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\u001b[31m23.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m15.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h" ] } ] }, { "cell_type": "code", "source": [ "# Standard library imports\n", "import warnings\n", "\n", "# Third-party imports\n", "import evaluate\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import torch\n", "import torch.nn as nn\n", "from datasets import load_dataset\n", "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, f1_score\n", "from torchvision.transforms import Compose, Normalize, RandomResizedCrop, ToTensor\n", "from transformers import (\n", " AutoImageProcessor,\n", " AutoModelForImageClassification,\n", " DefaultDataCollator,\n", " Trainer,\n", " TrainingArguments,\n", " logging,\n", " pipeline,\n", ")\n", "\n", "# Disabling warnings\n", "warnings.filterwarnings(\"ignore\")\n", "logging.set_verbosity_error()" ], "metadata": { "id": "3uWw49lFFyvj" }, "execution_count": 2, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Loading the MRI dataset" ], "metadata": { "id": "D9UoPJYLjQ-M" } }, { "cell_type": "code", "source": [ "# Loading the dataset from Hugging Face Hub\n", "mri_data = load_dataset(\"Falah/Alzheimer_MRI\", split=\"train\")" ], "metadata": { "id": "e0MQT_O2GFcc", "colab": { "base_uri": "https://localhost:8080/", "height": 177, "referenced_widgets": [ "509f92bf7c1b48de89afe07c4d1793e0", "bbe61f14e8484bad9b4b7ed6c474e2e5", "954989698eee47139bab6b0987c9acaf", "5a7b78ee14654aa7826989f542773fc7", "43f1706083a34414a81e40a51bf9169e", "d8a2e71bf7034292a09d81fd522c09ad", "03443b943dcd47fc9171589fe4e283cc", "ab5d3fcd036749e0ac55f1e91077ed8b", "c2def0250e1a4b55b7c33ea07a2349cf", "b5ca60d6c5a84e4a928e16930bfa6811", "f50478f4353644b3b89a05aa853ce193", "c4eba7439c3947c7a4d2cb5d0dfb6cbb", "ce45554c21a54a50a52cba2bc3830df8", "b538098e4aac484e9e73143749635790", "dc253d52288b4452a6a0b8032c482b5a", "6785a4c264224815a5aa239ac029437d", "d249fc66d7e644cf9b21010aad43a3e2", "aa0cee1920164f59835691bdd8595a7b", "5f1da7080d7b4829b537760219312cdb", "42e842cf608e44af8edb397c94fe2a31", "8fb1717e5ba141dfa392c6381e0e7b50", "42ec4c52504a4e27a4523ad84739495c", "309c95578629443cada4caca18d7815c", "a082278b4358487eb2b74c9f2e9e0689", "2c750ea69d2d43b595e8b81840ebf9ba", "98f6dc3f4f244e88ae7c574ed2444dfc", "dc66b35321c34204831b3d0417cc5d39", "0b420e0d7df54b9cbd69302020039480", "a5ff183711a844e8a445f02b6584f509", "419ef3523c964f03a27c025caa482bca", "3135b088e4bc496a94d2be10dfe20e0d", "07ec13008da243889dd7bdb005040793", "d5155954214c40fdac86dca3735f047e", "896f547c21ac4658bc120f2e61658c39", "266081030c4c4169b43d608b7bd4a69e", "068cb0868c1346e18ebdcc8caa06c294", "04c534a36ae04e66b19db19a5b5e2eb4", "1795f116649f4057939dffd8bd1cbd71", "d01f356bf9de4e0884ac440eea17344b", "0f39963d90024115943c31142268b0b2", "d5f121a410f94ef08d3edfe1a23d854c", "83d558d9018d421da7031ea0c41c453d", "ee32b5f3b82b4ddb93c893bdb1d602d5", "11164306e9e34fbca0825c3abb3c2014", "745b1cd43acf4ad481575ee749897802", "d78499d09ec542368730615be82e5f57", "17ee36a2348d4f42b067b69573f38985", "6e34b09e5b1d44e68595c539b2d6807e", "22e6abd08a474d4fafa14e31f91eb0b3", "ebb41695aedf4c7db4b92a4645270cd1", "6b980fabf40346bda451764b58ff1658", "77c0d1cf5a8d47aebd784ff870460fe1", "b5a3414e572142438e9464643c39d721", "360de3d803524874942cef36aa458c90", "670a11a9cf804ad3a68ddf97ce698fd4" ] }, "outputId": "5eff8463-074b-4c2a-ca78-2c02f173d43c" }, "execution_count": 3, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading readme: 0%| | 0.00/2.13k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "509f92bf7c1b48de89afe07c4d1793e0" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading data: 0%| | 0.00/22.6M [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "c4eba7439c3947c7a4d2cb5d0dfb6cbb" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading data: 0%| | 0.00/5.65M [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "309c95578629443cada4caca18d7815c" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Generating train split: 0%| | 0/5120 [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "896f547c21ac4658bc120f2e61658c39" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Generating test split: 0%| | 0/1280 [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "745b1cd43acf4ad481575ee749897802" } }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "# Splitting the data into training and validation sets\n", "mri_data_split = mri_data.train_test_split(\n", " test_size=0.2,\n", " stratify_by_column=\"label\",\n", " seed=12345,\n", ")\n", "mri_data_split" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Dyd7pfAcGLIb", "outputId": "b7a247f2-0357-43da-a54b-4560387887ef" }, "execution_count": 4, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['image', 'label'],\n", " num_rows: 4096\n", " })\n", " test: Dataset({\n", " features: ['image', 'label'],\n", " num_rows: 1024\n", " })\n", "})" ] }, "metadata": {}, "execution_count": 4 } ] }, { "cell_type": "markdown", "source": [ "# Data analysis" ], "metadata": { "id": "1FPS6VONnKHT" } }, { "cell_type": "code", "source": [ "labels = mri_data_split[\"train\"].features[\"label\"].names\n", "labels" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yugFU2J7nKYs", "outputId": "06f945f1-df5e-450b-cdea-75c4558f879d" }, "execution_count": 5, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['Mild_Demented', 'Moderate_Demented', 'Non_Demented', 'Very_Mild_Demented']" ] }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "code", "source": [ "# Computing the numbers of examples for each class (train/validation)\n", "class_counts_train = np.bincount(mri_data_split[\"train\"][\"label\"])\n", "class_counts_valid = np.bincount(mri_data_split[\"test\"][\"label\"])\n", "\n", "# Creating a summary DataFrame\n", "dataset_info = {\n", " \"train\": class_counts_train,\n", " \"test\": class_counts_valid\n", "}\n", "class_counts_info = pd.DataFrame(data=dataset_info, index=labels)\n", "class_counts_info" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 174 }, "id": "bEouAIqVnKz3", "outputId": "e8b79143-de3a-4949-fb86-b01a9b8393a3" }, "execution_count": 6, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " train test\n", "Mild_Demented 579 145\n", "Moderate_Demented 39 10\n", "Non_Demented 2053 513\n", "Very_Mild_Demented 1425 356" ], "text/html": [ "\n", "
\n", " | train | \n", "test | \n", "
---|---|---|
Mild_Demented | \n", "579 | \n", "145 | \n", "
Moderate_Demented | \n", "39 | \n", "10 | \n", "
Non_Demented | \n", "2053 | \n", "513 | \n", "
Very_Mild_Demented | \n", "1425 | \n", "356 | \n", "
Epoch | \n", "Training Loss | \n", "Validation Loss | \n", "F1 | \n", "
---|---|---|---|
1 | \n", "1.121800 | \n", "0.941877 | \n", "0.574249 | \n", "
2 | \n", "0.940000 | \n", "0.905402 | \n", "0.602930 | \n", "
3 | \n", "0.912300 | \n", "0.901887 | \n", "0.526183 | \n", "
4 | \n", "0.862500 | \n", "0.846535 | \n", "0.602914 | \n", "
