ViT_Mask_FineTuned.ipynb

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      "source": [
        "%%capture\n",
        "! pip install datasets transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GInvJPjwS2OT",
        "outputId": "b308d490-9861-4c1e-b636-91a4cd2bc730"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/MaskDetection\n"
          ]
        }
      ],
      "source": [
        "%cd MaskDetection/"
      ]
    },
    {
      "cell_type": "code",
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      "outputs": [
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      "source": [
        "import torch\n",
        "import torchvision.transforms as transforms\n",
        "import os\n",
        "import pandas as pd\n",
        "from skimage import io\n",
        "from torch.utils.data import (\n",
        "    Dataset,\n",
        ")\n",
        "\n",
        "from transformers import ViTFeatureExtractor\n",
        "\n",
        "model_name_or_path = 'google/vit-base-patch16-224-in21k'\n",
        "feature_extractor = ViTFeatureExtractor.from_pretrained(model_name_or_path)\n",
        "\n",
        "class FeatureExtractor(object):\n",
        "    def __call__(self, image, target):\n",
        "        sample = feature_extractor(image, return_tensors='pt')\n",
        "        sample[\"labels\"] = target\n",
        "        return sample\n",
        "\n",
        "class MaskDataset(Dataset):\n",
        "    def __init__(self, csv_file, root_dir, transform=None):\n",
        "        self.annotations = pd.read_csv(csv_file)\n",
        "        self.root_dir = root_dir\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.annotations)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        img_path = os.path.join(self.root_dir, self.annotations.iloc[index, 0])\n",
        "        image = io.imread(img_path)\n",
        "        y_label = torch.tensor(int(self.annotations.iloc[index, 1]))\n",
        "        \n",
        "        data = self.transform(image,y_label)\n",
        "\n",
        "        return data\n",
        "\n",
        "train_ds = MaskDataset(\n",
        "    csv_file=\"train.csv\",\n",
        "    root_dir=\"\",\n",
        "    transform=FeatureExtractor(),\n",
        ")\n",
        "\n",
        "test_ds = MaskDataset(\n",
        "    csv_file=\"test.csv\",\n",
        "    root_dir=\"\",\n",
        "    transform=FeatureExtractor(),\n",
        ")"
      ]
    },
    {
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              "          [-0.2471, -0.2471, -0.2471,  ..., -0.3882, -0.3882, -0.3882],\n",
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              "          [-0.7333, -0.7333, -0.7333,  ..., -0.6157, -0.6157, -0.6157],\n",
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              "          [ 0.8588,  0.8588,  0.8588,  ...,  0.7804,  0.7804,  0.7804]]]]), 'labels': tensor(1)}"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_ds.__getitem__(0)"
      ]
    },
    {
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      "source": [
        "import numpy as np\n",
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"accuracy\")\n",
        "def compute_metrics(p):\n",
        "    return metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids)"
      ]
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        },
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      "outputs": [
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        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at google/vit-base-patch16-224-in21k were not used when initializing ViTForImageClassification: ['pooler.dense.bias', 'pooler.dense.weight']\n",
            "- This IS expected if you are initializing ViTForImageClassification 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 ViTForImageClassification 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 ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "from transformers import ViTForImageClassification\n",
        "\n",
        "labels = [\"No Mask\",\"Mask\"]\n",
        "\n",
        "model = ViTForImageClassification.from_pretrained(\n",
        "    model_name_or_path,\n",
        "    num_labels=len(labels),\n",
        "    id2label={str(i): c for i, c in enumerate(labels)},\n",
        "    label2id={c: str(i) for i, c in enumerate(labels)}\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dr95lWdWIGqZ",
        "outputId": "337ceb7e-af1f-47a9-c158-be433a2255b2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "PyTorch: setting up devices\n"
          ]
        }
      ],
      "source": [
        "from transformers import TrainingArguments\n",
        "from transformers import EarlyStoppingCallback\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "  output_dir=\"./vit-base-mask-finetuned\",\n",
        "  per_device_train_batch_size=16,\n",
        "  evaluation_strategy=\"steps\",\n",
        "  num_train_epochs=4,\n",
        "  save_total_limit = 4, # Only last 4 models are saved. Older ones are deleted.\n",
