Computer Vision/Image Segmentation/Dominoes/Fine-Tuning - Dominoes - Image Segmentation with LoRA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dominoes: Image Segmentation Fine-Tuning\n",
    "\n",
    "Dataset Source: https://huggingface.co/datasets/adelavega/dominoes_raw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Git LFS initialized.\n"
     ]
    }
   ],
   "source": [
    "import os, sys\n",
    "os.environ['TOKENIZERS_PARALLELISM']='false'\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "import datasets\n",
    "from datasets import load_dataset\n",
    "\n",
    "import transformers\n",
    "from transformers import Trainer, TrainingArguments\n",
    "from transformers import AutoImageProcessor, AutoModelForSemanticSegmentation\n",
    "\n",
    "import torchvision\n",
    "from torchvision.transforms import ColorJitter\n",
    "\n",
    "import evaluate\n",
    "\n",
    "from PIL import Image\n",
    "\n",
    "import peft\n",
    "from peft import LoraConfig, get_peft_model\n",
    "\n",
    "!git lfs install"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display Library Versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Python : 3.9.12\n",
      "           NumPy : 1.25.1\n",
      "           Torch : 2.0.1\n",
      "    Torch Vision : 0.15.2\n",
      "    Transformers : 4.26.1\n",
      "        Datasets : 2.13.1\n",
      "        Evaluate : 0.4.0\n",
      "            PEFT : 0.3.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Python :\".rjust(18), sys.version[0:6])\n",
    "print(\"NumPy :\".rjust(18), np.__version__)\n",
    "print(\"Torch :\".rjust(18), torch.__version__)\n",
    "print(\"Torch Vision :\".rjust(18), torchvision.__version__)\n",
    "print(\"Transformers :\".rjust(18), transformers.__version__)\n",
    "print(\"Datasets :\".rjust(18), datasets.__version__)\n",
    "print(\"Evaluate :\".rjust(18), evaluate.__version__)\n",
    "print(\"PEFT :\".rjust(18), peft.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ingest Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset parquet (/Users/briandunn/.cache/huggingface/datasets/adelavega___parquet/adelavega--dominoes_raw-4d58def0d1b6754d/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "525666fc8f764d40b691da9fadafd951",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['image', 'annotation'],\n",
       "        num_rows: 763\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = load_dataset(\"adelavega/dominoes_raw\")\n",
    "\n",
    "data = data.remove_columns(\n",
    "    [\n",
    "        'uuid', \n",
    "        'status',\n",
    "        'label.annotations',\n",
    "        'name'\n",
    "    ]\n",
    ")\n",
    "\n",
    "data = data.rename_column(\"label.segmentation_bitmap\", \"annotation\")\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "example = data['train'][12]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display Above-Referenced Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "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",
      "image/png": "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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=448x448>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example['image']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split Dataset into Training & Testing Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_test = data['train'].train_test_split(test_size=0.10)\n",
    "\n",
    "train_ds = train_test['train']\n",
    "test_ds = train_test['test']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Conversions Between String & Integer Values For Label Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of Unique Label Values: ['unlabeled', 'domino']\n",
      "Number of Unique Label Values: 2\n",
      "label2id dictionary values: {'unlabeled': 0, 'domino': 1}\n",
      "id2label dictionary values: {0: 'unlabeled', 1: 'domino'}\n"
     ]
    }
   ],
   "source": [
    "id2label = {0: \"unlabeled\", 1: \"domino\"}\n",
    "label2id = {label: idx for idx, label in id2label.items()}\n",
    "\n",
    "unqiue_label_values = list(id2label.values())\n",
    "\n",
    "NUM_OF_LABELS = len(unqiue_label_values)\n",
    "\n",
    "print(f\"List of Unique Label Values: {unqiue_label_values}\")\n",
    "print(f\"Number of Unique Label Values: {NUM_OF_LABELS}\")\n",
    "print(f\"label2id dictionary values: {label2id}\")\n",
    "print(f\"id2label dictionary values: {id2label}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic Values/Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL_CKPT = \"nvidia/mit-b0\"\n",
    "MODEL_NAME = f'{MODEL_CKPT.split(\"/\")[-1]}-Image_segmentation_Dominoes_v2'\n",
    "\n",
    "LR = 5e-4\n",
    "NUM_OF_EPOCHS = 25\n",
    "\n",
    "STRATEGY = \"epoch\"\n",
    "REPORTS_TO = \"tensorboard\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define Image Processor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file preprocessor_config.json from cache at /Users/briandunn/.cache/huggingface/hub/models--nvidia--mit-b0/snapshots/ed0b85c75627eab6a3c6989627450cf95f115381/preprocessor_config.json\n",
      "Could not find image processor class in the image processor config or the model config. Loading based on pattern matching with the model's feature extractor configuration.\n",
      "/Users/briandunn/Desktop/python_venvs/text_sum/lib/python3.9/site-packages/transformers/models/segformer/image_processing_segformer.py:102: FutureWarning: The `reduce_labels` parameter is deprecated and will be removed in a future version. Please use `do_reduce_labels` instead.\n",
      "  warnings.warn(\n",
      "size should be a dictionary on of the following set of keys: ({'height', 'width'}, {'shortest_edge'}, {'longest_edge', 'shortest_edge'}), got 512. Converted to {'height': 512, 'width': 512}.\n",
      "Image processor SegformerImageProcessor {\n",
      "  \"do_normalize\": true,\n",
      "  \"do_reduce_labels\": false,\n",
      "  \"do_rescale\": true,\n",
      "  \"do_resize\": true,\n",
