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\n" }, "metadata": {}, "execution_count": 4 } ] }, { "cell_type": "code", "source": [ "label = ds['train'].features['label']\n", "label" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OvnlPDrfH4UN", "outputId": "e3e70311-8506-49ff-e99f-426bc842e96b" }, "execution_count": 5, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "ClassLabel(num_classes=20, names=['apple', 'banana', 'cake', 'candy', 'carrot', 'cookie', 'doughnut', 'grape', 'hot dog', 'ice cream', 'juice', 'muffin', 'orange', 'pineapple', 'popcorn', 'pretzel', 'salad', 'strawberry', 'waffle', 'watermelon'], id=None)" ] }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "code", "source": [ "print('No of images in Train dataset ', len(ds['train']))\n", "print('No of images in Test dataset ', len(ds['test']))\n", "print('No of images in Validation dataset ', len(ds['validation']))\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hNw9x7_YLG1C", "outputId": "bb1faf60-f888-4d5c-c806-3347cac6e1b8" }, "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "No of images in Train dataset 4838\n", "No of images in Test dataset 952\n", "No of images in Validation dataset 955\n" ] } ] }, { "cell_type": "code", "source": [ "label.int2str(eg['label'])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 36 }, "id": "wQkxlcUpIRb1", "outputId": "327d64db-6e9d-4b7b-9a78-ce3ecb9267a3" }, "execution_count": 7, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'grape'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 7 } ] }, { "cell_type": "code", "source": [ "\"\"\"\n", "from transformers.utils.dummy_vision_objects import ImageGPTFeatureExtractor\n", "import random\n", "from PIL import ImageDraw, ImageFont, Image\n", "\n", "def show_examples(ds, seed:int=1234, egs_per_class: int = 2, size=(224,224)):\n", " w, h =size\n", " labels = ds['train'].features['label'].names\n", " grid = Image.new('RGB', size = (egs_per_class*w, len(labels) * h))\n", " draw = ImageDraw.Draw(grid)\n", "\n", "\n", " for label_id, label in enumerate(labels):\n", " ds_slice = ds['train'].filter(lambda eg : eg['label'] == label_id).shuffle(seed).select(range(egs_per_class))\n", "\n", "\n", " for i, example in enumerate(ds_slice):\n", " image = example['image']\n", " idx = egs_per_class * label_id * i\n", " box = (idx % egs_per_class * w, idx // egs_per_class *h)\n", " grid.paste(image.resize(size), box=box)\n", " draw.text(box, label, (255, 255, 255))\n", "\n", " return grid\n", "\n", "show_examples(ds, seed=random.randint(0,1337), egs_per_class=2)\n", "\n", "\"\"\"\n", "\n", "\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 157 }, "id": "OJfwHNDNJLCz", "outputId": "2138e1e4-e987-425e-e196-2e966b5c4354" }, "execution_count": 8, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "\"\\nfrom transformers.utils.dummy_vision_objects import ImageGPTFeatureExtractor\\nimport random\\nfrom PIL import ImageDraw, ImageFont, Image\\n\\ndef show_examples(ds, seed:int=1234, egs_per_class: int = 2, size=(224,224)):\\n w, h =size\\n labels = ds['train'].features['label'].names\\n grid = Image.new('RGB', size = (egs_per_class*w, len(labels) * h))\\n draw = ImageDraw.Draw(grid)\\n\\n\\n for label_id, label in enumerate(labels):\\n ds_slice = ds['train'].filter(lambda