdiHmIIgRnR2B3h+ZwUPvl7C22V1ZCYn8MlzJzJjTAb/8uweMPj3jy/givnjTvpeOw/X8/Un32bfsSZWLJ7Edz86mwxNs3yf9q5uSqtbOFDZRPnxFhZNzKFwyijih/gl1tTexdNbynhoXSkHa1rIz0pm8dTRzBmXxdzxWczpE7h+a+/qZkNxLS/vPsZf9hzjSPCM3qm56SwoyGZBQQ4LC7KZOz6blMQ4qhrb2X20gT1HG9l9tIG9RxsIOMeo9KTgI5lR6YmMyUzhw3PzGZeta1l6RUEQA8pqW/jMbzdSWtPClNFpfP6CqXz8nIJ3v7wP1bRw56o32VZez2eWTOa7H51NSuL7B2K7ugP86tUifv6X/YxKT+LfbljA8jPHhPvHiTndAccreyt5amsZOw83cLjuvS64/KxkzhybxbTcdKblpTM1t+cxPjs1ZH81O+doaO2i7HgL5cdbOdbQRl1LJw1tPdNuTzx2Ha6nuaOb1MR4LpyZy6Wzx7D8rDGMydRYR6RTEES5E1Psmtq7+OkNC7h0dn6/XxAdXQF++uJeHlhbwuxxWXz1QzNoaOukqrG959HUzt6KRoqrmrl20Xh+cM3cqOzeGQnqWjrYfbSB3Ud6Hu8ca6S0uvlvxmKSE+K4YEYuN5xTwCWzxww6w6qivo2iqiaqm9qpbuqgpqn93f8fqWul/HgrTe1d73tdWlI82amJZAdPyJsxJoNLZ4/h/Om5/f4hIZFLQRDFSqubWfHABlo7u3nslvOGNMXur3uP8Y0nt3G85b3+2+zURPIye2a0/N2SyVw5hO4jCS/nHFWN7RRXN1NS3cw7FY28sLOCioY2slMTuXbReG44p4D5E7I51tDOxpIa1hfVsKG4htKalr95r4Q4Y3RGEqPTkxmfk0LBGWkUnJHKhJxUCs5IY2x2CjlpiZouG0V8CwIzuxz4byAe+I1z7l/7rJ8E/A7ICW7zbefcc4O9ZywFgXOOHYfrmZWf2e9fX8VVTdz0wEbau7r5/ReXnNIUu7qWDkprWsjLTCY3I0nX5BmhugOONw5U8/TWcl7cVUF7V4BR6UnUNncAkJmSwHlTR7Fk2mjmjM9iTGYyuRnJZKUkajA2xvgSBGYWD+wDLgPKgc3ACufc7l7brATecs7dZ2ZzgOecc1MGe99YCoJ/e2Ev971aRFpSPJfMzuej88ey7MwxpCTGc6CyiZse2EB3wPH7W8/jrLGaZx3r6ls7eXb7UTaV1DBvQjZLpo1m9risIQ9cS3Tz68zixcAB51xxsIgngGuB3b22ccCJb7Bs4IiH9Ywov1lbzH2vFnHdovGkJiXw4q4K/rTtCGlJ8XzorDFsLKnFOceq25YwK183kpOe7r2bzpvETedN8rsUGWG8DIIJQFmv5+XAeX22+T7wkpndCaQDl/b3RmZ2G3AbwKRJI/tDHgg4Gto6Bx2EfebNcn707B6unD+W/7xxEfFxxg+vncuG4lqe3XGUF3dVkBBnPH7rEmYqBERkmPyeGL4CeNg5959mthR41MzmOecCvTdyzq0EVkJP15APdYbM3at3sWrTIVYsnsRXL5n5vjsp/XXvMe56ejvnTx/Nzz656N1mfUJ8HBfOzOXCmbn88Nq5dAWcZm2ISEh4OSXgMDCx1/OC4LLebgGeBHDOrQdSgFwPa/LV5tJaHt1wkFn5mTy+6RDLfvoK97y8j+bgtL2tB2u5/fdvMmdcFis/WzjgAG5CfJxCQERCxssWwWZgpplNpScAPgXc1GebQ8AlwMNmNpueIKjysCbftHd1851ndjAhJ5WnvrSUYw1t/PTFd7jn5f08tuEgN58/hZVrihmXncpDnz9XZ/GKSNh41iJwznUBXwFeBPYATzrndpnZP5vZNcHNvgHcambbgFXAzW6kndgwRPe/WsyByiZ+dN080pMTmJaXwX1/dw7P3H4+03Iz+I+X9pGSGM8jX1gcUZcUEJHopxPKwqCoqokr7lnLR+aN5d4VZ79vvXOO9cU1TDwj7ZQuCiciMlS6MY2PAgHHd57ZQUpiHN+7ak6/25gZ50+P2qEREYlwOn/cY09tLWNTSS3/eOXs980QEhGJBAoCD1U1tvPj5/ayeOoobiycePIXiIj4QEHgoR/+eTetHd38+GPzdV0XEYlYCgKP/HXvMVZvO8KXl01nxpjB728rIuInBYEHGto6+cdndjIrP4M7ls/wuxwRkUEpCDzwk+f2UNnYxr/fsJCkBB1iEYls+pYKsTcOVLNqUxm3XjSNRRNz/C5HROSkFAQh1Nzexbef2c7U3HT+4bJZfpcjIjIkOqEshH764juU1bby5N8v1UXhRGTEUIsgRLaU1vK79aV8bulkFk8d5Xc5IiJDpiAIgbbObr759HbGZ6fyzcvP8rscEZFToq6hELj3r/sprm7msVvOI12XjxaREUYtghB4bkcFy8/M48KZunCciIw8CoJh6uoOUFbbwuxxWX6XIiJyWhQEw3Skro2ugGPK6HS/SxEROS0KgmEqqWkGYEqugkBERiYFwTAdPBEEo3VnMREZmRQEw1RS3UxaUrxuOiMiI5aCYJgO1rQweXQ6ZrrfgIiMTAqCYSqtbla3kIiMaAqCYejqDlB2vEUDxSIyoikIhuFofRud3U4tAhEZ0RQEw1BSfWLGkFoEIjJyKQiG4aDOIRCRKKAgGIaS6hZSE+MZo6mjIjKCKQiG4WBNM5NHp2nqqIiMaAqCYSipadb4gIiMeAqC09QdcJTVauqoiIx8CoLTdKSuVVNHRSQqKAhOU6lmDIlIlFAQnKbSmhZA5xCIyMinIDhNpdXNpCTGkZ+lqaMiMrIpCE7TweCMIU0dFZGRTkFwmkqqe84hEBEZ6RQEp6Fn6mirBopFJCp4GgRmdrmZvWNmB8zs2wNsc6OZ7TazXWb2uJf1hMrR+lY6ugMaKBaRqJDg1RubWTzwS+AyoBzYbGarnXO7e20zE/gOcIFz7riZjfGqnlAqrdaMIRGJHl62CBYDB5xzxc65DuAJ4No+29wK/NI5dxzAOVfpYT0h8945BBojEJGRz8sgmACU9XpeHlzW2yxglpm9YWYbzOzy/t7IzG4zsy1mtqWqqsqjcofu3amjmSl+lyIiMmx+DxYnADOBZcAK4AEzy+m7kXNupXOu0DlXmJeXF+YS36+0poXJo9KJi9PUUREZ+bwMgsPAxF7PC4LLeisHVjvnOp1zJcA+eoIhopXWaOqoiEQPL4NgMzDTzKaaWRLwKWB1n23+SE9rADPLpaerqNjDmoatO+A4VNPCVE0dFZEo4VkQOOe6gK8ALwJ7gCedc7vM7J/N7JrgZi8CNWa2G3gFuMs5V+NVTaFQ0dBGR3eAyZoxJCJRwrPpowDOueeA5/os+16v/zvg68HHiFBarRlDIhJd/B4sHnHenTqqFoGIRImYCYJ1B6q5+t7XqW/tHNb7lFY3k5wQx9gsTR0VkegQM0GQlZrIjsP1/PGtvhOXTk1pTQuTR6dp6qiIRI2YCYJ5E7JZUJDNqk2H6BmaGFxbZ3e/25VWN2ugWESiSswEAcCKxZPYW9HIW2V1g25XUd/Guf/yMjfcv56tB2vfXR4IOA7WauqoiESXmAqCqxeOJz0pnsc3Hhp0u/tfK6K1o5uy2hY+ft96vvToVoqrmnqmjnYFdDKZiESVmAqCjOQErj17An/efmTAQePKhjZWbTrE9R+YwKt3LePrl81i7f4qLvvZGr71h+0ATFXXkIhEkZgKAoCbFk+irTPA/3u7/0HjlWuK6Qo47lg+g7SkBL56yUxevWs5Ny2exLqinnPddEMaEYkmnp5QFonmTchm/oRsHt94iM8smfw39xyubmrnsY0HuXbR+L8ZEM7LTOaH183j5gumsP9YI+NzUv0oXUTEEzHXIoCBB41/s7aEjq4Adyyf0e/rpudlcPm8ceEoUUQkbGIyCK5Z1DNovKrXoHFtcwePrC/lqgXjmZ6X4V9xIiJhFpNBkJGcwDWLJvCn7UdoaOsZNH7w9RJaO7v5yof6bw2IiESrmAwCeG/Q+I9vHaa+pZOH15Vy5bxxzMrP9Ls0EZGwirnB4hPmF2Qzb0IWj288RHVTB03tXWoNiEhMitkWAcBNiyezt6KR+18r4iNz85k9LsvvkkREwi6mg+CaReNJS4qnoyvAnR+K+Dtkioh4Ima7hqBn0PgrH5pBZUM78yZk+12OiIgvYjoIAG5fpnEBEYltMd01JCIiCgIRkZinIBARiXEKAhGRGKcgEBGJcQoCEZEYpyAQEYlxCgIRkRhnzjm/azglZlYFHOyzOBuoH+B57//nAtUelNV3/6HYfrBt+ls3lGWDPffi2ETCcelvud/Hpb99hmJ7fWZOfZuR8pk51ePS32smO+fy+t3SOTfiH8DKgZ73+f+WcOw/FNsPtk1/64ay7CTHKeTHJhKOy6keB31m9JmJxM/MqR6XU31NtHQN/WmQ533XhWP/odh+sG36WzeUZSd7HmqRcFz6W+73cTmdfegzc/rbR8Nn5nTef8ivGXFdQ8NhZlucc4V+1xGJdGz6p+MyMB2b/o3E4xItLYKhWul3ARFMx6Z/Oi4D07Hp34g7LjHVIhARkfeLtRaBiIj0oSAQEYlxCgIRkRinIBARiXEKgiAzu8jM7jez35jZOr/riSRmFmdm/2Jm95rZ5/yuJ1KY2TIzWxv83Czzu55IYmbpZrbFzK7yu5ZIYpWiVv0AAAUXSURBVGazg5+Xp83sy37Xc0JUBIGZPWhmlWa2s8/yy83sHTM7YGbfHuw9nHNrnXNfAv4M/M7LesMpFMcGuBYoADqBcq9qDacQHRcHNAEp6Lj09S3gSW+q9EeIvmf2BL9nbgQu8LLeUxEV00fN7GJ6fiEfcc7NCy6LB/YBl9HzS7oZWAHEAz/p8xZfcM5VBl/3JHCLc64xTOV7KhTHJvg47pz7tZk97Zy7IVz1eyVEx6XaORcws3zgv5xznw5X/V4J0XFZCIymJyCrnXN/Dk/13grV94yZXQN8GXjUOfd4uOofTILfBYSCc26NmU3ps3gxcMA5VwxgZk8A1zrnfgL021w1s0lAfbSEAITm2JhZOdARfNrtXbXhE6rPTNBxINmLOsMtRJ+XZUA6MAdoNbPnnHMBL+sOh1B9Zpxzq4HVZvYsoCDw2ASgrNfzcuC8k7zmFuAhzyqKHKd6bJ4B7jWzi4A1Xhbms1M6LmZ2PfARIAf4hbel+eqUjotz7rsAZnYzwVaTp9X561Q/M8uA6+n5w+E5Tys7BdEcBKfMOXe33zVEIudcCz0hKb04556hJySlH865h/2uIdI4514FXvW5jPeJisHiARwGJvZ6XhBcJjo2A9Fx6Z+Oy8Ci4thEcxBsBmaa2VQzSwI+Baz2uaZIoWPTPx2X/um4DCwqjk1UBIGZrQLWA2eaWbmZ3eKc6wK+ArwI7AGedM7t8rNOP+jY9E/HpX86LgOL5mMTFdNHRUTk9EVFi0BERE6fgkBEJMYpCEREYpyCQEQkxikIRERinIJARCTGKQgkKphZU5j3F5J7VgTvaVBvZm+b2V4z+48hvOY6M5sTiv2LgIJApF9mNuh1uJxz54dwd2udc4uAs4GrzOxk16m/jp4re4qEhIJAopaZTTezF8xsa/BOYmcFl19tZhvN7C0zezl4PwHM7Ptm9qiZvQE8Gnz+oJm9ambFZvbVXu/dFPx3WXD908G/6H9vZhZcd2Vw2VYz+7mZDXpdfudcK/A2PVe0xMxuNbPNZrbNzP5gZmlmdj5wDfDTYCti+kA/p8hQKQgkmq0E7nTOnQP8X+BXweWvA0ucc2cDTwDf7PWaOcClzrkVwedn0XOp6cXA3WaW2M9+zga+FnztNOACM0sBfg1cEdx/3smKNbMzgJm8d6nvZ5xz5zrnFtJz+YJbnHPr6LmWzV3OuUXOuaJBfk6RIdFlqCUqmVkGcD7wVPAPdHjv5jEFwP+Y2TggCSjp9dLVwb/MT3jWOdcOtJtZJZDP+29Luck5Vx7c79vAFHruZFXsnDvx3quA2wYo9yIz20ZPCNzjnKsILp9nZj+i534HGfRcz+ZUfk6RIVEQSLSKA+qCfe993UvPrSVXB28U8v1e65r7bNve6//d9P87M5RtBrPWOXeVmU0FNpjZk865t4GHgeucc9uCN3lZ1s9rB/s5RYZEXUMSlZxzDUCJmX0CwHosDK7O5r1rxn/OoxLeAab1urXhJ0/2gmDr4V/pufE7QCZwNNgd1ft+yI3BdSf7OUWGREEg0SIteGngE4+v0/PleUuw22UXcG1w2+/T05WyFaj2ophg99LtwAvB/TQC9UN46f3AxcEA+SdgI/AGsLfXNk8AdwUHu6cz8M8pMiS6DLWIR8wswznXFJxF9Etgv3PuZ37XJdKXWgQi3rk1OHi8i57uqF/7XI9Iv9QiEBGJcWoRiIjEOAWBiEiMUxCIiMQ4BYGISIxTEIiIxLj/D9E4JJln4QtUAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lp8RX4NBzbOH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "outputId": "4521429d-659a-4ce0-bd43-1b2a145d329b"
      },
      "source": [
        "learn.fit_one_cycle(10, lr_max=slice(1e-5, 1e-4))\n",
        "learn.save('stage-2-quick-resnet101')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_k_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.877971</td>\n",
              "      <td>1.634380</td>\n",
              "      <td>0.604136</td>\n",
              "      <td>0.849631</td>\n",
              "      <td>02:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.880052</td>\n",
              "      <td>1.679194</td>\n",
