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      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.21.0-py3-none-any.whl.metadata (21 kB)\n",
            "Collecting clip\n",
            "  Downloading clip-0.2.0.tar.gz (5.5 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.15.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n",
            "Collecting pyarrow>=15.0.0 (from datasets)\n",
            "  Downloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (3.3 kB)\n",
            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.1.4)\n",
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            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "Collecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n",
            "Requirement already satisfied: fsspec<=2024.6.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<=2024.6.1,>=2023.1.0->datasets) (2024.6.1)\n",
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            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.1)\n",
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            "Downloading datasets-2.21.0-py3-none-any.whl (527 kB)\n",
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            "\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m14.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hBuilding wheels for collected packages: clip\n",
            "  Building wheel for clip (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for clip: filename=clip-0.2.0-py3-none-any.whl size=6989 sha256=78b055755f92c04762d2827512a0ca44123292e524b2e844c7a70dbd13eef787\n",
            "  Stored in directory: /root/.cache/pip/wheels/7f/5c/e6/2c0fdb453a3569188864b17e9676bea8b3b7e160c037117869\n",
            "Successfully built clip\n",
            "Installing collected packages: clip, xxhash, pyarrow, dill, multiprocess, datasets\n",
            "  Attempting uninstall: pyarrow\n",
            "    Found existing installation: pyarrow 14.0.2\n",
            "    Uninstalling pyarrow-14.0.2:\n",
            "      Successfully uninstalled pyarrow-14.0.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed clip-0.2.0 datasets-2.21.0 dill-0.3.8 multiprocess-0.70.16 pyarrow-17.0.0 xxhash-3.5.0\n"
          ]
        }
      ],
      "source": [
        "pip install datasets clip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "from datasets import load_dataset\n",
        "from tqdm.auto import tqdm\n",
        "from transformers import AutoProcessor, CLIPModel, AutoTokenizer\n",
        "from sklearn.metrics import accuracy_score\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ],
      "metadata": {
        "id": "oDPmmPHVT-XG"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "model = CLIPModel.from_pretrained(\"openai/clip-vit-large-patch14\").to(device)\n",
        "processor = AutoProcessor.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\")\n"
      ],
      "metadata": {
        "colab": {
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        "id": "G2YUGlAWT_IL",
        "outputId": "d10af4ff-19f4-46d7-c731-ef0c6417b291"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/4.52k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        {
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          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.71G [00:00<?, ?B/s]"
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      "cell_type": "code",
      "source": [
        "imagenette = load_dataset(\n",
        "    'frgfm/imagenette',\n",
        "    '320px',\n",
        "    split='validation',\n",
        "    revision=\"4d512db\"\n",
        ")"
      ],
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        "id": "XkNNZ9XKUFUy",
        "outputId": "d5e121cb-d27a-4cf6-f862-c63e1d1d2d0c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/5.31k [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/7.25k [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/4.65k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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              "model_id": "e5f5f08e8977485c9a08fe028a8c4b2d"
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          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The repository for frgfm/imagenette contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/frgfm/imagenette.\n",
            "You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n",
            "\n",
            "Do you wish to run the custom code? [y/N] y\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/342M [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "46949ed161f446d095721951f472a528"
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          "metadata": {}
        },
        {
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            ],
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          },
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        },
        {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/9469 [00:00<?, ? examples/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/3925 [00:00<?, ? examples/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e1b0559a0314ffcb6bf5e4a054464b6"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = imagenette.features[\"label\"].names\n",
        "print(f\"Class labels in dataset: {labels}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Go7r4aBrUHH_",
        "outputId": "e064ce3c-4b95-43bb-e2d7-d82d29603a70"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset features: {'image': Image(mode=None, decode=True, id=None), 'label': ClassLabel(names=['tench', 'English springer', 'cassette player', 'chain saw', 'church', 'French horn', 'garbage truck', 'gas pump', 'golf ball', 'parachute'], id=None)}\n",