      "  \"image_mean\": [\n",
      "    0.485,\n",
      "    0.456,\n",
      "    0.406\n",
      "  ],\n",
      "  \"image_processor_type\": \"SegformerImageProcessor\",\n",
      "  \"image_std\": [\n",
      "    0.229,\n",
      "    0.224,\n",
      "    0.225\n",
      "  ],\n",
      "  \"resample\": 2,\n",
      "  \"rescale_factor\": 0.00392156862745098,\n",
      "  \"size\": {\n",
      "    \"height\": 512,\n",
      "    \"width\": 512\n",
      "  }\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "image_processor = AutoImageProcessor.from_pretrained(MODEL_CKPT, reduce_labels=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Augment Data (to Prevent Model Overfitting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "jitter = ColorJitter(brightness=0.35, \n",
    "                     contrast=0.35, \n",
    "                     saturation=0.35, \n",
    "                     hue=0.20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define Function to Convert Any Greyscale Images to RGB Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_greyscale_images(image):\n",
    "    np_image = np.array(image)\n",
    "    if np_image.ndim == 2:\n",
    "        tiled_image = np.tile(np.expand_dims(np_image, -1), 3)\n",
    "        return Image.fromarray(tiled_image)\n",
    "    else:\n",
    "        return Image.fromarray(np_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use Above Functions to Create Functions to Transform Training Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_transform(sample_batch):\n",
    "    images = [prepare_greyscale_images(x) for x in sample_batch[\"image\"]]\n",
    "    labels = [x for x in sample_batch['annotation']]\n",
    "    inputs = image_processor(images, labels)\n",
    "    return inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use Above Functions to Create Function to Transform Testing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_transform(sample_batch):\n",
    "    images = [jitter(prepare_greyscale_images(x)) for x in sample_batch[\"image\"]]\n",
    "    labels = [x for x in sample_batch['annotation']]\n",
    "    inputs = image_processor(images, labels)\n",
    "    return inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Apply Transformation Functions on Respective Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ds.set_transform(train_transform)\n",
    "test_ds.set_transform(test_transform)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define Compute Metrics Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "metric = evaluate.load(\"mean_iou\")\n",
    "    \n",
    "def compute_metrics(eval_pred):\n",
    "    with torch.no_grad():\n",
    "        logits, labels = eval_pred\n",
    "        logits_tensor = torch.from_numpy(logits)\n",
    "        logits_tensor = nn.functional.interpolate(\n",
    "            logits_tensor,\n",
    "            size=labels.shape[-2:],\n",
    "            mode=\"bilinear\",\n",
    "            align_corners=False,\n",
    "        ).argmax(dim=1)\n",
    "\n",
    "        pred_labels = logits_tensor.detach().cpu().numpy()\n",
    "        metrics = metric._compute(\n",
    "            predictions=pred_labels,\n",
    "            references=labels,\n",
    "            num_labels=NUM_OF_LABELS,\n",
    "            ignore_index=255,\n",
    "            reduce_labels=False,\n",
    "        )\n",
    "        for key, value in metrics.items():\n",
    "            if type(value) is np.ndarray:\n",
    "                metrics[key] = value.tolist()\n",
    "        return metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define Function to Display Number of Trainable Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_trainable_parameters(model):\n",
    "    \"\"\"\n",
    "    This function prints the following values:\n",
    "    - the number of parameters\n",
    "    - the trainable parameters \n",
    "    - the percentage of total parameters \n",
    "        that are trainable.\n",
    "    \"\"\"\n",
    "    trainable_params = 0\n",
    "    all_param = 0\n",
    "    for _, param in model.named_parameters():\n",
    "        all_param += param.numel()\n",
    "        if param.requires_grad:\n",
    "            trainable_params += param.numel()\n",
    "    print(f\"| Trainable parameters: {trainable_params} |\")\n",
    "    print(f\"| All parameters: {all_param} |\")\n",
    "    print(f\"| % Trainable: {100 * trainable_params / all_param:.2f}\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define Base Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file config.json from cache at /Users/briandunn/.cache/huggingface/hub/models--nvidia--mit-b0/snapshots/ed0b85c75627eab6a3c6989627450cf95f115381/config.json\n",
      "Model config SegformerConfig {\n",
      "  \"_name_or_path\": \"nvidia/mit-b0\",\n",
      "  \"architectures\": [\n",
      "    \"SegformerForImageClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.0,\n",
      "  \"classifier_dropout_prob\": 0.1,\n",
      "  \"decoder_hidden_size\": 256,\n",
      "  \"depths\": [\n",
      "    2,\n",
      "    2,\n",
      "    2,\n",
      "    2\n",
      "  ],\n",
      "  \"downsampling_rates\": [\n",
      "    1,\n",
      "    4,\n",
      "    8,\n",
      "    16\n",
      "  ],\n",
      "  \"drop_path_rate\": 0.1,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.0,\n",
      "  \"hidden_sizes\": [\n",
      "    32,\n",
      "    64,\n",
      "    160,\n",
      "    256\n",
      "  ],\n",
      "  \"id2label\": {\n",
      "    \"0\": \"unlabeled\",\n",
      "    \"1\": \"domino\"\n",
      "  },\n",
      "  \"image_size\": 224,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"label2id\": {\n",
      "    \"domino\": 1,\n",
      "    \"unlabeled\": 0\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-06,\n",
      "  \"mlp_ratios\": [\n",
      "    4,\n",
      "    4,\n",
      "    4,\n",
      "    4\n",
      "  ],\n",
      "  \"model_type\": \"segformer\",\n",