              "      <td>0.592024</td>\n",
              "      <td>0.840177</td>\n",
              "      <td>02:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.898091</td>\n",
              "      <td>1.657956</td>\n",
              "      <td>0.590842</td>\n",
              "      <td>0.841359</td>\n",
              "      <td>02:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.845806</td>\n",
              "      <td>1.629556</td>\n",
              "      <td>0.597341</td>\n",
              "      <td>0.843722</td>\n",
              "      <td>02:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.764003</td>\n",
              "      <td>1.597111</td>\n",
              "      <td>0.610340</td>\n",
              "      <td>0.846381</td>\n",
              "      <td>02:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.678277</td>\n",
              "      <td>1.567661</td>\n",
              "      <td>0.614180</td>\n",
              "      <td>0.857607</td>\n",
              "      <td>02:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.600276</td>\n",
              "      <td>1.553793</td>\n",
              "      <td>0.623929</td>\n",
              "      <td>0.857607</td>\n",
              "      <td>02:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.567100</td>\n",
              "      <td>1.534099</td>\n",
              "      <td>0.625111</td>\n",
              "      <td>0.859084</td>\n",
              "      <td>02:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.535078</td>\n",
              "      <td>1.529610</td>\n",
              "      <td>0.626292</td>\n",
              "      <td>0.861448</td>\n",
              "      <td>02:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.517469</td>\n",
              "      <td>1.524259</td>\n",
              "      <td>0.627179</td>\n",
              "      <td>0.862334</td>\n",
              "      <td>02:01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/content/drive/My Drive/Houseplant Classifier/Models/stage-2-quick-resnet101.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iBaiOjTcKrsx"
      },
      "source": [
        "### Quick Resnet152"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195,
          "referenced_widgets": [
            "03cbd3e6a80e4148a587a7bf31fc38a3",
            "cf82659d21fa454b8718ee5767bb7f06",
            "f62309a4a1074c65a9f07cf9e7634e61",
            "83b56480e5024503beaece173711484e",
            "b55ebc1ba6454ec4b3793917dc9858ff",
            "ef9cffefef7e44d3a17b57980bbd6dac",
            "89bc50f07b0241b7ad4831c34e41c682",
            "3d589543802b4dd4ac348155b2318fe0"
          ]
        },
        "id": "7QUhc616Krsx",
        "outputId": "152a56db-c27c-4737-a326-10df0f695a36"
      },
      "source": [
        "learn = create_cnn_learner(pct_images=0.25, \n",
        "                           deterministic=True, \n",
        "                           size=224, \n",
        "                           img_path ='/content/plant_images_database_manualcleanedlosses10/', \n",
        "                           split_path = '/content/plant_image_database/',\n",
        "                           log_name = 'quickresnet101.csv', \n",
        "                           arch='resnet152')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random state set:101, cuda used: True\n",
            "Split Path defined, preparing pseudo stratified split.\n",
            "Creating path:label dataframe from /content/plant_image_database/, using 100.00% of the images (77700 images.) Shuffle = False.\n",
            "Preparing stratified 5 fold split, random state is 101. Returning fold 0.\n",
            "Creating path:label dataframe from /content/plant_images_database_manualcleanedlosses10/, using 25.00% of the images (16940 images.) Shuffle = False.\n",
            "We have 517 classes, with a training set of size 13555, and a validation set of size 3385\n",
            "The main split has 80.00% training data with 518 classes, the reduced split has 80.02 % training data, with 517 classes.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet152-b121ed2d.pth\" to /root/.cache/torch/hub/checkpoints/resnet152-b121ed2d.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "03cbd3e6a80e4148a587a7bf31fc38a3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=241530880.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "FPVNQZWGKrsy",
        "outputId": "de88488c-aa66-403b-f368-eb5ad72db2e1"
      },
      "source": [
        "print(f'Freezing model body')\n",
        "learn.freeze()\n",
        "enumerate_params(learn)\n",
        "learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Freezing model body\n",
            "60,514,880 total parameters.\n",
            "2,522,496 training parameters.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=0.0033113110810518267, lr_steep=0.005248074419796467)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "Ibe2bR5yKrsy",
        "outputId": "f889bcf7-5505-4c63-bd17-2a484c2432ec"
      },
      "source": [
        "learn.fit_one_cycle(10, 1e-02)\n",
        "learn.save('stage-1-quick-resnet152')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_k_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>4.799128</td>\n",
              "      <td>3.724181</td>\n",
              "      <td>0.238996</td>\n",
              "      <td>0.471196</td>\n",
              "      <td>02:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>4.120324</td>\n",
              "      <td>4.810383</td>\n",
              "      <td>0.154210</td>\n",
              "      <td>0.352733</td>\n",
              "      <td>02:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>3.285042</td>\n",
              "      <td>3.348911</td>\n",
              "      <td>0.275923</td>\n",
              "      <td>0.552142</td>\n",
              "      <td>02:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.824930</td>\n",
              "      <td>2.889234</td>\n",
              "      <td>0.330871</td>\n",
              "      <td>0.629542</td>\n",
              "      <td>02:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.475958</td>\n",
              "      <td>2.580218</td>\n",
              "      <td>0.405318</td>\n",
              "      <td>0.680650</td>\n",
              "      <td>02:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.041909</td>\n",
              "      <td>2.134550</td>\n",
              "      <td>0.489808</td>\n",
              "      <td>0.768390</td>\n",
              "      <td>02:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.648453</td>\n",
              "      <td>1.889895</td>\n",
              "      <td>0.536484</td>\n",
              "      <td>0.807681</td>\n",
              "      <td>02:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.208896</td>\n",
              "      <td>1.703043</td>\n",
              "      <td>0.581684</td>\n",
              "      <td>0.838109</td>\n",
              "      <td>02:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.984141</td>\n",
              "      <td>1.624222</td>\n",