eg : eg['label'] == label_id).shuffle(seed).select(range(egs_per_class))\\n\\n\\n for i, example in enumerate(ds_slice):\\n image = example['image']\\n idx = egs_per_class * label_id * i\\n box = (idx % egs_per_class * w, idx // egs_per_class *h)\\n grid.paste(image.resize(size), box=box)\\n draw.text(box, label, (255, 255, 255))\\n\\n return grid\\n\\nshow_examples(ds, seed=random.randint(0,1337), egs_per_class=2)\\n\\n\"" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 8 } ] }, { "cell_type": "code", "source": [ "from transformers import ViTFeatureExtractor\n", "model_name = 'google/vit-base-patch16-224-in21k'\n", "feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)" ], "metadata": { "id": "zVUNylhiWR9B" }, "execution_count": 9, "outputs": [] }, { "cell_type": "code", "source": [ "feature_extractor" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "FuCjEsY40dJI", "outputId": "a5ed3122-1f51-479c-ff36-5b5c633ba8b5" }, "execution_count": 10, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "ViTFeatureExtractor {\n", " \"do_normalize\": true,\n", " \"do_resize\": true,\n", " \"feature_extractor_type\": \"ViTFeatureExtractor\",\n", " \"image_mean\": [\n", " 0.5,\n", " 0.5,\n", " 0.5\n", " ],\n", " \"image_std\": [\n", " 0.5,\n", " 0.5,\n", " 0.5\n", " ],\n", " \"resample\": 2,\n", " \"size\": 224\n", "}" ] }, "metadata": {}, "execution_count": 10 } ] }, { "cell_type": "code", "source": [ "feature_extractor(image, return_tensors='pt')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "c6KOV6xw0gcP", "outputId": "a9f4d704-f870-4f32-9dd1-e5a91a5cea11" }, "execution_count": 11, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'pixel_values': tensor([[[[ 0.4431, 0.4667, 0.4667, ..., 0.9843, 0.9608, 0.9686],\n", " [ 0.4667, 0.4745, 0.4588, ..., 0.9843, 0.9608, 0.9686],\n", " [ 0.4745, 0.4667, 0.4353, ..., 0.9765, 0.9686, 0.9765],\n", " ...,\n", " [-0.2157, -0.4275, -0.5529, ..., 0.7176, 0.7333, 0.7176],\n", " [-0.2157, -0.4118, -0.5608, ..., 0.6314, 0.6314, 0.6000],\n", " [-0.1608, -0.3882, -0.5686, ..., 0.5765, 0.5529, 0.5059]],\n", "\n", " [[ 0.3333, 0.3569, 0.3569, ..., 0.5686, 0.5529, 0.5608],\n", " [ 0.3569, 0.3647, 0.3490, ..., 0.5216, 0.4980, 0.5137],\n", " [ 0.3647, 0.3569, 0.3255, ..., 0.4196, 0.4118, 0.4275],\n", " ...,\n", " [-0.4118, -0.6314, -0.7569, ..., 0.2235, 0.2549, 0.2627],\n", " [-0.4196, -0.6235, -0.7725, ..., 0.1216, 0.1373, 0.1216],\n", " [-0.3961, -0.6000, -0.7804, ..., 0.0667, 0.0510, 0.0275]],\n", "\n", " [[-0.4275, -0.4039, -0.4039, ..., -0.7961, -0.8196, -0.8275],\n", " [-0.4039, -0.3961, -0.4118, ..., -0.8196, -0.8431, -0.8510],\n", " [-0.3961, -0.4039, -0.4353, ..., -0.8745, -0.8902, -0.8980],\n", " ...,\n", " [-0.5922, -0.8118, -0.9294, ..., -0.4588, -0.4275, -0.4039],\n", " [-0.6235, -0.8196, -0.9686, ..., -0.5686, -0.5451, -0.5529],\n", " [-0.6000, -0.8118, -0.9843, ..., -0.6392, -0.6314, -0.6471]]]])}" ] }, "metadata": {}, "execution_count": 11 } ] }, { "cell_type": "code", "source": [ "def process_example(example):\n", " inputs = feature_extractor(example['image'], return_tensors='pt')\n", " inputs['label'] = example['label']\n", " return inputs" ], "metadata": { "id": "sYAdPE6w0qgP" }, "execution_count": 12, "outputs": [] }, { "cell_type": "code", "source": [ "process_example(ds['train'][0])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UTdFlT2P2Fm4", "outputId": "1ae9488a-17fb-41a0-ec5d-6b86c221e5d2" }, "execution_count": 13, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'pixel_values': tensor([[[[-0.5294, -0.7490, -0.8353, ..., -0.7255, -0.6157, -0.6471],\n", " [-0.6314, -0.8431, -0.8275, ..., -0.5529, -0.6157, -0.6471],\n", " [-0.7098, -0.8118, -0.6784, ..., -0.4902, -0.6941, -0.7098],\n", " ...,\n", " [-0.8902, -0.8039, -0.7647, ..., -0.9373, -0.8588, -0.8902],\n", " [-0.7882, -0.7333, -0.7176, ..., -0.9529, -0.8824, -0.9294],\n", " [-0.6000, -0.6314, -0.6549, ..., -0.9529, -0.8667, -0.9216]],\n", "\n", " [[ 0.2549, 0.1137, 0.1059, ..., -0.4039, -0.3098, -0.3804],\n", " [ 0.1765, 0.0275, 0.0667, ..., -0.2235, -0.3176, -0.3882],\n", " [ 0.1451, 0.0745, 0.1529, ..., -0.1608, -0.3882, -0.4431],\n", " ...,\n", " [-0.2392, -0.1451, -0.0667, ..., -0.4588, -0.3569, -0.3804],\n", " [-0.0980, -0.0353, 0.0196, ..., -0.4745, -0.3804, -0.4196],\n", " [ 0.1059, 0.0902, 0.0902, ..., -0.4588, -0.3569, -0.4118]],\n", "\n", " [[ 1.0000, 0.9765, 0.9059, ..., -0.4745, -0.4431, -0.5373],\n", " [ 0.9922, 0.8745, 0.8902, ..., -0.3098, -0.4431, -0.5216],\n", " [ 0.9765, 0.8824, 0.9529, ..., -0.2471, -0.5059, -0.5686],\n", " ...,\n", " [ 0.6784, 0.7804, 0.8745, ..., 0.2706, 0.3647, 0.3412],\n", " [ 0.8431, 0.9137, 0.9686, ..., 0.2627, 0.3569, 0.3098],\n", " [ 0.9843, 0.9922, 1.0000, ..., 0.2784, 0.3804, 0.3255]]]]), 'label': 7}" ] }, "metadata": {}, "execution_count": 13 } ] }, { "cell_type": "code", "source": [ "def transform(example_batch):\n", " inputs = feature_extractor([x for x in example_batch['image']], return_tensors='pt')\n", "\n", " inputs['label'] = example_batch['label']\n", " return inputs\n", "\n", "prepared_ds = ds.with_transform(transform)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7toiKhsp2P9X", "outputId": "eef8bbc5-e332-4856-f989-5973c59ca9c8" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Parameter 'transform'= of the transform datasets.arrow_dataset.Dataset.set_format couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n" ] } ] }, { "cell_type": "code", "source": [ "prepared_ds['train'][0:2]" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "FP11fVbD24Xw", "outputId": "af0f4eb7-069d-4a8a-c9ce-ef7abb938f11" }, "execution_count": 15, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'pixel_values': tensor([[[[-0.5294, -0.7490, -0.8353, ..., -0.7255, -0.6157, -0.6471],\n", " [-0.6314, -0.8431, -0.8275, ..., -0.5529, -0.6157, -0.6471],\n", " [-0.7098, -0.8118, -0.6784, ..., -0.4902, -0.6941, -0.7098],\n", " ...,\n", " [-0.8902, -0.8039, -0.7647, ..., -0.9373, -0.8588, -0.8902],\n", " [-0.7882, -0.7333, -0.7176, ..., -0.9529, -0.8824, -0.9294],\n", " [-0.6000, -0.6314, -0.6549, ..., -0.9529, -0.8667, -0.9216]],\n", "\n", " [[ 0.2549, 0.1137, 0.1059, ..., -0.4039, -0.3098, -0.3804],\n", " [ 0.1765, 0.0275, 0.0667, ..., -0.2235, -0.3176, -0.3882],\n", " [ 0.1451, 0.0745, 0.1529, ..., -0.1608, -0.3882, 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..., 0.3333, 0.1529, -0.0039]],\n", "\n", " [[ 0.2706, 0.2549, 0.2078, ..., -0.1529, -0.1216, -0.1059],\n", " [ 0.2157, 0.2314, 0.2157, ..., -0.1451, -0.1294, -0.1059],\n", " [ 0.2235, 0.2471, 0.2471, ..., -0.1373, -0.1216, -0.1059],\n", " ...,\n", " [-0.0588, -0.0588, -0.0510, ..., 