        "  fp16=True,\n",
        "  save_steps=100,\n",
        "  eval_steps=100,\n",
        "  logging_steps=10,\n",
        "  learning_rate=2e-4,\n",
        "  remove_unused_columns=False,\n",
        "  push_to_hub=False,\n",
        "  report_to='tensorboard',\n",
        "  load_best_model_at_end=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "pQDGsAPiIGoW"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "\n",
        "def collate_fn(batch):\n",
        "    return {\n",
        "        'pixel_values': torch.stack([x['pixel_values'][0] for x in batch]),\n",
        "        'labels': torch.tensor([x['labels'] for x in batch])\n",
        "    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4w97XyHiIGlw",
        "outputId": "16286ed9-f55d-4351-f40f-d6c5029beeee"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Using amp half precision backend\n"
          ]
        }
      ],
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=collate_fn,\n",
        "    compute_metrics=compute_metrics,\n",
        "    train_dataset=train_ds,\n",
        "    eval_dataset=test_ds,\n",
        "    tokenizer=feature_extractor,\n",
        "    callbacks = [EarlyStoppingCallback(early_stopping_patience=3)]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FRyjG3JWIGjr",
        "outputId": "56df77d5-fe8a-41b2-929e-f813e6b423bd"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use thePyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 14428\n",
            "  Num Epochs = 4\n",
            "  Instantaneous batch size per device = 16\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 3608\n"
          ]
        },
        {
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='773' max='3608' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 773/3608 37:24 < 2:17:33, 0.34 it/s, Epoch 0.86/4]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.021400</td>\n",
              "      <td>0.155222</td>\n",
              "      <td>0.961346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.041500</td>\n",
              "      <td>0.061964</td>\n",
              "      <td>0.986374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.037200</td>\n",
              "      <td>0.058919</td>\n",
              "      <td>0.984983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.073800</td>\n",
              "      <td>0.173725</td>\n",
              "      <td>0.927419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.009600</td>\n",
              "      <td>0.039475</td>\n",
              "      <td>0.989433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.058400</td>\n",
              "      <td>0.175120</td>\n",
              "      <td>0.949944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.009700</td>\n",
              "      <td>0.045691</td>\n",
              "      <td>0.987764</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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          "output_type": "stream",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 3596\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-base-mask-finetuned/checkpoint-100\n",
            "Configuration saved in ./vit-base-mask-finetuned/checkpoint-100/config.json\n",
            "Model weights saved in ./vit-base-mask-finetuned/checkpoint-100/pytorch_model.bin\n",
            "Configuration saved in ./vit-base-mask-finetuned/checkpoint-100/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3596\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-base-mask-finetuned/checkpoint-200\n",
            "Configuration saved in ./vit-base-mask-finetuned/checkpoint-200/config.json\n",
            "Model weights saved in ./vit-base-mask-finetuned/checkpoint-200/pytorch_model.bin\n",
            "Configuration saved in ./vit-base-mask-finetuned/checkpoint-200/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3596\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-base-mask-finetuned/checkpoint-300\n",
            "Configuration saved in ./vit-base-mask-finetuned/checkpoint-300/config.json\n",
            "Model weights saved in ./vit-base-mask-finetuned/checkpoint-300/pytorch_model.bin\n",
            "Configuration saved in ./vit-base-mask-finetuned/checkpoint-300/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3596\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-base-mask-finetuned/checkpoint-400\n",
            "Configuration saved in ./vit-base-mask-finetuned/checkpoint-400/config.json\n",
            "Model weights saved in ./vit-base-mask-finetuned/checkpoint-400/pytorch_model.bin\n",
            "Configuration saved in ./vit-base-mask-finetuned/checkpoint-400/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3596\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-base-mask-finetuned/checkpoint-500\n",
            "Configuration saved in ./vit-base-mask-finetuned/checkpoint-500/config.json\n",
            "Model weights saved in ./vit-base-mask-finetuned/checkpoint-500/pytorch_model.bin\n",