              "      <td>0.603250</td>\n",
              "      <td>0.849335</td>\n",
              "      <td>02:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.829259</td>\n",
              "      <td>1.608792</td>\n",
              "      <td>0.603840</td>\n",
              "      <td>0.853471</td>\n",
              "      <td>02:08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/content/drive/My Drive/Houseplant Classifier/Models/stage-1-quick-resnet152.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "YK3OcYd7Krsy",
        "outputId": "bb3ddfaf-0714-4b4a-85c1-a13a7af949cb"
      },
      "source": [
        "print(f'Unfreezing model body')\n",
        "learn.unfreeze()\n",
        "enumerate_params(learn)\n",
        "learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unfreezing model body\n",
            "60,514,880 total parameters.\n",
            "60,514,880 training parameters.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=6.309573450380412e-08, lr_steep=7.585775847473997e-07)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "r9BxoVs8Krsy",
        "outputId": "50db8e43-2d43-454a-f2bc-3da8f4c17584"
      },
      "source": [
        "learn.fit_one_cycle(10, lr_max=slice(1e-5, 1e-4))\n",
        "learn.save('stage-2-quick-resnet152')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_k_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.844308</td>\n",
              "      <td>1.611323</td>\n",
              "      <td>0.605022</td>\n",
              "      <td>0.849335</td>\n",
              "      <td>02:45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.904222</td>\n",
              "      <td>1.672938</td>\n",
              "      <td>0.586706</td>\n",
              "      <td>0.841654</td>\n",
              "      <td>02:44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.902256</td>\n",
              "      <td>1.676822</td>\n",
              "      <td>0.580207</td>\n",
              "      <td>0.839586</td>\n",
              "      <td>02:44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.853559</td>\n",
              "      <td>1.630254</td>\n",
              "      <td>0.599705</td>\n",
              "      <td>0.848744</td>\n",
              "      <td>02:44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.753349</td>\n",
              "      <td>1.596496</td>\n",
              "      <td>0.607386</td>\n",
              "      <td>0.853176</td>\n",
              "      <td>02:44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.668108</td>\n",
              "      <td>1.558501</td>\n",
              "      <td>0.611226</td>\n",
              "      <td>0.855539</td>\n",
              "      <td>02:44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.598716</td>\n",
              "      <td>1.545720</td>\n",
              "      <td>0.618907</td>\n",
              "      <td>0.859380</td>\n",
              "      <td>02:44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.536063</td>\n",
              "      <td>1.528448</td>\n",
              "      <td>0.616544</td>\n",
              "      <td>0.861448</td>\n",
              "      <td>02:44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.501012</td>\n",
              "      <td>1.523646</td>\n",
              "      <td>0.615066</td>\n",
              "      <td>0.863811</td>\n",
              "      <td>02:44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.495243</td>\n",
              "      <td>1.517921</td>\n",
              "      <td>0.618021</td>\n",
              "      <td>0.864402</td>\n",
              "      <td>02:44</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/content/drive/My Drive/Houseplant Classifier/Models/stage-2-quick-resnet152.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DTGRdpBWTItJ"
      },
      "source": [
        "### Quick Resnet34 - 448px"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zAEkPsNeTIDT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2ec96c9-c776-4bb0-fc70-70bc26dc3e27"
      },
      "source": [
        "learn = create_cnn_learner(pct_images=0.25, \n",
        "                           deterministic=True, \n",
        "                           size=448, \n",
        "                           img_path ='/content/plant_images_database_manualcleanedlosses10/', \n",
        "                           split_path = '/content/plant_image_database/',\n",
        "                           log_name = 'quickbaseline_448px.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random state set:101, cuda used: True\n",
            "Split Path defined, preparing pseudo stratified split.\n",
            "Creating path:label dataframe from /content/plant_image_database/, using 100.00% of the images (77700 images.) Shuffle = False.\n",
            "Preparing stratified 5 fold split, random state is 101. Returning fold 0.\n",
            "Creating path:label dataframe from /content/plant_images_database_manualcleanedlosses10/, using 25.00% of the images (16940 images.) Shuffle = False.\n",
            "We have 517 classes, with a training set of size 13555, and a validation set of size 3385\n",
            "The main split has 80.00% training data with 518 classes, the reduced split has 80.02 % training data, with 517 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-YDawdSTSCQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "bcdf00cc-90f4-4b27-9c92-a1317ca6b5b2"
      },
      "source": [
        "print(f'Freezing model body')\n",
        "learn.freeze()\n",
        "enumerate_params(learn)\n",
        "learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Freezing model body\n",
            "22,076,736 total parameters.\n",
            "809,088 training parameters.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=0.005754399299621582, lr_steep=0.010964781977236271)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WurbKgJQTRo4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "outputId": "6c75e02a-b0e6-4b98-9518-b7d350a4ed3a"
      },
      "source": [
        "learn.fit_one_cycle(10, 1e-02)\n",
        "learn.save('stage-1-quickbaseline-448px')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_k_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>5.292181</td>\n",
              "      <td>3.751250</td>\n",
              "      <td>0.225997</td>\n",
              "      <td>0.464402</td>\n",
              "      <td>01:52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>4.009425</td>\n",
              "      <td>3.554969</td>\n",
              "      <td>0.259970</td>\n",
              "      <td>0.502511</td>\n",
              "      <td>01:53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>3.201278</td>\n",
              "      <td>2.923901</td>\n",
              "      <td>0.357459</td>\n",
              "      <td>0.622747</td>\n",
              "      <td>01:53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.787172</td>\n",
              "      <td>2.604293</td>\n",
              "      <td>0.393796</td>\n",
              "      <td>0.683013</td>\n",