      "  \"num_attention_heads\": [\n",
      "    1,\n",
      "    2,\n",
      "    5,\n",
      "    8\n",
      "  ],\n",
      "  \"num_channels\": 3,\n",
      "  \"num_encoder_blocks\": 4,\n",
      "  \"patch_sizes\": [\n",
      "    7,\n",
      "    3,\n",
      "    3,\n",
      "    3\n",
      "  ],\n",
      "  \"reshape_last_stage\": true,\n",
      "  \"semantic_loss_ignore_index\": 255,\n",
      "  \"sr_ratios\": [\n",
      "    8,\n",
      "    4,\n",
      "    2,\n",
      "    1\n",
      "  ],\n",
      "  \"strides\": [\n",
      "    4,\n",
      "    2,\n",
      "    2,\n",
      "    2\n",
      "  ],\n",
      "  \"torch_dtype\": \"float32\",\n",
      "  \"transformers_version\": \"4.26.1\"\n",
      "}\n",
      "\n",
      "loading weights file pytorch_model.bin from cache at /Users/briandunn/.cache/huggingface/hub/models--nvidia--mit-b0/snapshots/ed0b85c75627eab6a3c6989627450cf95f115381/pytorch_model.bin\n",
      "Some weights of the model checkpoint at nvidia/mit-b0 were not used when initializing SegformerForSemanticSegmentation: ['classifier.bias', 'classifier.weight']\n",
      "- This IS expected if you are initializing SegformerForSemanticSegmentation from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing SegformerForSemanticSegmentation from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of SegformerForSemanticSegmentation were not initialized from the model checkpoint at nvidia/mit-b0 and are newly initialized: ['decode_head.linear_c.1.proj.bias', 'decode_head.linear_c.3.proj.bias', 'decode_head.classifier.weight', 'decode_head.batch_norm.num_batches_tracked', 'decode_head.batch_norm.running_mean', 'decode_head.linear_fuse.weight', 'decode_head.linear_c.2.proj.weight', 'decode_head.linear_c.3.proj.weight', 'decode_head.linear_c.1.proj.weight', 'decode_head.classifier.bias', 'decode_head.batch_norm.weight', 'decode_head.linear_c.2.proj.bias', 'decode_head.linear_c.0.proj.weight', 'decode_head.linear_c.0.proj.bias', 'decode_head.batch_norm.running_var', 'decode_head.batch_norm.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Trainable parameters: 3714658 |\n",
      "| All parameters: 3714658 |\n",
      "| % Trainable: 100.00\n"
     ]
    }
   ],
   "source": [
    "model = AutoModelForSemanticSegmentation.from_pretrained(\n",
    "    MODEL_CKPT, \n",
    "    id2label=id2label, \n",
    "    label2id=label2id,\n",
    "    ignore_mismatched_sizes=True)\n",
    "\n",
    "print_trainable_parameters(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define LoRA Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "lora_config = LoraConfig(\n",
    "    r=32, \n",
    "    lora_alpha=32,\n",
    "    target_modules=['query', 'values'],\n",
    "    lora_dropout=0.1,\n",
    "    bias='lora_only',\n",
    "    modules_to_save=['decode_head'],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wrap Base Model With LoRA Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Trainable parameters: 857092 |\n",
      "| All parameters: 4175460 |\n",
      "| % Trainable: 20.53\n"
     ]
    }
   ],
   "source": [
    "lora_model = get_peft_model(model, \n",
    "                            lora_config)\n",
    "\n",
    "print_trainable_parameters(lora_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define Training Arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PyTorch: setting up devices\n"
     ]
    }
   ],
   "source": [
    "args = TrainingArguments(\n",
    "    MODEL_NAME,\n",
    "    learning_rate=LR,\n",
    "    remove_unused_columns=False,\n",
    "    num_train_epochs=NUM_OF_EPOCHS,\n",
    "    auto_find_batch_size=True,\n",
    "    evaluation_strategy=STRATEGY,\n",
    "    save_strategy=STRATEGY,\n",
    "    label_names=['labels'],\n",
    "    logging_first_step=True,\n",
    "    logging_strategy=\"steps\",\n",
    "    logging_steps=24,\n",
    "    load_best_model_at_end=True,\n",
    "    report_to=REPORTS_TO,\n",
    "    hub_private_repo=True,\n",
    "    push_to_hub=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define Trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/briandunn/Desktop/Image Segmentation/Dominoes/Dominoes_v2/mit-b0-Image_segmentation_Dominoes_v2 is already a clone of https://huggingface.co/DunnBC22/mit-b0-Image_segmentation_Dominoes_v2. Make sure you pull the latest changes with `repo.git_pull()`.\n"
     ]
    }
   ],
   "source": [
    "trainer = Trainer(\n",
    "    lora_model,\n",
    "    args,\n",
    "    train_dataset=train_ds,\n",
    "    eval_dataset=test_ds,\n",
    "    compute_metrics=compute_metrics,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/briandunn/Desktop/python_venvs/text_sum/lib/python3.9/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n",
      "***** Running training *****\n",
      "  Num examples = 686\n",
      "  Num Epochs = 25\n",
      "  Instantaneous batch size per device = 8\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 2150\n",
      "  Number of trainable parameters = 857092\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cbf6a7d0b7af4c7a81b766401e118226",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2150 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'loss': 0.66, 'learning_rate': 0.0004997674418604651, 'epoch': 0.01}\n",
      "{'loss': 0.2098, 'learning_rate': 0.0004944186046511627, 'epoch': 0.28}\n",
      "{'loss': 0.0685, 'learning_rate': 0.0004888372093023256, 'epoch': 0.56}\n",
      "{'loss': 0.0461, 'learning_rate': 0.0004832558139534884, 'epoch': 0.84}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f0f778f6d1e4b8da370dc1d219cbe54",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9721967854031923, 0.8578619172251059]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9869082633464498, 0.9184139264010376]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-86\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.12327656149864197, 'eval_mean_iou': 0.9150293513141491, 'eval_mean_accuracy': 0.9526610948737437, 'eval_overall_accuracy': 0.9761918798669592, 'eval_per_category_iou': [0.9721967854031923, 0.8578619172251059], 'eval_per_category_accuracy': [0.9869082633464498, 0.9184139264010376], 'eval_runtime': 16.9947, 'eval_samples_per_second': 4.531, 'eval_steps_per_second': 0.588, 'epoch': 1.0}\n",
