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ViT_bloodmnist_std_30

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  1. README.md +130 -0
  2. config.json +44 -0
  3. model.safetensors +3 -0
  4. preprocessor_config.json +22 -0
  5. runs/Nov09_10-50-55_533218bcf843/events.out.tfevents.1731149458.533218bcf843.24.0 +3 -0
  6. runs/Nov09_10-50-55_533218bcf843/events.out.tfevents.1731157014.533218bcf843.24.1 +3 -0
  7. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: google/vit-base-patch16-224
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - medmnist-v2
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+ metrics:
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+ - accuracy
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+ - f1
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+ model-index:
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+ - name: ViT_bloodmnist_std_30
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+ results:
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+ - task:
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+ name: Image Classification
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+ type: image-classification
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+ dataset:
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+ name: medmnist-v2
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+ type: medmnist-v2
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+ config: bloodmnist
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+ split: validation
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+ args: bloodmnist
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9429991230634317
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+ - name: F1
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+ type: f1
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+ value: 0.9339022055894328
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # ViT_bloodmnist_std_30
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+
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+ This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1697
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+ - Accuracy: 0.9430
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+ - F1: 0.9339
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 3
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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+ |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|
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+ | 0.5658 | 0.0595 | 200 | 1.2306 | 0.5076 | 0.4526 |
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+ | 0.2887 | 0.1189 | 400 | 0.6368 | 0.7751 | 0.7410 |
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+ | 0.2406 | 0.1784 | 600 | 0.6641 | 0.7827 | 0.7050 |
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+ | 0.2229 | 0.2378 | 800 | 0.4808 | 0.8072 | 0.7832 |
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+ | 0.1955 | 0.2973 | 1000 | 0.4868 | 0.8002 | 0.7827 |
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+ | 0.1654 | 0.3567 | 1200 | 0.3306 | 0.8657 | 0.8466 |
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+ | 0.1627 | 0.4162 | 1400 | 0.3754 | 0.8732 | 0.8367 |
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+ | 0.1479 | 0.4756 | 1600 | 0.2421 | 0.9118 | 0.8949 |
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+ | 0.1501 | 0.5351 | 1800 | 0.2125 | 0.9235 | 0.9076 |
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+ | 0.1372 | 0.5945 | 2000 | 0.3706 | 0.8616 | 0.8337 |
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+ | 0.1194 | 0.6540 | 2200 | 0.1552 | 0.9451 | 0.9370 |
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+ | 0.1194 | 0.7134 | 2400 | 0.2345 | 0.9194 | 0.8992 |
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+ | 0.1135 | 0.7729 | 2600 | 0.2121 | 0.9287 | 0.9113 |
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+ | 0.1032 | 0.8323 | 2800 | 0.2023 | 0.9299 | 0.9152 |
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+ | 0.1006 | 0.8918 | 3000 | 0.1784 | 0.9451 | 0.9376 |
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+ | 0.0814 | 0.9512 | 3200 | 0.1273 | 0.9533 | 0.9484 |
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+ | 0.0842 | 1.0107 | 3400 | 0.2012 | 0.9363 | 0.9240 |
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+ | 0.0426 | 1.0702 | 3600 | 0.2221 | 0.9340 | 0.9280 |
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+ | 0.06 | 1.1296 | 3800 | 0.2641 | 0.9100 | 0.9037 |
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+ | 0.0632 | 1.1891 | 4000 | 0.1796 | 0.9433 | 0.9339 |
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+ | 0.0506 | 1.2485 | 4200 | 0.2771 | 0.8989 | 0.8838 |
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+ | 0.0467 | 1.3080 | 4400 | 0.1939 | 0.9393 | 0.9265 |
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+ | 0.0469 | 1.3674 | 4600 | 0.1896 | 0.9410 | 0.9322 |
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+ | 0.0457 | 1.4269 | 4800 | 0.1477 | 0.9509 | 0.9479 |
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+ | 0.0416 | 1.4863 | 5000 | 0.2789 | 0.9206 | 0.9086 |
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+ | 0.043 | 1.5458 | 5200 | 0.1832 | 0.9463 | 0.9389 |
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+ | 0.0412 | 1.6052 | 5400 | 0.2100 | 0.9404 | 0.9337 |