              "      <td>01:52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.404189</td>\n",
              "      <td>2.474571</td>\n",
              "      <td>0.437223</td>\n",
              "      <td>0.708124</td>\n",
              "      <td>01:52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.048426</td>\n",
              "      <td>2.079883</td>\n",
              "      <td>0.512851</td>\n",
              "      <td>0.773412</td>\n",
              "      <td>01:53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.687058</td>\n",
              "      <td>1.831584</td>\n",
              "      <td>0.552437</td>\n",
              "      <td>0.819498</td>\n",
              "      <td>01:53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.320713</td>\n",
              "      <td>1.683838</td>\n",
              "      <td>0.599409</td>\n",
              "      <td>0.840177</td>\n",
              "      <td>01:53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.109586</td>\n",
              "      <td>1.616789</td>\n",
              "      <td>0.607681</td>\n",
              "      <td>0.850517</td>\n",
              "      <td>01:52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.950999</td>\n",
              "      <td>1.608150</td>\n",
              "      <td>0.610340</td>\n",
              "      <td>0.854653</td>\n",
              "      <td>01:53</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/content/drive/My Drive/Houseplant Classifier/Models/stage-1-quickbaseline-448px.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ndaXPJ8gTRsp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "7c495096-3757-4270-d9dc-2042cc498707"
      },
      "source": [
        "print(f'Unfreezing model body')\n",
        "learn.unfreeze()\n",
        "enumerate_params(learn)\n",
        "learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unfreezing model body\n",
            "22,076,736 total parameters.\n",
            "22,076,736 training parameters.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=1.318256749982538e-07, lr_steep=7.585775847473997e-07)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tpRts6lBTRlV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "outputId": "099a5cd7-dadd-48f5-ec54-582021fedd29"
      },
      "source": [
        "learn.fit_one_cycle(10, lr_max=slice(1e-5, 1e-4))\n",
        "learn.save('stage-2-quickbaseline-448px')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_k_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.985053</td>\n",
              "      <td>1.607244</td>\n",
              "      <td>0.613294</td>\n",
              "      <td>0.850812</td>\n",
              "      <td>02:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.993009</td>\n",
              "      <td>1.627172</td>\n",
              "      <td>0.613885</td>\n",
              "      <td>0.848744</td>\n",
              "      <td>02:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.008064</td>\n",
              "      <td>1.608149</td>\n",
              "      <td>0.608863</td>\n",
              "      <td>0.849926</td>\n",
              "      <td>02:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.921860</td>\n",
              "      <td>1.585556</td>\n",
              "      <td>0.615657</td>\n",
              "      <td>0.852880</td>\n",
              "      <td>02:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.839203</td>\n",
              "      <td>1.562396</td>\n",
              "      <td>0.624225</td>\n",
              "      <td>0.857016</td>\n",
              "      <td>02:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.748415</td>\n",
              "      <td>1.533355</td>\n",
              "      <td>0.626292</td>\n",
              "      <td>0.861743</td>\n",
              "      <td>02:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.743582</td>\n",
              "      <td>1.536822</td>\n",
              "      <td>0.631019</td>\n",
              "      <td>0.859084</td>\n",
              "      <td>02:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.665574</td>\n",
              "      <td>1.513259</td>\n",
              "      <td>0.632792</td>\n",
              "      <td>0.862334</td>\n",
              "      <td>02:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.658676</td>\n",
              "      <td>1.510013</td>\n",
              "      <td>0.635746</td>\n",
              "      <td>0.864993</td>\n",
              "      <td>02:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.638493</td>\n",
              "      <td>1.507260</td>\n",
              "      <td>0.636337</td>\n",
              "      <td>0.862925</td>\n",
              "      <td>02:31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "display_data",
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/content/drive/My Drive/Houseplant Classifier/Models/stage-2-quickbaseline-448px.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N7PKlLiMiful"
      },
      "source": [
        "## Quick XResNet\n",
        "\n",
        "> Poor performance, likely due to quality of pretrained weights."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hu4YVfq0d3Pu"
      },
      "source": [
        "# Import the xresnet models\n",
        "from fastai.vision.models.xresnet import *"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vAXMLSLqKL3b"
      },
      "source": [
        "### Quick XResNet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PzqbcpLUKQSm"
      },
      "source": [
        "\n",
        "* XResNet - Based on the Bag of Tricks for Resnet paper\n",
        "* Mish - A new activation function that has shown fantastic results\n",
        "* Ranger - A new optimizer function based on two seperate papers:\n",
        "  * On the Varience of the Adaptive Learning rate and Beyond, RAdam\n",
        "  * Lookahead Optimizer: k steps forward, 1 step back\n",
        "* Self-Attention - Bringing in ideas from GAN's into image classification \n",
        "* Flatten + Anneal Scheduling - Mikhail Grankin\n",
        "* Label Smoothing Cross Entropy - A threshold base (were you close) rather than yes or no"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XFqYXBPgjxxm"
      },
      "source": [
        "def xresnet50_custom(pretrained=False, sa=False, n_out=517):\n",
        "  model = xresnet50(sa=sa, n_out=n_out, pretrained=pretrained)\n",
        "  return model"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xvddYCMgBDHn",
        "outputId": "d5cf0247-1daf-44b7-e669-c7aefc7ccd7e"
      },
      "source": [
        "learn = create_cnn_learner(pct_images=0.25,\n",
        "                           deterministic=True,  \n",
        "                           arch='xresnet50_custom', \n",
        "                           size=224, \n",
        "                           img_path ='/content/plant_images_database_manualcleanedlosses10/', \n",
        "                           split_path = '/content/plant_image_database/',\n",
        "                           log_name = 'xresnet50.csv',\n",
        "                           bs=64,\n",