      "{'loss': 0.0253, 'learning_rate': 0.00047767441860465114, 'epoch': 1.12}\n",
      "{'loss': 0.0218, 'learning_rate': 0.0004720930232558139, 'epoch': 1.4}\n",
      "{'loss': 0.0185, 'learning_rate': 0.00046651162790697676, 'epoch': 1.67}\n",
      "{'loss': 0.0708, 'learning_rate': 0.00046093023255813954, 'epoch': 1.95}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "292ac1f580984931bceab22af2447cd1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9732821853093164, 0.8611008788165083]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9898473600751747, 0.9082362492748777]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-172\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.13664233684539795, 'eval_mean_iou': 0.9171915320629124, 'eval_mean_accuracy': 0.9490418046750262, 'eval_overall_accuracy': 0.9770787722104556, 'eval_per_category_iou': [0.9732821853093164, 0.8611008788165083], 'eval_per_category_accuracy': [0.9898473600751747, 0.9082362492748777], 'eval_runtime': 16.5905, 'eval_samples_per_second': 4.641, 'eval_steps_per_second': 0.603, 'epoch': 2.0}\n",
      "{'loss': 0.0232, 'learning_rate': 0.0004553488372093023, 'epoch': 2.23}\n",
      "{'loss': 0.0233, 'learning_rate': 0.00044976744186046516, 'epoch': 2.51}\n",
      "{'loss': 0.048, 'learning_rate': 0.00044418604651162794, 'epoch': 2.79}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "85db5d055a0e4739be26772f231e7dc7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9740118174014271, 0.8658241844233872]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9888392553004053, 0.9179240730467295]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-258\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.12595057487487793, 'eval_mean_iou': 0.919918000912407, 'eval_mean_accuracy': 0.9533816641735674, 'eval_overall_accuracy': 0.9777441148634081, 'eval_per_category_iou': [0.9740118174014271, 0.8658241844233872], 'eval_per_category_accuracy': [0.9888392553004053, 0.9179240730467295], 'eval_runtime': 29.4271, 'eval_samples_per_second': 2.617, 'eval_steps_per_second': 0.34, 'epoch': 3.0}\n",
      "{'loss': 0.0211, 'learning_rate': 0.00043860465116279067, 'epoch': 3.07}\n",
      "{'loss': 0.0158, 'learning_rate': 0.0004330232558139535, 'epoch': 3.35}\n",
      "{'loss': 0.0194, 'learning_rate': 0.0004274418604651163, 'epoch': 3.63}\n",
      "{'loss': 0.0535, 'learning_rate': 0.00042186046511627907, 'epoch': 3.91}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2abf3d29288448f089b4d54ac2d79d13",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.974142444792198, 0.8658711064023369]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9896291184589182, 0.9142864290038782]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-344\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.1184200793504715, 'eval_mean_iou': 0.9200067755972674, 'eval_mean_accuracy': 0.9519577737313982, 'eval_overall_accuracy': 0.9778412657898742, 'eval_per_category_iou': [0.974142444792198, 0.8658711064023369], 'eval_per_category_accuracy': [0.9896291184589182, 0.9142864290038782], 'eval_runtime': 27.4965, 'eval_samples_per_second': 2.8, 'eval_steps_per_second': 0.364, 'epoch': 4.0}\n",
      "{'loss': 0.036, 'learning_rate': 0.00041627906976744185, 'epoch': 4.19}\n",
      "{'loss': 0.0207, 'learning_rate': 0.0004106976744186047, 'epoch': 4.47}\n",
      "{'loss': 0.0185, 'learning_rate': 0.0004051162790697674, 'epoch': 4.74}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c9167550012345e5acbca54f98116148",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9737715695013129, 0.8627108292167807]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9910418746696423, 0.904378218719681]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-430\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.12956373393535614, 'eval_mean_iou': 0.9182411993590468, 'eval_mean_accuracy': 0.9477100466946617, 'eval_overall_accuracy': 0.9774827833299513, 'eval_per_category_iou': [0.9737715695013129, 0.8627108292167807], 'eval_per_category_accuracy': [0.9910418746696423, 0.904378218719681], 'eval_runtime': 16.6364, 'eval_samples_per_second': 4.628, 'eval_steps_per_second': 0.601, 'epoch': 5.0}\n",
      "{'loss': 0.0221, 'learning_rate': 0.0003995348837209302, 'epoch': 5.02}\n",
      "{'loss': 0.0188, 'learning_rate': 0.00039395348837209304, 'epoch': 5.3}\n",
      "{'loss': 0.0253, 'learning_rate': 0.0003883720930232558, 'epoch': 5.58}\n",
      "{'loss': 0.036, 'learning_rate': 0.0003827906976744186, 'epoch': 5.86}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "384d15aa196a4fb3ac8d444ab3e28661",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9745002408443008, 0.8680673581922554]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9892677512186527, 0.9182967669045321]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-516\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.14100100100040436, 'eval_mean_iou': 0.9212837995182781, 'eval_mean_accuracy': 0.9537822590615924, 'eval_overall_accuracy': 0.9781638801871956, 'eval_per_category_iou': [0.9745002408443008, 0.8680673581922554], 'eval_per_category_accuracy': [0.9892677512186527, 0.9182967669045321], 'eval_runtime': 16.57, 'eval_samples_per_second': 4.647, 'eval_steps_per_second': 0.604, 'epoch': 6.0}\n",
      "{'loss': 0.0148, 'learning_rate': 0.00037720930232558144, 'epoch': 6.14}\n",
      "{'loss': 0.0118, 'learning_rate': 0.0003716279069767442, 'epoch': 6.42}\n",
      "{'loss': 0.0256, 'learning_rate': 0.00036604651162790695, 'epoch': 6.7}\n",