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+ | 0.0358 | 1.6647 | 5600 | 0.2368 | 0.9287 | 0.9135 |
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+ | 0.0376 | 1.7241 | 5800 | 0.2668 | 0.9252 | 0.9096 |
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+ | 0.0385 | 1.7836 | 6000 | 0.2145 | 0.9398 | 0.9291 |
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+ | 0.0273 | 1.8430 | 6200 | 0.1995 | 0.9433 | 0.9302 |
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+ | 0.0251 | 1.9025 | 6400 | 0.1900 | 0.9486 | 0.9395 |
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+ | 0.0298 | 1.9620 | 6600 | 0.1617 | 0.9597 | 0.9526 |
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+ | 0.02 | 2.0214 | 6800 | 0.1984 | 0.9463 | 0.9343 |
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+ | 0.0083 | 2.0809 | 7000 | 0.1899 | 0.9498 | 0.9377 |
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+ | 0.0068 | 2.1403 | 7200 | 0.2592 | 0.9340 | 0.9199 |
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+ | 0.0059 | 2.1998 | 7400 | 0.2101 | 0.9428 | 0.9335 |
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+ | 0.0066 | 2.2592 | 7600 | 0.2247 | 0.9422 | 0.9259 |
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+ | 0.0062 | 2.3187 | 7800 | 0.2370 | 0.9439 | 0.9348 |
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+ | 0.0084 | 2.3781 | 8000 | 0.2266 | 0.9474 | 0.9390 |
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+ | 0.0049 | 2.4376 | 8200 | 0.2343 | 0.9480 | 0.9354 |
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+ | 0.0075 | 2.4970 | 8400 | 0.2032 | 0.9486 | 0.9378 |
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+ | 0.0025 | 2.5565 | 8600 | 0.1916 | 0.9515 | 0.9436 |
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+ | 0.0064 | 2.6159 | 8800 | 0.2066 | 0.9533 | 0.9436 |
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+ | 0.004 | 2.6754 | 9000 | 0.2404 | 0.9445 | 0.9321 |
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+ | 0.0029 | 2.7348 | 9200 | 0.2402 | 0.9439 | 0.9322 |
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+ | 0.0008 | 2.7943 | 9400 | 0.2256 | 0.9468 | 0.9365 |
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+ | 0.003 | 2.8537 | 9600 | 0.2265 | 0.9492 | 0.9408 |
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+ | 0.002 | 2.9132 | 9800 | 0.2278 | 0.9515 | 0.9419 |
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+ | 0.0013 | 2.9727 | 10000 | 0.2175 | 0.9504 | 0.9422 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.45.1
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+ - Pytorch 2.4.0
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+ - Datasets 3.0.1
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+ - Tokenizers 0.20.0
config.json ADDED
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+ {
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+ "_name_or_path": "google/vit-base-patch16-224",
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+ "architectures": [
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+ "ViTForImageClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.0,
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+ "encoder_stride": 16,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.0,
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "basophil",
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+ "1": "eosinophil",
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+ "2": "erythroblast",
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+ "3": "immature granulocytes(myelocytes, metamyelocytes and promyelocytes)",
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+ "4": "lymphocyte",
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+ "5": "monocyte",
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+ "6": "neutrophil",
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+ "7": "platelet"
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+ },
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+ "image_size": 224,
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+ "initializer_range": 0.02,
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+ "label2id": {
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+ "basophil": "0",
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+ "eosinophil": "1",
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+ "immature granulocytes(myelocytes, metamyelocytes and promyelocytes)": "3",
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+ "lymphocyte": "4",
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+ "monocyte": "5",
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+ "neutrophil": "6",
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+ "platelet": "7"
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "model_type": "vit",
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+ "num_attention_heads": 12,
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+ "num_channels": 3,
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+ "num_hidden_layers": 12,
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+ "patch_size": 16,
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+ "problem_type": "single_label_classification",
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+ "qkv_bias": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.45.1"
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+ }
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