        "                           pretrained=True\n",
        "                           )"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random state set:101, cuda used: True\n",
            "Split Path defined, preparing pseudo stratified split.\n",
            "Creating path:label dataframe from /content/plant_image_database/, using 100.00% of the images (77700 images.) Shuffle = False.\n",
            "Preparing stratified 5 fold split, random state is 101. Returning fold 0.\n",
            "Creating path:label dataframe from /content/plant_images_database_manualcleanedlosses10/, using 25.00% of the images (16940 images.) Shuffle = False.\n",
            "We have 517 classes, with a training set of size 13555, and a validation set of size 3385\n",
            "The main split has 80.00% training data with 518 classes, the reduced split has 80.02 % training data, with 517 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OgRMsHLBN-ZA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202,
          "referenced_widgets": [
            "0d86ac0e6e8d4d029e13d523ea62ed5c",
            "8894a65d606d4a1ca3f0735354277624",
            "7fe6b6c73c754265a880989bc9356caa",
            "b4979beb46284f2bbe6c7a23a7273bfc",
            "01a39524b98e4c1e9f67df363be6e82d",
            "cc249b0f03fa44b3af25bd24bcc05a50",
            "1a384fd2a7bc452fa58c143fe2fcfb01",
            "0388764fedd348f397064bd6ea28a877"
          ]
        },
        "outputId": "39434557-0b97-48b2-e571-fd770bb215f2"
      },
      "source": [
        "learn = create_cnn_learner(pct_images=0.25,\n",
        "                           deterministic=True,  \n",
        "                           arch='xresnet50', \n",
        "                           size=224, \n",
        "                           img_path ='/content/plant_images_database_manualcleanedlosses10/', \n",
        "                           split_path = '/content/plant_image_database/',\n",
        "                           log_name = 'xresnet50.csv',\n",
        "                           bs=64,\n",
        "                           pretrained=True\n",
        "                           )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random state set:101, cuda used: True\n",
            "Split Path defined, preparing pseudo stratified split.\n",
            "Creating path:label dataframe from /content/plant_image_database/, using 100.00% of the images (77700 images.) Shuffle = False.\n",
            "Preparing stratified 5 fold split, random state is 101. Returning fold 0.\n",
            "Creating path:label dataframe from /content/plant_images_database_manualcleanedlosses10/, using 25.00% of the images (16940 images.) Shuffle = False.\n",
            "We have 517 classes, with a training set of size 13555, and a validation set of size 3385\n",
            "The main split has 80.00% training data with 518 classes, the reduced split has 80.02 % training data, with 517 classes.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://s3.amazonaws.com/fast-ai-modelzoo/xrn50_940.pth\" to /root/.cache/torch/hub/checkpoints/xrn50_940.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0d86ac0e6e8d4d029e13d523ea62ed5c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=256198016.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cWK4gd8mRHXQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "719721cb-f333-4ecc-86dd-3fe4e8e5b725"
      },
      "source": [
        "print(f'Freezing model body')\n",
        "learn.freeze()\n",
        "enumerate_params(learn)\n",
        "learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Freezing model body\n",
            "25,898,336 total parameters.\n",
            "2,424,320 training parameters.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=0.002290867641568184, lr_steep=0.005248074419796467)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_NYkahCscRDQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "outputId": "28baa4e6-eafd-4979-80d4-2b95faf1cdbe"
      },
      "source": [
        "learn.fit_one_cycle(10, 1e-2)\n",
        "learn.save('stage-1-quick-xresnet50')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_k_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>6.363160</td>\n",
              "      <td>5.429543</td>\n",
              "      <td>0.057016</td>\n",
              "      <td>0.163663</td>\n",
              "      <td>01:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>5.355260</td>\n",
              "      <td>4.755956</td>\n",
              "      <td>0.097784</td>\n",
              "      <td>0.246677</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>4.580175</td>\n",
              "      <td>4.317061</td>\n",
              "      <td>0.142393</td>\n",
              "      <td>0.342984</td>\n",
              "      <td>01:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>4.220543</td>\n",
              "      <td>4.045034</td>\n",
              "      <td>0.174003</td>\n",
              "      <td>0.392910</td>\n",
              "      <td>01:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>3.839763</td>\n",
              "      <td>3.760348</td>\n",
              "      <td>0.203840</td>\n",
              "      <td>0.449631</td>\n",
              "      <td>01:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>3.533624</td>\n",
              "      <td>3.518166</td>\n",
              "      <td>0.253767</td>\n",
              "      <td>0.510487</td>\n",
              "      <td>01:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>3.183973</td>\n",
              "      <td>3.334555</td>\n",
              "      <td>0.281832</td>\n",
              "      <td>0.543279</td>\n",
              "      <td>01:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>2.831918</td>\n",
              "      <td>3.173747</td>\n",
              "      <td>0.316100</td>\n",
              "      <td>0.575480</td>\n",
              "      <td>01:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>2.564847</td>\n",
              "      <td>3.097710</td>\n",
              "      <td>0.327031</td>\n",
              "      <td>0.587592</td>\n",
              "      <td>01:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>2.369013</td>\n",
              "      <td>3.086977</td>\n",
              "      <td>0.326440</td>\n",
              "      <td>0.589069</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/content/drive/My Drive/Houseplant Classifier/Models/stage-1-quick-xresnet50.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nms1x2L6bdla",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "outputId": "6b6a2018-5ac2-4ace-fec0-718cfbe7c1ec"
      },
      "source": [
        "print(f'Unfreezing model body')\n",
        "learn.unfreeze()\n",
        "enumerate_params(learn)\n",
        "learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unfreezing model body\n",
            "25,898,336 total parameters.\n",