      "{'loss': 0.0376, 'learning_rate': 0.00036046511627906973, 'epoch': 6.98}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8fdc565a29154d5d9842f95bb8b44e0e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9741455743906073, 0.8669703237367214]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9883004639689904, 0.9216576612178001]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-602\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.14512722194194794, 'eval_mean_iou': 0.9205579490636644, 'eval_mean_accuracy': 0.9549790625933953, 'eval_overall_accuracy': 0.9778737650289164, 'eval_per_category_iou': [0.9741455743906073, 0.8669703237367214], 'eval_per_category_accuracy': [0.9883004639689904, 0.9216576612178001], 'eval_runtime': 16.4233, 'eval_samples_per_second': 4.688, 'eval_steps_per_second': 0.609, 'epoch': 7.0}\n",
      "{'loss': 0.0241, 'learning_rate': 0.00035488372093023257, 'epoch': 7.26}\n",
      "{'loss': 0.0235, 'learning_rate': 0.00034930232558139535, 'epoch': 7.53}\n",
      "{'loss': 0.0186, 'learning_rate': 0.00034372093023255813, 'epoch': 7.81}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "217753d1c8a249ffaaf343233d8e6d22",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9733616852468584, 0.8616466350192237]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9897043519116697, 0.9094762400541087]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-688\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.13804595172405243, 'eval_mean_iou': 0.917504160133041, 'eval_mean_accuracy': 0.9495902959828892, 'eval_overall_accuracy': 0.9771521432059151, 'eval_per_category_iou': [0.9733616852468584, 0.8616466350192237], 'eval_per_category_accuracy': [0.9897043519116697, 0.9094762400541087], 'eval_runtime': 16.3694, 'eval_samples_per_second': 4.704, 'eval_steps_per_second': 0.611, 'epoch': 8.0}\n",
      "{'loss': 0.0271, 'learning_rate': 0.00033813953488372097, 'epoch': 8.09}\n",
      "{'loss': 0.0181, 'learning_rate': 0.00033255813953488375, 'epoch': 8.37}\n",
      "{'loss': 0.0297, 'learning_rate': 0.0003269767441860465, 'epoch': 8.65}\n",
      "{'loss': 0.0162, 'learning_rate': 0.0003213953488372093, 'epoch': 8.93}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6efe220504454ac6b4af123e359915d7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9746840649852051, 0.8688930149000804]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.989455276913138, 0.9182917005479264]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-774\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.1459001749753952, 'eval_mean_iou': 0.9217885399426428, 'eval_mean_accuracy': 0.9538734887305322, 'eval_overall_accuracy': 0.9783212736055449, 'eval_per_category_iou': [0.9746840649852051, 0.8688930149000804], 'eval_per_category_accuracy': [0.989455276913138, 0.9182917005479264], 'eval_runtime': 16.4631, 'eval_samples_per_second': 4.677, 'eval_steps_per_second': 0.607, 'epoch': 9.0}\n",
      "{'loss': 0.0123, 'learning_rate': 0.0003158139534883721, 'epoch': 9.21}\n",
      "{'loss': 0.032, 'learning_rate': 0.0003102325581395349, 'epoch': 9.49}\n",
      "{'loss': 0.0169, 'learning_rate': 0.00030465116279069766, 'epoch': 9.77}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a98290bc87044a9d86d525e7b39e8b0d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9739086600912814, 0.8642187978193332]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9901195747929759, 0.9102564589713776]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-860\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.1467013657093048, 'eval_mean_iou': 0.9190637289553073, 'eval_mean_accuracy': 0.9501880168821768, 'eval_overall_accuracy': 0.9776244720855316, 'eval_per_category_iou': [0.9739086600912814, 0.8642187978193332], 'eval_per_category_accuracy': [0.9901195747929759, 0.9102564589713776], 'eval_runtime': 16.4671, 'eval_samples_per_second': 4.676, 'eval_steps_per_second': 0.607, 'epoch': 10.0}\n",
      "{'loss': 0.0243, 'learning_rate': 0.0002990697674418605, 'epoch': 10.05}\n",
      "{'loss': 0.0291, 'learning_rate': 0.0002934883720930233, 'epoch': 10.33}\n",
      "{'loss': 0.0169, 'learning_rate': 0.000287906976744186, 'epoch': 10.6}\n",
      "{'loss': 0.0102, 'learning_rate': 0.00028232558139534885, 'epoch': 10.88}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "00771cf1ff174949ad2acef784f7d8a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9737696499931041, 0.8644247331609153]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9889789745698009, 0.915789237032027]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-946\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.15493769943714142, 'eval_mean_iou': 0.9190971915770096, 'eval_mean_accuracy': 0.9523841058009139, 'eval_overall_accuracy': 0.9775279651988636, 'eval_per_category_iou': [0.9737696499931041, 0.8644247331609153], 'eval_per_category_accuracy': [0.9889789745698009, 0.915789237032027], 'eval_runtime': 16.4883, 'eval_samples_per_second': 4.67, 'eval_steps_per_second': 0.606, 'epoch': 11.0}\n",
      "{'loss': 0.0379, 'learning_rate': 0.00027674418604651163, 'epoch': 11.16}\n",
      "{'loss': 0.026, 'learning_rate': 0.0002711627906976744, 'epoch': 11.44}\n",
      "{'loss': 0.0122, 'learning_rate': 0.00026558139534883725, 'epoch': 11.72}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'loss': 0.0204, 'learning_rate': 0.00026000000000000003, 'epoch': 12.0}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d5134cbd8fb47efbfd09ce91980a65c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.974639596078376, 0.8682964916021273]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.989902977623774, 0.9155653673995151]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-1032\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.15023675560951233, 'eval_mean_iou': 0.9214680438402516, 'eval_mean_accuracy': 0.9527341725116445, 'eval_overall_accuracy': 0.9782723761224127, 'eval_per_category_iou': [0.974639596078376, 0.8682964916021273], 'eval_per_category_accuracy': [0.989902977623774, 0.9155653673995151], 'eval_runtime': 16.4023, 'eval_samples_per_second': 4.694, 'eval_steps_per_second': 0.61, 'epoch': 12.0}\n",
      "{'loss': 0.0108, 'learning_rate': 0.0002544186046511628, 'epoch': 12.28}\n",