0.4980, 0.3333, 0.1843],\n", " [-0.0824, -0.0745, -0.0588, ..., 0.4431, 0.2706, 0.1137],\n", " [-0.1059, -0.0980, -0.0824, ..., 0.3647, 0.1922, 0.0353]],\n", "\n", " [[-0.1843, -0.1765, -0.1765, ..., -0.4980, -0.4745, -0.4667],\n", " [-0.2392, -0.2000, -0.1608, ..., -0.4980, -0.4824, -0.4667],\n", " [-0.2235, -0.1843, -0.1294, ..., -0.4902, -0.4824, -0.4667],\n", " ...,\n", " [-0.1529, -0.1529, -0.1451, ..., 0.5294, 0.3333, 0.1765],\n", " [-0.1765, -0.1686, -0.1529, ..., 0.4510, 0.2627, 0.0902],\n", " [-0.2000, -0.1922, -0.1765, ..., 0.3569, 0.1686, -0.0039]]]]), 'label': [7, 7]}" ] }, "metadata": {}, "execution_count": 15 } ] }, { "cell_type": "code", "source": [ "import torch\n", "\n", "def collate_fn(examples):\n", " pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n", " labels = torch.tensor([example[\"label\"] for example in examples])\n", " return {\"pixel_values\": pixel_values, \"labels\": labels}\n", "\n" ], "metadata": { "id": "yYf8yUs14zTL" }, "execution_count": 16, "outputs": [] }, { "cell_type": "code", "source": [ "import numpy as np\n", "from datasets import load_metric\n", "\n", "metric = load_metric('accuracy')\n", "\n", "def compute_metrics(eval_pred):\n", " predictions, labels = eval_pred\n", " predictions = np.argmax(predictions, axis=1)\n", " return metric.compute(predictions=predictions, references=labels)" ], "metadata": { "id": "SJanrSQs7cMH" }, "execution_count": 17, "outputs": [] }, { "cell_type": "code", "source": [ "from transformers import ViTForImageClassification\n", "\n", "label = ds['train'].features['label'].names\n", "\n", "model = ViTForImageClassification.from_pretrained(model_name,\n", " num_labels=20,\n", " id2label={str(i): c for i,c in enumerate(label)},\n", " label2id={c:str(i) for i,c in enumerate(label)})" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xGxVdr-i9H4e", "outputId": "443614aa-04c0-4e23-b65b-9f4426e821c7" }, "execution_count": 19, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Some weights of the model checkpoint at google/vit-base-patch16-224-in21k were not used when initializing ViTForImageClassification: ['pooler.dense.weight', 'pooler.dense.bias']\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.weight', 'classifier.bias']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ] } ] }, { "cell_type": "code", "source": [ "from transformers import TrainingArguments\n", "\n", "training_args = TrainingArguments(\n", " f\"vit-snacks\",\n", " per_device_train_batch_size = 16,\n", " evaluation_strategy='steps',\n", " num_train_epochs=5,\n", " save_steps=100,\n", " eval_steps=100,\n", " logging_steps =10,\n", " learning_rate=2e-4,\n", " save_total_limit=2,\n", " remove_unused_columns=False,\n", " push_to_hub=True,\n", " report_to='tensorboard',\n", " load_best_model_at_end=True,\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bnI-Agzw-t8v", "outputId": "c325ef96-7cad-4795-ed5d-b9eee685062c" }, "execution_count": 29, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "PyTorch: setting up devices\n" ] } ] }, { "cell_type": "code", "source": [ "%%capture\n", "! pip install huggingface_hub\n", "! git config --global credential.helper store" ], "metadata": { "id": "wPt9Pj1K4HiO" }, "execution_count": 