            "25,898,336 training parameters.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=1.9054606673307718e-05, lr_steep=1.9054607491852948e-06)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EHAgmkojIkfnl/75LUfkBfnrRbFKTdH2I3tDVNYmvAfebWVb0cRlwRTCRRES6b92uSu79xzY+t3A8x+cNCztOv9HVvZtWAceZ2ZDo40ozuwF4J8hwIiJdEYk4Nz+zmuxByXznrBlhx+lXunV8urtXtjpn0zcDyCMi0m2PvF1IQWE5N597NNnpKWHH6Vd6chIT7VcmIqErqarnZ3/awAmTc/jU3LFhx+l3elISOi2HiITux8+vo64xwo8/NQsz/e3a2zodkzCzKtovAwMGBZJIRKSL7n9jG8+u3MUNZ0xjSu7gsOP0S52WhLvrSBQRiUt/f7eEH/1xHWccPYJrT9O1qoOiE6uLSJ+zubiabzy8gmkjBvNLXas6UCoJEelTymoauGrx26QmJXD3FQsYnNrVw73kSOjTFZE+o66xma8+sJzd5XU8cvUixg3VNSKCppIQkT4hEnH+5YlVvLVtP7d9bi7zJ+oaEbGgzU0i0if87M8beO6d3Xz37Bmcf9yYsOMMGCoJEYl797+xjTv/9h6XL5rIVz86Oew4A4pKQkTi2l/W7uGHS9ZyxtEj+LdPztQBczGmkhCRuLV8exnXPlLA7HHZ3Pa5uSQl6ldWrAX2iZtZmpm9ZWarzGytmf2onXlSzewxM9tsZsvMLC+oPCLSt2wpqeaqxW8zOiuNe69YQHqK9rMJQ5C1XA+c5u7HAXOAs8xsUZt5rgLK3H0q8N/AzwLMIyJ9RHFlHVfc+xZJCcbiLy0kZ3Bq2JEGrMBKwltURx8mR29tzwN1AbA4ev9J4HTTBkeRAa22oYkvLX6b/TUN3Hvl8UzMyQg70oAW6AY+M0uMXgu7GHjJ3Ze1mWUssAPA3ZuACiAnyEwiEr8iEeebj61i3a5Kbv/8XI4dlx12pAEv0JJw92Z3nwOMAxaa2awjeR0zu9rM8s0sv6SkpHdDikjc+PlLG3lx7R5uPncmp80YGXYcIUZ7N7l7OfAKcFabp4qA8QBmlgRkAfvaWf4ud1/g7gtyc3ODjisiIXh6xU7+55UtfG7hBL50Ul7YcSQqyL2bcs0sO3p/EHAmsKHNbEuAK6L3PwO87O66mJHIALN8+36++9RqTpySwy0XHKNjIeJIkPuUjQYWm1kiLWX0uLs/Z2a3APnuvgS4B3jAzDYD+4FLAswjInGoqPwAX31gOWOy0/jNpfNI1rEQcSWwknD3d4C57Uz/Qav7dcDFQWUQkfhW29DElxfnU98Y4dGrjyc7PSXsSNKGjk4RkVBEIs6Nj69i455K7rnyeKaO0OVH45HW60QkFL/66yb+tGYPN519NB+fPiLsONIBlYSIxNzDywr51V838el54/jyKZPCjiOdUEmISEw9U1DEzc+s5uPTc/npRbO1J1OcU0mISMy8uGYPNz6xikWTcvjtZfNJSdKvoHin/0IiEhNLNxZz7SMrOHZcFr+7YgFpyYlhR5IuUEmISOBeWL2br9yfz7QRmdx35UIGp2rHyr5CJSEigXrkrUK+8fAKjhuXzSNXLyIrPTnsSNINqnMRCcxvl27hZy9u4NTpufz20vkMStEmpr5GJSEivS4Scf7jhfXc/dpWLpgzhv+6+DidbqOPUkmISK9qaIrw7SdX8czKXVx5Yh4/OG8mCQnazbWvUkmISK+pqW/iaw8u59VNpXzrn6ZzzalTdBxEH6eSEJFeUVJVz1WL32btrkr+89PH8tnjx4cdSXqBSkJEeuy9kmqu/P3bFFfVcedl8zljpq4q11+oJESkR1YUlnHVfW9jZjx69QnMGa/rUvcnKgkROWJ/WbuHax8pYFRWGou/uJC84RlhR5JeppIQkSPy4Jvb+cGza5g9Lpt7r1hAzuDUsCNJAFQSItIt7s7P//Iut7+ymdNmjOD2z88lPUW/Svor/ZcVkS6rONDILX9cx1MrdnLJ8eP58YWzSNJBcv2aSkJEDqu0up57X9vKA29sp6q+ietPn8YNZ0zTMRADgEpCRDq0cU8VD7y5jSfyd9LQHOHc2aP5+qlTOGZMVtjRJEZUEiLyIY3NEV5YvZuH3izkrW37SUlK4FNzxvLVj01mcu7gsONJjKkkROR9KwrL+N7Tq9mwp4qJOel875wZXDx/PEMzUsKOJiFRSYgIlXWN/OeLG3hoWSEjM9P4zaXzOOuYUToxnwRXEmY2HrgfGAk4cJe7/6rNPKcCzwJbo5OedvdbgsokIh/m7jz3zm5ueW4d+6rrufLEPG78xHRdOU7eF+Q3oQm40d1XmFkmsNzMXnL3dW3me9Xdzwswh4i0Y1tpDd9/dg2vbipl1tgh3HPFAo4dp1NqyIcFVhLuvhvYHb1fZWbrgbFA25IQkRjaV13PPa9t5e7XtpKSmMAPPzmTy0/II1GblqQdMVmnNLM8YC6wrJ2nTzCzVcAu4F/cfW0sMokMNLsrDnDX39/jkbcKqW+KcN6xY7j5nKMZlZUWdjSJY4GXhJkNBp4CbnD3yjZPrwAmunu1mZ0DPANMa+c1rgauBpgwYULAiUX6j13lB/jr+r38Zd1e3tiyDwcunDOWr586makjMsOOJ32AuXtwL26WDDwH/Nndf9GF+bcBC9y9tKN5FixY4Pn5+b0XUqQf2VNRx9vb9pO/bT/Ltu5nw54qACYNz+ATM0dy2aKJjB+WHnJKCYOZLXf3Bd1dLsi9mwy4B1jfUUGY2Shgr7u7mS0EEoB9QWUS6S+amiO8V1rDul2VrN9dyfo9VazfXUlJVT0A6SmJzJswlO+cNZYzZ45k6ggdBCdHJsjNTScBlwOrzWxldNr3gAkA7n4H8Bng62bWBBwALvEgV22kRxqbIxRX1bOn4gB7K+sZlJLIyMw0Rg5JZWh6yiH71Nc1NrN8exkFhWXkDc/gxCnDGaaDsrqttqGJDXuqWLerknW7K1m7q5INuyupb4oAkJKYwLSRg/notFyOGTOE4/OGcfToTJ14T3pFkHs3vQZ0uruEu98O3B5UBum6hqYIO8tq2b6/lp37ayncX8vOsgPsq25gX009ZbWNlNU20FGFJycao7MGMSY7jTHZgyipquetrfvf/0V20MzRQ5g5ZggHGpupqmuiuq6RpIQEstKTyRqUzPDBqZx37GhmjR0Y5waqa2xm1Y5ytpTUUFxVR3FVPSVV9eyvaaCspoH9tQ2U1za+P3/WoGSOHp3JZYsmcsyYIRwzJovJuRkkqxAkIIGOSQShL49JNDVHaIo4acmJHc4TiTh1Tc3UN0YYnJbUrf/5y2oaWLZ1H4X7azl71uhDtj27O+t3V/FeaTU7yw6ws6yWwv0H2FZaQ1H5AZojH3wXUpMSGDd0EMMHp5IzOIVhGSnkZKQyOiuNUVlpjMhM40BjMyVVdeytrGd3RR27Kw6wq/wARWUHyExL5qSpwzl5Wg7zJw5jc3E1r28u5R9bStlaWkNGahKZaclkpibR2Byh4kAjFQcaKa2up7HZmTM+m8sXTeS0GSOIuNPY7DQ2R2iOOM3uRCJOxCExARITEkg0IzU5gfSURNJTkuJqd87q+iZ27K+luKqe/TX17KtuoKj8AAWF5azdVUFj8wef+7CMFHKjn/nQjBSGpaeQm5nKjFGZHDM2izFZaTrzqhyRIx2TUEn00DMFRfx1QzFTcjOYPjKT6aMyycvJ+NCml7rGZh55q5DfLt3CvpoGjhqZyZzxWcwcPYTS6gbe3VvFxr1V7Cw7QEOrv7yTE40puYOZPiqTqbmDSUtOxAzMjEjEqWloorahmaq6RlbuqGDDnsr3/9I3g49PH8HlJ0xkaHoKz7+zixdW76Go/MD7r5+dnsy4oYPIy8lg0vAMJuZkkJeTzoRh6QwfnBrKKRkq6xp5evlOHnhzO1tKao74ddKSExg5JI2x2YMYkz2IIWnJlNU2sC/6F3puZirzJw5l3oShHDc+q1cvmrOvup4H3yzk5Y3F7Nhfy/6ahnbzHTs2m/l5Q5k/YSgzxwwhNzNVawQSGJVED5VW15OTkdLlv9LcnV/+7yZ+9ddNDMtI+dCmmMzUJOZMyGbu+GwyUpO49x9b2VtZz8JJw1gwcSiriypYtaOcyromzCAvJ4OjRg4mLyeDQSmJpCUnkpKYQHFVfUuB7Kn60C/31tKSE8hISWL6qExOmJzDCVNyGDkkjSfyd/DwWzsorW4ZyExONE6ZlsvZs0Yxe1wWY7MHkZmW3CufXRDcnTfe28f63VUkJxrJiQkkJRhJiUaCtdzMoDniRNxpanYamiPU1jdT29BMdX0juyvqWtZsyg9QVdcUXRtq+Qt9Z9kBNhdXv/9+KYkJpCUnkJacSHZ6MuOHpjN+WDrjhg56f81pRGYqI4ekMSjl0DVBd2fj3ioWv76Np1YU0dAUYWHeMKaMGMyEYemMHzaIUUPSyImuJWSmJmmNQGJKJdEDxVV1nPKzV/j+eTO5bNHEw87f1Bzh5j+s4bH8HXxm/jh+etFsmpqdzcXVrN9Tyaod5RQUlrNhTyURh4V5w7jhzGmcOGX4+6/h7uyqqCMnI6XTzU8H1TU20xT9hegRSEjgsJtVGpoivLRuL/VNzZw+YyRZ6fFbCmEor22gYEc5a4sqqK5vpq6x5bavpoEd0TGZ6vqmQ5YbP2wQ00cOYfqowTQ1O2t2VbCmqJKKA42kJiVw0bxxXHXyJO1RJHFFJdEDTy3fyY1PrOK48dk8+42TOpyvsTlCQWE5v1m6maUbS7j2tKl888yjOvyLsKa+ieKqevJy0vVXYx/k7pTXNrK3qo6S6IByUdkBNkbX7t4rrSHRjOmjMpk1dgizxmZx1jGjyBmcGnZ0kUPE3XESfclrm1uO3Vu1o5ytpTVMGp7xoeeXbizmwTcLeWNLKTUNzSQnGj++cNZh1zoyUpOYpLNp9llmxtDo5qkZow59vr6pmQQzjSNIvzbgf4O5O69tLmXhpGG8vW0/S1bu4vozPjgzSGl1PV97cDnZg1K4cO5YTpmWywlTcsgapE03A11q0uE3E4r0dQO+JDburaKkqp5vfWI6iWY8u6qI606f+v7mod//Yyv1TREe/PJHtI1ZRAacAb+e/Nqmlk1NJ08bzgVzxvBeSQ1rd7Wch7CyrpH7X9/O2bNGqSBEZEAa8CXx6qZSJudmMCZ7EGfPGk1yovHsyiIAHnhjO1X1TVxz6tSQU4qIhGNAl0R9UzPLtu7jlKktu6ZmpSdz6vQRLFm1i5r6Ju59bSsfOyp3wJwiQkSkrQFdEsu3l1HXGOHkabnvT7tgzhj2VtZz4+Or2FfTwDc+rrUIERm4BnRJvLaplMQEY9HkYe9PO33GSDJSEnlx7R6OzxvKwknDOnkFEZH+bWCXxOZS5o7P/tDpKQalJPJPs1p2ir9GaxEiMsAN2F1gy2oaWF1UwfWnH3K1VK47bRozRmVy6lG57SwpIjJwDNiSeH3LPtzhlGnDD3kub3gGV390SgipRETiy4Dd3PTa5hIyU5M4blx22FFEROLWgC2JFdvLmZ83VJd4FBHpxID8DXmgoZlNxVUcq+MfREQ6NSBLYt3uCiKODpITETmMAVkSq3dWADB7nEpCRKQzA7MkiioZPjiVUUPSwo4iIhLXBmRJrCmqYPbYIbpanIjIYQy4kqhtaGJTcRWzNR4hInJYgZWEmY03s1fMbJ2ZrTWz69uZx8zsNjPbbGbvmNm8oPIctH53pQatRUS6KMgjrpuAG919hZllAsvN7CV3X9dqnrOBadHbR4DfRv8NjAatRUS6LrA1CXff7e4rovergPXA2DazXQDc7y3eBLLNbHRQmUCD1iIi3RGTMQkzywPmAsvaPDUW2NHq8U4OLZJetbqoXIPWIiJdFHhJmNlg4CngBnevPMLXuNrM8s0sv6Sk5Iiz1DY0sbm4WoPWIiJdFGhJmFkyLQXxkLs/3c4sRcD4Vo/HRad9iLvf5e4L3H1Bbu6Rn75bg9YiIt0T5N5NBtwDrHf3X3Qw2xLgC9G9nBYBFe6+O6hMGrQWEemeIPduOgm4HFhtZiuj074HTABw9zuAF4BzgM1ALfDFAPPwTlGFBq1FRLohsJJw99eATkeH3d2BbwSVoS0daS0i0j0D5ohrDVqLiHTfgCkJDVqLiHTfgCmJsppGRg5J1aC1iEg3BDlwHVfOmDmSM2aODDuGiEifMmDWJANnCsQAAAdDSURBVEREpPtUEiIi0iGVhIiIdEglISIiHVJJiIhIh1QSIiLSIZWEiIh0SCUhIiIdspZz7PUdZlYCbAeygIro5MPdP/jvcKD0CN629Wt25/m20zt73DZr62lHkjuWmVvfD+OzPty0eMzc3vT++v3orcyd5epO5o5ydnS/v3ynJ7p79y/I4+598gbc1dX7rf7N7+l7def5ttM7e9w2a09zxzJz2J/14abFY+aB9P3orcyHy93VzH3h+xF25ta3vry56Y/duN96Wk/fqzvPt53e2eP2svYkdywzt74fxmd9uGnxmLm96f31+9FbmQ+3fFczt50Wj9+PsDO/r89tbuoJM8t39wVh5+iuvphbmWOnL+ZW5tjojcx9eU3iSNwVdoAj1BdzK3Ps9MXcyhwbPc48oNYkRESkewbamoSIiHSDSkJERDqkkhARkQ6pJKLM7BQzu8PM7jaz18PO0xVmlmBmPzGzX5vZFWHn6SozO9XMXo1+3qeGnaerzCzDzPLN7Lyws3SFmR0d/YyfNLOvh52nq8zsQjP7nZk9ZmafCDtPV5jZZDO7x8yeDDtLZ6Lf4cXRz/fSrizTL0rCzO41s2IzW9Nm+llmttHMNpvZdzt7DXd/1d2/BjwHLA4ybzRbjzMDFwDjgEZgZ1BZW+ul3A5UA2nEIHcvZQb4DvB4MCk/rJe+0+uj3+nPAicFmbdVvt7I/Yy7fwX4GvB/gswbzdYbmd9z96uCTdq+bua/CHgy+vme36U36OnRePFwAz4KzAPWtJqWCGwBJgMpwCpgJjCbliJofRvRarnHgcy+kBn4LvDV6LJP9pXPGkiILjcSeKiPZD4TuAS4EjivL2SOLnM+8Cfg833l+9FquZ8D8/pY5pj8f9iD/DcBc6LzPNyV10+iH3D3v5tZXpvJC4HN7v4egJk9Clzg7j8F2t1cYGYTgAp3rwowLtA7mc1sJ9AQfdgcXNoP9NZnHVUGpAaRs7Ve+qxPBTJo+R/tgJm94O6ReM4cfZ0lwBIzex54OKi8rd6vNz5rA24F/uTuK4JN3Ovf6ZjrTn5a1tzHASvp4pakflESHRgL7Gj1eCfwkcMscxXw+8ASHV53Mz8N/NrMTgH+HmSww+hWbjO7CPgnIBu4PdhoHepWZne/GcDMrgRKgyyITnT3cz6Vls0LqcALgSbrXHe/19cCZwBZZjbV3e8IMlwHuvtZ5wA/Aeaa2U3RMglTR/lvA243s3Pp4qk7+nNJdJu7/1vYGbrD3WtpKbY+xd2fpqXg+hx3vy/sDF3l7kuBpSHH6DZ3v42WX2Z9hrvvo2UMJa65ew3wxe4s0y8GrjtQBIxv9XhcdFo864uZoW/mVubY6Yu5+2Lm1notf38uibeBaWY2ycxSaBl0XBJypsPpi5mhb+ZW5tjpi7n7YubWei9/rEfiAxrdfwTYzQe7gl4VnX4O8C4to/w3h52zr2fuq7mVWbn7W+ZY5tcJ/kREpEP9eXOTiIj0kEpCREQ6pJIQEZEOqSRERKRDKgkREemQSkJERDqkkpB+wcyqY/x+vXLNEWu5tkaFma00sw1m9l9dWOZCM5vZG+8vcjgqCZF2mFmn5zVz9xN78e1edfc5wFzgPDM73LUfLqTlbLQigVNJSL9lZlPM7EUzW24tV8KbEZ3+STNbZmYFZva/ZjYyOv2HZvaAmf0DeCD6+F4zW2pm75nZda1euzr676nR55+Mrgk8FD3VNWZ2TnTacjO7zcye6yyvux+g5RTOY6PLf8XM3jazVWb2lJmlm9mJtFwj4v9F1z6mdPRzivQGlYT0Z3cB17r7fOBfgN9Ep78GLHL3ucCjwLdbLTMTOMPdPxd9PIOW05ovBP7NzJLbeZ+5wA3RZScDJ5lZGnAncHb0/XMPF9bMhgLT+OC070+7+/HufhywnpbTLbxOyzl4vuXuc9x9Syc/p0iP6VTh0i+Z2WDgROCJ6B/28MEFjsYBj5nZaFqu2rW11aJLon/RH/S8u9cD9WZWTMvV9NpecvUtd98Zfd+VQB4tl2d9z90PvvYjwNUdxD3FzFbRUhC/dPc90emzzOzHtFx3YzDw527+nCI9ppKQ/ioBKI9u62/r18Av3H1J9MI8P2z1XE2beetb3W+m/f9nujJPZ1519/PMbBLwppk97u4rgfuAC919VfRiR6e2s2xnP6dIj2lzk/RL7l4JbDWzi6Hlkphmdlz06Sw+OLf+FQFF2AhMbnVZyf9zuAWiax23At+JTsoEdkc3cV3aataq6HOH+zlFekwlIf1FupntbHX7Ji2/WK+KbspZS8s1fqFlzeEJM1sOlAYRJrrJ6hrgxej7VAEVXVj0DuCj0XL5PrAM+AewodU8jwLfig68T6Hjn1Okx3SqcJGAmNlgd6+O7u30P8Amd//vsHOJdIfWJESC85XoQPZaWjZx3RlyHpFu05qEiIh0SGsSIiLSIZWEiIh0SCUhIiIdUkmIiEiHVBIiItIhlYSIiHTo/wMEwaUsUE7+ngAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d_MwHFy9bIlV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "outputId": "a0fbdb69-2a37-42a3-e023-d91d83424575"
      },
      "source": [
        "learn.fit_one_cycle(10, lr_max=slice(1e-5, 1e-4))\n",
        "learn.save('stage-2-quick-xresnet50')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_k_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>2.398705</td>\n",
              "      <td>3.083523</td>\n",
              "      <td>0.330871</td>\n",
              "      <td>0.589956</td>\n",
              "      <td>01:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>2.386346</td>\n",
              "      <td>3.068331</td>\n",
              "      <td>0.332939</td>\n",
              "      <td>0.594387</td>\n",
              "      <td>01:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.330010</td>\n",
              "      <td>3.058271</td>\n",
              "      <td>0.333235</td>\n",
              "      <td>0.595864</td>\n",
              "      <td>01:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.340703</td>\n",
              "      <td>3.040931</td>\n",
              "      <td>0.335303</td>\n",
              "      <td>0.598818</td>\n",
              "      <td>01:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.304447</td>\n",
              "      <td>3.037189</td>\n",
              "      <td>0.341211</td>\n",
              "      <td>0.603545</td>\n",
              "      <td>01:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.269883</td>\n",
              "      <td>3.021683</td>\n",
              "      <td>0.345643</td>\n",
              "      <td>0.601477</td>\n",
              "      <td>01:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>2.229787</td>\n",
              "      <td>3.021473</td>\n",
              "      <td>0.342984</td>\n",
              "      <td>0.606795</td>\n",
              "      <td>01:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>2.241567</td>\n",
              "      <td>3.019022</td>\n",
              "      <td>0.346824</td>\n",
              "      <td>0.606204</td>\n",
              "      <td>01:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>2.242438</td>\n",
              "      <td>3.006408</td>\n",
              "      <td>0.345938</td>\n",
              "      <td>0.607681</td>\n",
              "      <td>01:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>2.258759</td>\n",
              "      <td>3.012908</td>\n",
              "      <td>0.347120</td>\n",
              "      <td>0.607681</td>\n",
              "      <td>01:20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/content/drive/My Drive/Houseplant Classifier/Models/stage-2-quick-xresnet50.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7uVB1fs4wG07"
      },
      "source": [
        "### SOTA xResnet\n",
        "\n",
        "* XResNet - Based on the Bag of Tricks for Resnet paper"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sVC5ff-my7kP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c9069f0-faea-4621-f608-b98f2d1d8efc"
      },
      "source": [
        "printm()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Gen RAM Free: 26.1 GB  | Proc size: 303.2 MB\n",
            "GPU RAM Free: 16120MB | Used: 10MB | Util   0% | Total 16130MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ut-I-h01tNoM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202,
          "referenced_widgets": [
            "f6bdadc35d5b459c83ee1d2bf2b68f33",
            "7de87f5cbf40495cbffcf3c53363011e",
            "176a30623ae143529470a0762936d3bd",
            "e8d4144b806c45b1b1c5d62d92becf80",
            "8c670f0ed1074219b58f736c71a4891e",
            "26c3361ae5f44b76975c7a695f6b53a5",
            "1cb4379ca9e149a3bcb539369fc328b9",
            "aa71f9bc20e745c9a8cc45f1d962355f"
          ]
        },
        "outputId": "d6bd760c-4b7d-43d2-a675-084c43c1034e"
      },
      "source": [
        "learn = create_cnn_learner(pct_images=0.25,\n",
        "                           deterministic=True,  \n",
        "                           arch='xresnet50', \n",
        "                           act_cls=Mish, \n",
        "                           sa=True, \n",
        "                           loss_func=LabelSmoothingCrossEntropy(),\n",
        "                           size=224, \n",
        "                           img_path ='/content/plant_images_database_manualcleanedlosses10/', \n",
        "                           split_path = '/content/plant_image_database/',\n",
        "                           log_name = 'xresnet50_sota.csv',\n",
        "                           bs=56, #60 is too many\n",
        "                           pretrained=True\n",
        "                           )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random state set:101, cuda used: True\n",
            "Split Path defined, preparing pseudo stratified split.\n",
            "Creating path:label dataframe from /content/plant_image_database/, using 100.00% of the images (77700 images.) Shuffle = False.\n",
            "Preparing stratified 5 fold split, random state is 101. Returning fold 0.\n",
            "Creating path:label dataframe from /content/plant_images_database_manualcleanedlosses10/, using 25.00% of the images (16940 images.) Shuffle = False.\n",
            "We have 517 classes, with a training set of size 13555, and a validation set of size 3385\n",