      "{'loss': 0.0319, 'learning_rate': 0.0002488372093023256, 'epoch': 12.56}\n",
      "{'loss': 0.0268, 'learning_rate': 0.00024325581395348835, 'epoch': 12.84}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "06890b28764f4260bbb65cfca58370e9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9740020531855834, 0.8647199376136]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.99011699066189, 0.9107963425971664]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-1118\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.1412886083126068, 'eval_mean_iou': 0.9193609953995917, 'eval_mean_accuracy': 0.9504566666295282, 'eval_overall_accuracy': 0.9777067605551187, 'eval_per_category_iou': [0.9740020531855834, 0.8647199376136], 'eval_per_category_accuracy': [0.99011699066189, 0.9107963425971664], 'eval_runtime': 16.4252, 'eval_samples_per_second': 4.688, 'eval_steps_per_second': 0.609, 'epoch': 13.0}\n",
      "{'loss': 0.0181, 'learning_rate': 0.00023767441860465116, 'epoch': 13.12}\n",
      "{'loss': 0.0213, 'learning_rate': 0.00023209302325581397, 'epoch': 13.4}\n",
      "{'loss': 0.0184, 'learning_rate': 0.00022651162790697675, 'epoch': 13.67}\n",
      "{'loss': 0.0166, 'learning_rate': 0.00022093023255813954, 'epoch': 13.95}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e974e32713ab4e02a147e4d45de25765",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9731154475737929, 0.8614276032542578]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9884142831972749, 0.9152366875147241]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-1204\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.1583731472492218, 'eval_mean_iou': 0.9172715254140253, 'eval_mean_accuracy': 0.9518254853559995, 'eval_overall_accuracy': 0.9769651734983766, 'eval_per_category_iou': [0.9731154475737929, 0.8614276032542578], 'eval_per_category_accuracy': [0.9884142831972749, 0.9152366875147241], 'eval_runtime': 16.4301, 'eval_samples_per_second': 4.687, 'eval_steps_per_second': 0.609, 'epoch': 14.0}\n",
      "{'loss': 0.0166, 'learning_rate': 0.00021534883720930232, 'epoch': 14.23}\n",
      "{'loss': 0.0105, 'learning_rate': 0.00020976744186046513, 'epoch': 14.51}\n",
      "{'loss': 0.0159, 'learning_rate': 0.0002041860465116279, 'epoch': 14.79}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "857346845a6e46c2ba24ebfe10ec1bf5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9731832402253996, 0.8607442858381036]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9896456803899689, 0.9087960816798012]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-1290\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.1562587469816208, 'eval_mean_iou': 0.9169637630317515, 'eval_mean_accuracy': 0.949220881034885, 'eval_overall_accuracy': 0.9769962360332538, 'eval_per_category_iou': [0.9731832402253996, 0.8607442858381036], 'eval_per_category_accuracy': [0.9896456803899689, 0.9087960816798012], 'eval_runtime': 16.4516, 'eval_samples_per_second': 4.68, 'eval_steps_per_second': 0.608, 'epoch': 15.0}\n",
      "{'loss': 0.0261, 'learning_rate': 0.0001986046511627907, 'epoch': 15.07}\n",
      "{'loss': 0.0115, 'learning_rate': 0.0001930232558139535, 'epoch': 15.35}\n",
      "{'loss': 0.0267, 'learning_rate': 0.00018744186046511628, 'epoch': 15.63}\n",
      "{'loss': 0.0211, 'learning_rate': 0.00018186046511627907, 'epoch': 15.91}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7fd9a0ea657c4c109916f34f70b6afa7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9725201360275898, 0.8574847000491036]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.989323310037, 0.9068449010920532]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-1376\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.14351056516170502, 'eval_mean_iou': 0.9150024180383467, 'eval_mean_accuracy': 0.9480841055645266, 'eval_overall_accuracy': 0.9764190277495941, 'eval_per_category_iou': [0.9725201360275898, 0.8574847000491036], 'eval_per_category_accuracy': [0.989323310037, 0.9068449010920532], 'eval_runtime': 16.4116, 'eval_samples_per_second': 4.692, 'eval_steps_per_second': 0.609, 'epoch': 16.0}\n",
      "{'loss': 0.0133, 'learning_rate': 0.00017627906976744188, 'epoch': 16.19}\n",
      "{'loss': 0.0228, 'learning_rate': 0.00017069767441860466, 'epoch': 16.47}\n",
      "{'loss': 0.0128, 'learning_rate': 0.00016511627906976744, 'epoch': 16.74}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "865b25c3f8e548a5aa3c2f7cf88ee62a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9745789801464504, 0.8677394402794754]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9901920479238856, 0.9136255861141298]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-1462\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.14211878180503845, 'eval_mean_iou': 0.9211592102129629, 'eval_mean_accuracy': 0.9519088170190078, 'eval_overall_accuracy': 0.9782127281288048, 'eval_per_category_iou': [0.9745789801464504, 0.8677394402794754], 'eval_per_category_accuracy': [0.9901920479238856, 0.9136255861141298], 'eval_runtime': 16.475, 'eval_samples_per_second': 4.674, 'eval_steps_per_second': 0.607, 'epoch': 17.0}\n",
      "{'loss': 0.021, 'learning_rate': 0.00015953488372093022, 'epoch': 17.02}\n",
      "{'loss': 0.0208, 'learning_rate': 0.00015395348837209303, 'epoch': 17.3}\n",
      "{'loss': 0.0119, 'learning_rate': 0.00014837209302325581, 'epoch': 17.58}\n",
      "{'loss': 0.0167, 'learning_rate': 0.0001427906976744186, 'epoch': 17.86}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f2e82a85152a4329bc6b706d7790796f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9746811993626879, 0.8686470009484697]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9897428202266988, 0.9166850322093621]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-1548\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.15581434965133667, 'eval_mean_iou': 0.9216641001555788, 'eval_mean_accuracy': 0.9532139262180305, 'eval_overall_accuracy': 0.9783124552144633, 'eval_per_category_iou': [0.9746811993626879, 0.8686470009484697], 'eval_per_category_accuracy': [0.9897428202266988, 0.9166850322093621], 'eval_runtime': 16.3576, 'eval_samples_per_second': 4.707, 'eval_steps_per_second': 0.611, 'epoch': 18.0}\n",