21, "outputs": [] }, { "cell_type": "code", "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 327, "referenced_widgets": [ "ca7c3a5fba644a508f786cf5c5a7999a", "facf2022580948299453af6b562fe1c4", "4d40c583c368402fbc3c984074d52c53", "3b7304f9f56041d986ea23a97f6a296b", "34bb5eed32714ceca08d6f7e9f7782ca", "5a339252ae104cd98bc8aeba104f617c", "0211a0640ec440a6a97460916e5d4756", "440895a9c85a4c4aa9b0d00acad0601e", "afeace46391f4b0f9d20a77e4067ccd6", "c861cc52460741b8bc9a01069bdb2af4", "86726d91174f4880b6366c994a2aeb92", "5c239cfd89c942e8b24782134c2721e4", "aeff36a5c03747b1a3bacbba885ce25f", "78a6f99eddd64d52ac8be816012b3196" ] }, "id": "2P7spcp1377u", "outputId": "7db3c410-9608-46bc-897b-16b613d1e4ef" }, "execution_count": 22, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='
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StepTraining LossValidation LossAccuracy
1000.8764000.9601470.840838
2000.6548000.7472120.833508
3000.4724000.5027570.878534
4000.2764000.4742960.880628
5000.2529000.4472880.883770
6000.1179000.4663290.878534
7000.0666000.4181980.897382
8000.0548000.4054320.902618
9000.0956000.3497260.917277
10000.0177000.3167500.912042
11000.0094000.3726280.909948
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13000.0066000.3361320.923560
14000.0063000.3309040.923560
15000.0062000.3278780.922513

" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "***** Running Evaluation *****\n", " Num examples = 955\n", " Batch size = 8\n", "Saving model checkpoint to vit-snacks/checkpoint-100\n", "Configuration saved in vit-snacks/checkpoint-100/config.json\n", "Model weights saved in vit-snacks/checkpoint-100/pytorch_model.bin\n", "Feature extractor saved in vit-snacks/checkpoint-100/preprocessor_config.json\n", "***** Running Evaluation *****\n", " Num examples = 955\n", " Batch size = 8\n", "Saving model checkpoint to vit-snacks/checkpoint-200\n", "Configuration saved in vit-snacks/checkpoint-200/config.json\n", "Model weights saved in vit-snacks/checkpoint-200/pytorch_model.bin\n", "Feature extractor saved in vit-snacks/checkpoint-200/preprocessor_config.json\n", "***** Running Evaluation *****\n", " Num examples = 955\n", " Batch size = 8\n", "Saving model checkpoint to vit-snacks/checkpoint-300\n", "Configuration saved in vit-snacks/checkpoint-300/config.json\n", "Model weights saved in vit-snacks/checkpoint-300/pytorch_model.bin\n", "Feature extractor saved in vit-snacks/checkpoint-300/preprocessor_config.json\n", "Deleting older checkpoint [vit-snacks/checkpoint-100] due to args.save_total_limit\n", "***** Running Evaluation *****\n", " Num examples = 955\n", " Batch size = 8\n", "Saving model checkpoint to vit-snacks/checkpoint-400\n", "Configuration saved in vit-snacks/checkpoint-400/config.json\n", "Model weights saved in vit-snacks/checkpoint-400/pytorch_model.bin\n", "Feature extractor saved in vit-snacks/checkpoint-400/preprocessor_config.json\n", "Deleting older checkpoint [vit-snacks/checkpoint-200] due to args.save_total_limit\n", "***** Running Evaluation *****\n", " Num examples = 955\n", " Batch size = 8\n", "Saving model checkpoint to vit-snacks/checkpoint-500\n", "Configuration saved in vit-snacks/checkpoint-500/config.json\n", "Model weights saved in vit-snacks/checkpoint-500/pytorch_model.bin\n", "Feature extractor saved in