            "Configuration saved in ./vit-base-mask-finetuned/checkpoint-500/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-base-mask-finetuned/checkpoint-100] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3596\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-base-mask-finetuned/checkpoint-600\n",
            "Configuration saved in ./vit-base-mask-finetuned/checkpoint-600/config.json\n",
            "Model weights saved in ./vit-base-mask-finetuned/checkpoint-600/pytorch_model.bin\n",
            "Configuration saved in ./vit-base-mask-finetuned/checkpoint-600/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-base-mask-finetuned/checkpoint-200] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3596\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-base-mask-finetuned/checkpoint-700\n",
            "Configuration saved in ./vit-base-mask-finetuned/checkpoint-700/config.json\n",
            "Model weights saved in ./vit-base-mask-finetuned/checkpoint-700/pytorch_model.bin\n",
            "Configuration saved in ./vit-base-mask-finetuned/checkpoint-700/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-base-mask-finetuned/checkpoint-300] due to args.save_total_limit\n"
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              "      [ 800/3608 40:09 < 2:21:19, 0.33 it/s, Epoch 0/4]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
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          "output_type": "stream",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 3596\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-base-mask-finetuned/checkpoint-800\n",
            "Configuration saved in ./vit-base-mask-finetuned/checkpoint-800/config.json\n",
            "Model weights saved in ./vit-base-mask-finetuned/checkpoint-800/pytorch_model.bin\n",
            "Configuration saved in ./vit-base-mask-finetuned/checkpoint-800/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-base-mask-finetuned/checkpoint-400] due to args.save_total_limit\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "Loading best model from ./vit-base-mask-finetuned/checkpoint-500 (score: 0.03947526961565018).\n",
            "Saving model checkpoint to ./vit-base-mask-finetuned\n",
            "Configuration saved in ./vit-base-mask-finetuned/config.json\n",
            "Model weights saved in ./vit-base-mask-finetuned/pytorch_model.bin\n",
            "Configuration saved in ./vit-base-mask-finetuned/preprocessor_config.json\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "***** train metrics *****\n",
            "  epoch                    =        0.89\n",
            "  total_flos               = 923776502GF\n",
            "  train_loss               =       0.057\n",
            "  train_runtime            =  0:40:10.40\n",
            "  train_samples_per_second =      23.943\n",
            "  train_steps_per_second   =       1.497\n"
          ]
        }
      ],
      "source": [
        "train_results = trainer.train()\n",
        "trainer.save_model()\n",
        "trainer.log_metrics(\"train\", train_results.metrics)\n",
        "trainer.save_metrics(\"train\", train_results.metrics)\n",
        "trainer.save_state()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "vWhlNQHJIGhS",
        "outputId": "b22e1522-3f5d-42d8-d973-41a3e697ced1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 3596\n",
            "  Batch size = 8\n"
          ]
        },
        {
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              "\n",
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              "      \n",
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              "      [450/450 00:36]\n",
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              "    "
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          "text": [
            "***** eval metrics *****\n",
            "  epoch                   =       0.89\n",
            "  eval_accuracy           =     0.9894\n",
            "  eval_loss               =     0.0395\n",
            "  eval_runtime            = 0:00:36.81\n",
            "  eval_samples_per_second =     97.685\n",
            "  eval_steps_per_second   =     12.224\n"
          ]
        }
      ],
      "source": [
        "metrics = trainer.evaluate(test_ds)\n",
        "trainer.log_metrics(\"eval\", metrics)\n",
        "trainer.save_metrics(\"eval\", metrics)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JvxJV4gNo26g",
        "outputId": "6d9fe3a1-ba76-49b4-f77f-96aa57a507db"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  adding: vit-base-mask-finetuned/ (stored 0%)\n",
            "  adding: vit-base-mask-finetuned/runs/ (stored 0%)\n",
            "  adding: vit-base-mask-finetuned/runs/Feb17_12-52-55_0ef67e2ff67c/ (stored 0%)\n",
            "  adding: vit-base-mask-finetuned/runs/Feb17_12-52-55_0ef67e2ff67c/events.out.tfevents.1645102429.0ef67e2ff67c.84.0 (deflated 58%)\n",
            "  adding: vit-base-mask-finetuned/runs/Feb17_12-52-55_0ef67e2ff67c/1645102429.3300188/ (stored 0%)\n",