      "{'loss': 0.0155, 'learning_rate': 0.0001372093023255814, 'epoch': 18.14}\n",
      "{'loss': 0.018, 'learning_rate': 0.0001316279069767442, 'epoch': 18.42}\n",
      "{'loss': 0.0097, 'learning_rate': 0.00012604651162790697, 'epoch': 18.7}\n",
      "{'loss': 0.0201, 'learning_rate': 0.00012046511627906977, 'epoch': 18.98}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "934370dba81040c0b3b05170718a2c18",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9727184720007118, 0.8584339325695252]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9894484642039114, 0.9072695251050635]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-1634\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.16230051219463348, 'eval_mean_iou': 0.9155762022851185, 'eval_mean_accuracy': 0.9483589946544875, 'eval_overall_accuracy': 0.9765910359172078, 'eval_per_category_iou': [0.9727184720007118, 0.8584339325695252], 'eval_per_category_accuracy': [0.9894484642039114, 0.9072695251050635], 'eval_runtime': 17.2315, 'eval_samples_per_second': 4.469, 'eval_steps_per_second': 0.58, 'epoch': 19.0}\n",
      "{'loss': 0.0214, 'learning_rate': 0.00011488372093023255, 'epoch': 19.26}\n",
      "{'loss': 0.0182, 'learning_rate': 0.00010930232558139536, 'epoch': 19.53}\n",
      "{'loss': 0.0133, 'learning_rate': 0.00010372093023255814, 'epoch': 19.81}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5e2f1262eb7748679ccec37a86e56cdf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9738320500157303, 0.8640203613069115]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9898665061373113, 0.9112263496140702]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-1720\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.15733253955841064, 'eval_mean_iou': 0.9189262056613209, 'eval_mean_accuracy': 0.9505464278756908, 'eval_overall_accuracy': 0.9775627433479607, 'eval_per_category_iou': [0.9738320500157303, 0.8640203613069115], 'eval_per_category_accuracy': [0.9898665061373113, 0.9112263496140702], 'eval_runtime': 16.4506, 'eval_samples_per_second': 4.681, 'eval_steps_per_second': 0.608, 'epoch': 20.0}\n",
      "{'loss': 0.0124, 'learning_rate': 9.813953488372094e-05, 'epoch': 20.09}\n",
      "{'loss': 0.0175, 'learning_rate': 9.255813953488372e-05, 'epoch': 20.37}\n",
      "{'loss': 0.0215, 'learning_rate': 8.69767441860465e-05, 'epoch': 20.65}\n",
      "{'loss': 0.012, 'learning_rate': 8.139534883720931e-05, 'epoch': 20.93}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c2853826259d48ad86388dd27c93117e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9731344243001482, 0.8597866189796295]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9904592118400188, 0.9040137576913626]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-1806\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.16311487555503845, 'eval_mean_iou': 0.9164605216398889, 'eval_mean_accuracy': 0.9472364847656907, 'eval_overall_accuracy': 0.9769342595880682, 'eval_per_category_iou': [0.9731344243001482, 0.8597866189796295], 'eval_per_category_accuracy': [0.9904592118400188, 0.9040137576913626], 'eval_runtime': 16.3499, 'eval_samples_per_second': 4.71, 'eval_steps_per_second': 0.612, 'epoch': 21.0}\n",
      "{'loss': 0.0195, 'learning_rate': 7.58139534883721e-05, 'epoch': 21.21}\n",
      "{'loss': 0.0096, 'learning_rate': 7.023255813953489e-05, 'epoch': 21.49}\n",
      "{'loss': 0.0148, 'learning_rate': 6.465116279069767e-05, 'epoch': 21.77}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e81914e249b48009f866932b38c69f5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9735162429121835, 0.8627239955489192]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9894034768309156, 0.9120129014770962]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-1892\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.1628579944372177, 'eval_mean_iou': 0.9181201192305514, 'eval_mean_accuracy': 0.9507081891540059, 'eval_overall_accuracy': 0.9772952191241375, 'eval_per_category_iou': [0.9735162429121835, 0.8627239955489192], 'eval_per_category_accuracy': [0.9894034768309156, 0.9120129014770962], 'eval_runtime': 16.9646, 'eval_samples_per_second': 4.539, 'eval_steps_per_second': 0.589, 'epoch': 22.0}\n",
      "{'loss': 0.0145, 'learning_rate': 5.906976744186047e-05, 'epoch': 22.05}\n",
      "{'loss': 0.0176, 'learning_rate': 5.348837209302326e-05, 'epoch': 22.33}\n",
      "{'loss': 0.0102, 'learning_rate': 4.7906976744186046e-05, 'epoch': 22.6}\n",
      "{'loss': 0.0137, 'learning_rate': 4.232558139534884e-05, 'epoch': 22.88}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "506952f8d2474911a01e59cdccfcd0ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9719681843338751, 0.8552607882028388]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9885083690609032, 0.908250815050119]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-1978\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.1701258420944214, 'eval_mean_iou': 0.913614486268357, 'eval_mean_accuracy': 0.9483795920555111, 'eval_overall_accuracy': 0.9759515539392248, 'eval_per_category_iou': [0.9719681843338751, 0.8552607882028388], 'eval_per_category_accuracy': [0.9885083690609032, 0.908250815050119], 'eval_runtime': 17.2345, 'eval_samples_per_second': 4.468, 'eval_steps_per_second': 0.58, 'epoch': 23.0}\n",
      "{'loss': 0.0121, 'learning_rate': 3.6744186046511624e-05, 'epoch': 23.16}\n",
      "{'loss': 0.011, 'learning_rate': 3.116279069767442e-05, 'epoch': 23.44}\n",