vit-snacks/checkpoint-500/preprocessor_config.json\n", "Deleting older checkpoint [vit-snacks/checkpoint-300] due to args.save_total_limit\n", "***** Running Evaluation *****\n", " Num examples = 955\n", " Batch size = 8\n", "Saving model checkpoint to vit-snacks/checkpoint-600\n", "Configuration saved in vit-snacks/checkpoint-600/config.json\n", "Model weights saved in vit-snacks/checkpoint-600/pytorch_model.bin\n", "Feature extractor saved in vit-snacks/checkpoint-600/preprocessor_config.json\n", "Deleting older checkpoint [vit-snacks/checkpoint-400] due to args.save_total_limit\n", "***** Running Evaluation *****\n", " Num examples = 955\n", " Batch size = 8\n", "Saving model checkpoint to vit-snacks/checkpoint-700\n", "Configuration saved in vit-snacks/checkpoint-700/config.json\n", "Model weights saved in vit-snacks/checkpoint-700/pytorch_model.bin\n", "Feature extractor saved in vit-snacks/checkpoint-700/preprocessor_config.json\n", "Deleting older checkpoint [vit-snacks/checkpoint-500] due to args.save_total_limit\n", "***** Running Evaluation *****\n", " Num examples = 955\n", " Batch size = 8\n", "Saving model checkpoint to vit-snacks/checkpoint-800\n", "Configuration saved in vit-snacks/checkpoint-800/config.json\n", "Model weights saved in vit-snacks/checkpoint-800/pytorch_model.bin\n", "Feature extractor saved in vit-snacks/checkpoint-800/preprocessor_config.json\n", "Deleting older checkpoint [vit-snacks/checkpoint-600] due to args.save_total_limit\n", "***** Running Evaluation *****\n", " Num examples = 955\n", " Batch size = 8\n", "Saving model checkpoint to vit-snacks/checkpoint-900\n", "Configuration saved in vit-snacks/checkpoint-900/config.json\n", "Model weights saved in vit-snacks/checkpoint-900/pytorch_model.bin\n", "Feature extractor saved in vit-snacks/checkpoint-900/preprocessor_config.json\n", "Deleting older checkpoint [vit-snacks/checkpoint-700] due to args.save_total_limit\n", "***** Running Evaluation *****\n", " Num examples = 955\n", " Batch size = 8\n", "Saving model checkpoint to vit-snacks/checkpoint-1000\n", "Configuration saved in vit-snacks/checkpoint-1000/config.json\n", "Model weights saved in vit-snacks/checkpoint-1000/pytorch_model.bin\n", "Feature extractor saved in vit-snacks/checkpoint-1000/preprocessor_config.json\n", "Deleting older checkpoint [vit-snacks/checkpoint-800] due to args.save_total_limit\n", "***** Running Evaluation *****\n", " Num examples = 955\n", " Batch size = 8\n", "Saving model checkpoint to vit-snacks/checkpoint-1100\n", "Configuration saved in vit-snacks/checkpoint-1100/config.json\n", "Model weights saved in vit-snacks/checkpoint-1100/pytorch_model.bin\n", "Feature extractor saved in vit-snacks/checkpoint-1100/preprocessor_config.json\n", "Deleting older checkpoint [vit-snacks/checkpoint-900] due to args.save_total_limit\n", "***** Running Evaluation *****\n", " Num examples = 955\n", " Batch size = 8\n", "Saving model checkpoint to vit-snacks/checkpoint-1200\n", "Configuration saved in vit-snacks/checkpoint-1200/config.json\n", "Model weights saved in vit-snacks/checkpoint-1200/pytorch_model.bin\n", "Feature extractor saved in vit-snacks/checkpoint-1200/preprocessor_config.json\n", "Deleting older checkpoint [vit-snacks/checkpoint-1100] due to args.save_total_limit\n", "***** Running