            "  adding: vit-base-mask-finetuned/runs/Feb17_12-52-55_0ef67e2ff67c/1645102429.3300188/events.out.tfevents.1645102429.0ef67e2ff67c.84.1 (deflated 62%)\n",
            "  adding: vit-base-mask-finetuned/runs/Feb17_13-36-51_0ef67e2ff67c/ (stored 0%)\n",
            "  adding: vit-base-mask-finetuned/runs/Feb17_13-36-51_0ef67e2ff67c/events.out.tfevents.1645105012.0ef67e2ff67c.84.2 (deflated 65%)\n",
            "  adding: vit-base-mask-finetuned/runs/Feb17_13-36-51_0ef67e2ff67c/1645105012.531838/ (stored 0%)\n",
            "  adding: vit-base-mask-finetuned/runs/Feb17_13-36-51_0ef67e2ff67c/1645105012.531838/events.out.tfevents.1645105012.0ef67e2ff67c.84.3 (deflated 62%)\n",
            "  adding: vit-base-mask-finetuned/runs/Feb17_13-36-51_0ef67e2ff67c/events.out.tfevents.1645107987.0ef67e2ff67c.84.4 (deflated 27%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-500/ (stored 0%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-500/config.json (deflated 46%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-500/pytorch_model.bin (deflated 7%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-500/preprocessor_config.json (deflated 46%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-500/training_args.bin (deflated 49%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-500/optimizer.pt (deflated 7%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-500/scheduler.pt (deflated 49%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-500/scaler.pt (deflated 55%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-500/trainer_state.json (deflated 81%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-500/rng_state.pth (deflated 27%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-600/ (stored 0%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-600/config.json (deflated 46%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-600/pytorch_model.bin (deflated 7%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-600/preprocessor_config.json (deflated 46%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-600/training_args.bin (deflated 49%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-600/optimizer.pt (deflated 7%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-600/scheduler.pt (deflated 49%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-600/scaler.pt (deflated 55%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-600/trainer_state.json (deflated 82%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-600/rng_state.pth (deflated 27%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-700/ (stored 0%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-700/config.json (deflated 46%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-700/pytorch_model.bin (deflated 7%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-700/preprocessor_config.json (deflated 46%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-700/training_args.bin (deflated 49%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-700/optimizer.pt (deflated 7%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-700/scheduler.pt (deflated 49%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-700/scaler.pt (deflated 55%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-700/trainer_state.json (deflated 82%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-700/rng_state.pth (deflated 27%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-800/ (stored 0%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-800/config.json (deflated 46%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-800/pytorch_model.bin (deflated 7%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-800/preprocessor_config.json (deflated 46%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-800/training_args.bin (deflated 49%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-800/optimizer.pt (deflated 7%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-800/scheduler.pt (deflated 49%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-800/scaler.pt (deflated 55%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-800/trainer_state.json (deflated 83%)\n",
            "  adding: vit-base-mask-finetuned/checkpoint-800/rng_state.pth (deflated 27%)\n",
            "  adding: vit-base-mask-finetuned/config.json (deflated 46%)\n",
            "  adding: vit-base-mask-finetuned/pytorch_model.bin (deflated 7%)\n",
            "  adding: vit-base-mask-finetuned/preprocessor_config.json (deflated 46%)\n",
            "  adding: vit-base-mask-finetuned/training_args.bin (deflated 49%)\n",
            "  adding: vit-base-mask-finetuned/train_results.json (deflated 37%)\n",
            "  adding: vit-base-mask-finetuned/all_results.json (deflated 51%)\n",
            "  adding: vit-base-mask-finetuned/trainer_state.json (deflated 83%)\n",
            "  adding: vit-base-mask-finetuned/eval_results.json (deflated 38%)\n"
          ]
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      ],
      "source": [
        "!zip -r vit-base-mask-finetuned.zip vit-base-mask-finetuned/"
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