            "The main split has 80.00% training data with 518 classes, the reduced split has 80.02 % training data, with 517 classes.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://s3.amazonaws.com/fast-ai-modelzoo/xrn50_940.pth\" to /root/.cache/torch/hub/checkpoints/xrn50_940.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f6bdadc35d5b459c83ee1d2bf2b68f33",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=256198016.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_QeMsMuLzB8u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "95174aa2-560f-4c1e-a786-2bbe2a8a12a0"
      },
      "source": [
        "print(f'Freezing model body')\n",
        "learn.freeze()\n",
        "enumerate_params(learn)\n",
        "learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Freezing model body\n",
            "25,963,873 total parameters.\n",
            "2,424,320 training parameters.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=0.0013182567432522773, lr_steep=2.75422871709452e-06)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pQSVEFzJzDFS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c97a6f7-230b-4c64-f450-87eb4e751065"
      },
      "source": [
        "learn.fit_one_cycle(10, 1e-2)\n",
        "learn.save('stage-1-quick-xresnet50-sota')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_k_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>6.737947</td>\n",
              "      <td>5.911587</td>\n",
              "      <td>0.038996</td>\n",
              "      <td>0.123191</td>\n",
              "      <td>01:17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>5.634314</td>\n",
              "      <td>5.161563</td>\n",
              "      <td>0.079764</td>\n",
              "      <td>0.225111</td>\n",
              "      <td>01:17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>4.961504</td>\n",
              "      <td>4.744925</td>\n",
              "      <td>0.128804</td>\n",
              "      <td>0.314919</td>\n",
              "      <td>01:17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>4.679482</td>\n",
              "      <td>4.501132</td>\n",
              "      <td>0.167208</td>\n",
              "      <td>0.385524</td>\n",
              "      <td>01:17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>4.381416</td>\n",
              "      <td>4.314567</td>\n",
              "      <td>0.194092</td>\n",
              "      <td>0.442836</td>\n",
              "      <td>01:17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>4.071916</td>\n",
              "      <td>4.087054</td>\n",
              "      <td>0.241654</td>\n",
              "      <td>0.502806</td>\n",
              "      <td>01:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>3.842650</td>\n",
              "      <td>3.866076</td>\n",
              "      <td>0.285967</td>\n",
              "      <td>0.554801</td>\n",
              "      <td>01:17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>3.530564</td>\n",
              "      <td>3.742130</td>\n",
              "      <td>0.319941</td>\n",
              "      <td>0.577253</td>\n",
              "      <td>01:17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>3.334864</td>\n",
              "      <td>3.699803</td>\n",
              "      <td>0.334417</td>\n",
              "      <td>0.593501</td>\n",
              "      <td>01:17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>3.171216</td>\n",
              "      <td>3.692502</td>\n",
              "      <td>0.334417</td>\n",
              "      <td>0.590547</td>\n",
              "      <td>01:17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/content/drive/My Drive/Houseplant Classifier/Models/stage-1-quick-xresnet50-sota.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ozHV4z4zzHd1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4be95eac-18c6-4948-d2a9-ea28006783ee"
      },
      "source": [
        "print(f'Unfreezing model body')\n",
        "learn.unfreeze()\n",
        "enumerate_params(learn)\n",
        "learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unfreezing model body\n",
            "25,963,873 total parameters.\n",
            "25,963,873 training parameters.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=7.585775847473997e-08, lr_steep=6.309573450380412e-07)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QD6R9KtAzIaW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6543bccf-2fe8-448b-d011-6eb0518dda24"
      },
      "source": [
        "learn.fit_one_cycle(10, lr_max=slice(1e-5, 1e-4))\n",
        "learn.save('stage-2-quick-xresnet50-sota')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>top_k_accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>3.217570</td>\n",
              "      <td>3.685616</td>\n",
              "      <td>0.337962</td>\n",
              "      <td>0.590842</td>\n",
              "      <td>01:35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>3.167722</td>\n",
              "      <td>3.676413</td>\n",
              "      <td>0.336189</td>\n",
              "      <td>0.595864</td>\n",
              "      <td>01:35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>3.177233</td>\n",
              "      <td>3.669021</td>\n",
              "      <td>0.335007</td>\n",
              "      <td>0.594682</td>\n",
              "      <td>01:35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>3.135299</td>\n",
              "      <td>3.643317</td>\n",
              "      <td>0.343870</td>\n",
              "      <td>0.600295</td>\n",
              "      <td>01:35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>3.131316</td>\n",
              "      <td>3.642608</td>\n",
              "      <td>0.346529</td>\n",
              "      <td>0.605613</td>\n",
              "      <td>01:35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>3.089329</td>\n",
              "      <td>3.628105</td>\n",
              "      <td>0.355096</td>\n",
              "      <td>0.607976</td>\n",
              "      <td>01:35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>3.066456</td>\n",
              "      <td>3.629627</td>\n",
              "      <td>0.351846</td>\n",
              "      <td>0.606795</td>\n",
              "      <td>01:35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>3.066684</td>\n",
              "      <td>3.625455</td>\n",
              "      <td>0.349778</td>\n",
              "      <td>0.608272</td>\n",
              "      <td>01:35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>3.061377</td>\n",
              "      <td>3.621203</td>\n",
              "      <td>0.349483</td>\n",
              "      <td>0.609453</td>\n",
              "      <td>01:35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>3.101516</td>\n",
              "      <td>3.621573</td>\n",
              "      <td>0.352733</td>\n",
              "      <td>0.608272</td>\n",
              "      <td>01:35</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/content/drive/My Drive/Houseplant Classifier/Models/stage-2-quick-xresnet50-sota.pth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    }
  ]
}