5 | \n", "0.810400 | \n", "0.781023 | \n", "0.631924 | \n", "
6 | \n", "0.724400 | \n", "0.727819 | \n", "0.703721 | \n", "
7 | \n", "0.697000 | \n", "0.629985 | \n", "0.748010 | \n", "
8 | \n", "0.586500 | \n", "0.565929 | \n", "0.766220 | \n", "
9 | \n", "0.519900 | \n", "0.544534 | \n", "0.772069 | \n", "
10 | \n", "0.473400 | \n", "0.674992 | \n", "0.718480 | \n", "
11 | \n", "0.439900 | \n", "0.489264 | \n", "0.827378 | \n", "
12 | \n", "0.381700 | \n", "0.557753 | \n", "0.784368 | \n", "
13 | \n", "0.331800 | \n", "0.469930 | \n", "0.822824 | \n", "
14 | \n", "0.309600 | \n", "0.445963 | \n", "0.839947 | \n", "
15 | \n", "0.278700 | \n", "0.410541 | \n", "0.839936 | \n", "
16 | \n", "0.251700 | \n", "0.348793 | \n", "0.857752 | \n", "
17 | \n", "0.234600 | \n", "0.387677 | \n", "0.877273 | \n", "
18 | \n", "0.228600 | \n", "0.341952 | \n", "0.857452 | \n", "
19 | \n", "0.191400 | \n", "0.412256 | \n", "0.868153 | \n", "
20 | \n", "0.184400 | \n", "0.289442 | \n", "0.891273 | \n", "
21 | \n", "0.173000 | \n", "0.319741 | \n", "0.888702 | \n", "
22 | \n", "0.168700 | \n", "0.262622 | \n", "0.907471 | \n", "
23 | \n", "0.160100 | \n", "0.295084 | \n", "0.906839 | \n", "
24 | \n", "0.146600 | \n", "0.266578 | \n", "0.904947 | \n", "
25 | \n", "0.146800 | \n", "0.213564 | \n", "0.910279 | \n", "
26 | \n", "0.122600 | \n", "0.238661 | \n", "0.912666 | \n", "
27 | \n", "0.118600 | \n", "0.213083 | \n", "0.927114 | \n", "
28 | \n", "0.095100 | \n", "0.252022 | \n", "0.912958 | \n", "
29 | \n", "0.104900 | \n", "0.209648 | \n", "0.925930 | \n", "
30 | \n", "0.093600 | \n", "0.187472 | \n", "0.930865 | \n", "
"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=1920, training_loss=0.3936845714847247, metrics={'train_runtime': 2362.4807, 'train_samples_per_second': 52.013, 'train_steps_per_second': 0.813, 'total_flos': 9.52238637305561e+18, 'train_loss': 0.3936845714847247, 'epoch': 30.0})"
]
},
"metadata": {},
"execution_count": 25
}
]
},
{
"cell_type": "code",
"source": [
"trainer.push_to_hub()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
},
"id": "vbfO9xPPdOm1",
"outputId": "b9a26771-d432-472e-cc13-8fd8836efebd"
},
"execution_count": 26,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"CommitInfo(commit_url='https://huggingface.co/spolivin/alz-mri-vit/commit/b2b0ecb84e796d5b50af69bcf0e3ebddfce7672b', commit_message='End of training', commit_description='', oid='b2b0ecb84e796d5b50af69bcf0e3ebddfce7672b', pr_url=None, pr_revision=None, pr_num=None)"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 26
}
]
},
{
"cell_type": "markdown",
"source": [
"# Inference"
],
"metadata": {
"id": "1d903JNhuOZW"
}
},
{
"cell_type": "code",
"source": [
"mri_data_test = load_dataset(\"Falah/Alzheimer_MRI\", split=\"test\")"
],
"metadata": {
"id": "C5QWE6aaJQxQ"
},
"execution_count": 27,
"outputs": []
},
{
"cell_type": "code",
"source": [
"id = -9\n",
"image = mri_data_test[\"image\"][id]\n",
"print(id2label[str(mri_data_test[\"label\"][id])])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BTK1oVf3TbNx",
"outputId": "ba039f1a-49d4-45d5-951b-3c1fa3c666b6"
},
"execution_count": 28,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mild_Demented\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"image"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 145
},
"id": "8uZk7V9pT1tk",
"outputId": "ef256bc3-c809-4e17-dc2e-e776d989d56b"
},
"execution_count": 29,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"