Evaluation *****\n", " Num examples = 955\n", " Batch size = 8\n", "Saving model checkpoint to vit-snacks/checkpoint-1300\n", "Configuration saved in vit-snacks/checkpoint-1300/config.json\n", "Model weights saved in vit-snacks/checkpoint-1300/pytorch_model.bin\n", "Feature extractor saved in vit-snacks/checkpoint-1300/preprocessor_config.json\n", "Deleting older checkpoint [vit-snacks/checkpoint-1200] due to args.save_total_limit\n", "***** Running Evaluation *****\n", " Num examples = 955\n", " Batch size = 8\n", "Saving model checkpoint to vit-snacks/checkpoint-1400\n", "Configuration saved in vit-snacks/checkpoint-1400/config.json\n", "Model weights saved in vit-snacks/checkpoint-1400/pytorch_model.bin\n", "Feature extractor saved in vit-snacks/checkpoint-1400/preprocessor_config.json\n", "Deleting older checkpoint [vit-snacks/checkpoint-1300] due to args.save_total_limit\n", "***** Running Evaluation *****\n", " Num examples = 955\n", " Batch size = 8\n", "Saving model checkpoint to vit-snacks/checkpoint-1500\n", "Configuration saved in vit-snacks/checkpoint-1500/config.json\n", "Model weights saved in vit-snacks/checkpoint-1500/pytorch_model.bin\n", "Feature extractor saved in vit-snacks/checkpoint-1500/preprocessor_config.json\n", "Deleting older checkpoint [vit-snacks/checkpoint-1400] 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-snacks/checkpoint-1000 (score: 0.316749632358551).\n", "Saving model checkpoint to vit-snacks\n", "Configuration saved in vit-snacks/config.json\n", "Model weights saved in vit-snacks/pytorch_model.bin\n", "Feature extractor saved in vit-snacks/preprocessor_config.json\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "***** train metrics *****\n", " epoch = 5.0\n", " total_flos = 1746074896GF\n", " train_loss = 0.2606\n", " train_runtime = 0:19:45.83\n", " train_samples_per_second = 20.399\n", " train_steps_per_second = 1.278\n" ] } ] }, { "cell_type": "code", "source": [ "metrics = trainer.evaluate(prepared_ds['validation'])\n", "trainer.log_metrics('eval',metrics)\n", "trainer.save_metrics('eval', metrics)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 211 }, "id": "9bFxocoW2pbf", "outputId": "70497464-390a-4ea6-bd49-36ac22b11513" }, "execution_count": 26, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "***** Running Evaluation *****\n", " Num examples = 955\n", " Batch size = 8\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

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\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "***** eval metrics *****\n", " epoch = 5.0\n", " eval_accuracy = 0.912\n", " eval_loss = 0.3167\n", " eval_runtime = 0:00:14.32\n", " eval_samples_per_second = 66.648\n", " eval_steps_per_second = 8.375\n" ] } ] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "F7rLIPIO54-N" }, "execution_count": 37, "outputs": [] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "TeU4RPptGn01" }, "execution_count": 37, "outputs": [] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "Yxsqm2G-HXS8" }, "execution_count": 37, "outputs": [] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "0HgUv-3mENL-" }, "execution_count": 37, "outputs": [] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "zP3HoN6EGcuG" }, "execution_count": null, "outputs": [] } ] }