      "{'loss': 0.0135, 'learning_rate': 2.558139534883721e-05, 'epoch': 23.72}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'loss': 0.0142, 'learning_rate': 2e-05, 'epoch': 24.0}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7af064346756472696dc8eb9e1fcdcc2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9723134197764093, 0.8568918401744342]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9887405884771245, 0.9089100747034281]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-2064\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.16463343799114227, 'eval_mean_iou': 0.9146026299754217, 'eval_mean_accuracy': 0.9488253315902764, 'eval_overall_accuracy': 0.9762505865716314, 'eval_per_category_iou': [0.9723134197764093, 0.8568918401744342], 'eval_per_category_accuracy': [0.9887405884771245, 0.9089100747034281], 'eval_runtime': 16.5904, 'eval_samples_per_second': 4.641, 'eval_steps_per_second': 0.603, 'epoch': 24.0}\n",
      "{'loss': 0.0185, 'learning_rate': 1.441860465116279e-05, 'epoch': 24.28}\n",
      "{'loss': 0.0145, 'learning_rate': 8.837209302325582e-06, 'epoch': 24.56}\n",
      "{'loss': 0.0156, 'learning_rate': 3.2558139534883724e-06, 'epoch': 24.84}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6f42dee0647541819fff9b25171347ed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.9723929259786395, 0.856345354289624]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9898487696012216, 0.9032139066422469]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2/checkpoint-2150\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.16153134405612946, 'eval_mean_iou': 0.9143691401341317, 'eval_mean_accuracy': 0.9465313381217342, 'eval_overall_accuracy': 0.97629418311181, 'eval_per_category_iou': [0.9723929259786395, 0.856345354289624], 'eval_per_category_accuracy': [0.9898487696012216, 0.9032139066422469], 'eval_runtime': 16.3649, 'eval_samples_per_second': 4.705, 'eval_steps_per_second': 0.611, 'epoch': 25.0}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n",
      "Loading best model from mit-b0-Image_segmentation_Dominoes_v2/checkpoint-344 (score: 0.1184200793504715).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_runtime': 11473.4494, 'train_samples_per_second': 1.495, 'train_steps_per_second': 0.187, 'train_loss': 0.02368642721065255, 'epoch': 25.0}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=2150, training_loss=0.02368642721065255, metrics={'train_runtime': 11473.4494, 'train_samples_per_second': 1.495, 'train_steps_per_second': 0.187, 'train_loss': 0.02368642721065255, 'epoch': 25.0})"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 77\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c5ff26241a574a5288a55b9ad1ae35cb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"[0.974110559111975, 0.8655745252092782]\" of type <class 'list'> for key \"eval/per_category_iou\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"[0.9897833441005461, 0.913253525550903]\" of type <class 'list'> for key \"eval/per_category_accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'eval_loss': 0.11491713672876358,\n",
       " 'eval_mean_iou': 0.9198425421606267,\n",
       " 'eval_mean_accuracy': 0.9515184348257246,\n",
       " 'eval_overall_accuracy': 0.9778097573812906,\n",
       " 'eval_per_category_iou': [0.974110559111975, 0.8655745252092782],\n",
       " 'eval_per_category_accuracy': [0.9897833441005461, 0.913253525550903],\n",
       " 'eval_runtime': 16.8487,\n",
       " 'eval_samples_per_second': 4.57,\n",
       " 'eval_steps_per_second': 0.594,\n",
       " 'epoch': 25.0}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.evaluate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "lora_model.save_pretrained(f'DunnBC22/{MODEL_NAME}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Push Model to Hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving model checkpoint to mit-b0-Image_segmentation_Dominoes_v2\n",
      "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf8579a2c57442479130dd307418b226",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload file runs/Jul26_16-13-57_Brians-Mac-mini/events.out.tfevents.1690406040.Brians-Mac-mini.18899.2:   0%| …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6979c819168d409d9b9f63eff05e8922",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload file runs/Jul26_16-13-57_Brians-Mac-mini/events.out.tfevents.1690417530.Brians-Mac-mini.18899.4:   0%| …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "To https://huggingface.co/DunnBC22/mit-b0-Image_segmentation_Dominoes_v2\n",
      "   b1757f7..9a10fec  main -> main\n",
      "\n",
      "To https://huggingface.co/DunnBC22/mit-b0-Image_segmentation_Dominoes_v2\n",
      "   9a10fec..3773bdd  main -> main\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'https://huggingface.co/DunnBC22/mit-b0-Image_segmentation_Dominoes_v2/commit/9a10fec84746c059cd0e59364255a9e4b5e4fc42'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.push_to_hub(\"All DUNN!!!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notes & Other Takeaways From This Project\n",
    "\n",
    "****\n",
    "- I am surprised at both how well the results are of this project and how quickly this project converged on the optimal weights. \n",
    "\n",
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Citations\n",
    "\n",
    "- Model Checkpoint (nvidia/mit-b0)\n",
    "    > @article{DBLP:journals/corr/abs-2105-15203, author = {Enze Xie and Wenhai Wang and Zhiding Yu and Anima Anandkumar and Jose M. Alvarez and Ping Luo}, title = {SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers}, journal = {CoRR}, volume = {abs/2105.15203}, year = {2021}, url = {https://arxiv.org/abs/2105.15203}, eprinttype = {arXiv}, eprint = {2105.15203}, timestamp = {Wed, 02 Jun 2021 11:46:42 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib}, bibsource = {dblp computer science bibliography, https://dblp.org}}\n",
    "\n",
    "- Metrics (mean_iou)\n",
    "    > @software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020, author = {{MMSegmentation Contributors}}, license = {Apache-2.0}, month = {7}, title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}}, url = {https://github.com/open-mmlab/mmsegmentation}, year = {2020}}"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch_cv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}