            "Number of images in dataset: 3925\n",
            "Class labels in dataset: ['tench', 'English springer', 'cassette player', 'chain saw', 'church', 'French horn', 'garbage truck', 'gas pump', 'golf ball', 'parachute']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example data for class distribution\n",
        "labels = ['tench', 'English springer', 'cassette player', 'chain saw', 'church', 'French horn', 'garbage truck', 'gas pump', 'golf ball', 'parachute']\n",
        "class_counts = [400, 300, 150, 450, 500, 200, 350, 250, 300, 200]\n",
        "\n",
        "# Plotting the class distribution\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x=labels, y=class_counts, palette='viridis')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.title('Class Distribution in Imagenette Dataset')\n",
        "plt.xlabel('Class Labels')\n",
        "plt.ylabel('Number of Images')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 746
        },
        "id": "Qig3T0SFUIZO",
        "outputId": "d0b8ee60-07e3-4e34-92e1-9bacf953f4da"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-c26b3e544194>:7: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=labels, y=class_counts, palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "selected_images = []\n",
        "selected_labels = []\n",
        "\n",
        "for example in tqdm(imagenette):\n",
        "    label = example[\"label\"]\n",
        "    selected_images.append(example[\"image\"])\n",
        "    selected_labels.append(label)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "d142ea7e25ed47728885b00935afb721",
            "05fa95ecc8044525a4419a6cd489a2a2",
            "4fa5202f0cf2472c98286c547798118f",
            "926bc95c208b47e8816c3e29626550cd",
            "6c4c436a5dd1406ba378096c20724fa6",
            "84e2bd7da6f447de8fb7d53c2df42087",
            "f90b6f9bd9b64244831b4379fadb8d4c",
            "2590308cc8914a05aa794e487836a63c",
            "a8df7630931740ac8fa8050543113df6",
            "3baffde724e5482b872ab3e0c0cbabff",
            "40c6b36bfe82415d9ddd40eecda4d26d"
          ]
        },
        "id": "Kn-fF864UJm5",
        "outputId": "bcd97bee-00bd-4b6d-886b-9af6b1aef572"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3925 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d142ea7e25ed47728885b00935afb721"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_inputs = tokenizer([f\"a photo of a {c}\" for c in labels], return_tensors=\"pt\", padding=True).to(device)\n"
      ],
      "metadata": {
        "id": "aHGpt23iULFD"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.no_grad():\n",
        "    label_emb = model.get_text_features(input_ids=text_inputs['input_ids'], attention_mask=text_inputs['attention_mask'])\n",
        "    label_emb = label_emb.cpu().numpy()\n"
      ],
      "metadata": {
        "id": "rfazmm7OUMON"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preds = []\n",
        "batch_size = 50\n",
        "\n",
        "for i in tqdm(range(0, len(selected_images), batch_size)):\n",
        "    i_end = min(i + batch_size, len(selected_images))\n",
        "    images = processor(\n",
        "        images=selected_images[i:i_end],\n",
        "        return_tensors='pt'\n",
        "    )['pixel_values'].to(device)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        img_emb = model.get_image_features(images)\n",
        "        img_emb = img_emb.cpu().numpy()\n",
        "\n",
        "    # Calculate similarity scores between image embeddings and text embeddings\n",
        "    scores = np.dot(img_emb, label_emb.T)\n",
        "    preds.extend(np.argmax(scores, axis=1))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "10a7c93bf01147528f4d39fdd191fe73",
            "a91a7c114b5c491cadd6ab3831d38842",
            "5babbdab569e49daba905a3ab5157c3e",
            "c60f11abcd454a8aa0afc04beb2c7843",
            "fed561ac48064ee8b95059071c5f6513",
            "5ef6de14f1ed4dabab37495400cea74a",
            "a48b4284462748d49e356723d92048ca",
            "df9fc2b424714319bb9a4904c38757cb",
            "3cc77da171bc420594083ca1c5f69b7c",
            "b955bd264b1a4393b990208371ea6145",
            "ba603c1c9bc148d0ace2a3158c04f2cc"
          ]
        },
        "id": "VFtmFooXUNVu",
        "outputId": "81bd58f6-fd37-4b8d-d4a5-29e71898a955"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "10a7c93bf01147528f4d39fdd191fe73"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(selected_labels, preds)\n",
        "print(f\"Zero-shot classification accuracy on Imagenette: {accuracy * 100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0-R2uT3NUOa7",
        "outputId": "bf08ed00-bf22-4375-95db-b0c71a679139"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Zero-shot classification accuracy on Imagenette: 99.36%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Total number of images processed: {len(selected_images)}\")\n",
        "class_counts = {labels[i]: selected_labels.count(i) for i in range(len(labels))}\n",
        "print(f\"Number of images per class: {class_counts}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P_7dQNh1UPbG",
        "outputId": "a10cc37b-74f0-4254-848a-7bc402dea519"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of images processed: 3925\n",
            "Number of images per class: {'tench': 387, 'English springer': 395, 'cassette player': 357, 'chain saw': 386, 'church': 409, 'French horn': 394, 'garbage truck': 389, 'gas pump': 419, 'golf ball': 399, 'parachute': 390}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "f1Ig9UcnUQoO"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}