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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: rsna-intracranial-hemorrhage-detection |
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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: imagefolder |
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type: imagefolder |
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config: default |
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split: test |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.6151724137931035 |
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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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# rsna-intracranial-hemorrhage-detection |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2164 |
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- Accuracy: 0.6152 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 1.5655 | 1.0 | 238 | 1.5235 | 0.4039 | |
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| 1.3848 | 2.0 | 477 | 1.3622 | 0.4692 | |
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| 1.2812 | 3.0 | 716 | 1.2811 | 0.5150 | |
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| 1.2039 | 4.0 | 955 | 1.1795 | 0.5556 | |
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| 1.1641 | 5.0 | 1193 | 1.1627 | 0.5534 | |
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| 1.1961 | 6.0 | 1432 | 1.1393 | 0.5705 | |
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| 1.1382 | 7.0 | 1671 | 1.0921 | 0.5804 | |
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| 0.9653 | 8.0 | 1910 | 1.0790 | 0.5876 | |
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| 0.9346 | 9.0 | 2148 | 1.0727 | 0.5931 | |
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| 0.9083 | 10.0 | 2387 | 1.0605 | 0.5994 | |
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| 0.8936 | 11.0 | 2626 | 1.0147 | 0.6146 | |
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| 0.8504 | 12.0 | 2865 | 1.0849 | 0.5818 | |
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| 0.8544 | 13.0 | 3103 | 1.0349 | 0.6052 | |
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| 0.7884 | 14.0 | 3342 | 1.0435 | 0.6074 | |
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| 0.7974 | 15.0 | 3581 | 1.0082 | 0.6127 | |
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| 0.7921 | 16.0 | 3820 | 1.0438 | 0.6017 | |
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| 0.709 | 17.0 | 4058 | 1.0484 | 0.6094 | |
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| 0.6646 | 18.0 | 4297 | 1.0554 | 0.6221 | |
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| 0.6832 | 19.0 | 4536 | 1.0455 | 0.6124 | |
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| 0.7076 | 20.0 | 4775 | 1.0905 | 0.6 | |
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| 0.7442 | 21.0 | 5013 | 1.1094 | 0.6008 | |
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| 0.6332 | 22.0 | 5252 | 1.0777 | 0.6063 | |
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| 0.6417 | 23.0 | 5491 | 1.0765 | 0.6141 | |
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| 0.6267 | 24.0 | 5730 | 1.1057 | 0.6091 | |
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| 0.6082 | 25.0 | 5968 | 1.0962 | 0.6171 | |
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| 0.6191 | 26.0 | 6207 | 1.1178 | 0.6039 | |
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| 0.5654 | 27.0 | 6446 | 1.1386 | 0.5948 | |
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| 0.5776 | 28.0 | 6685 | 1.1121 | 0.6105 | |
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| 0.5531 | 29.0 | 6923 | 1.1497 | 0.6030 | |
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| 0.6275 | 30.0 | 7162 | 1.1796 | 0.6028 | |
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| 0.5373 | 31.0 | 7401 | 1.1306 | 0.6132 | |
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| 0.4775 | 32.0 | 7640 | 1.1523 | 0.6058 | |
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| 0.5469 | 33.0 | 7878 | 1.1634 | 0.6127 | |
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| 0.4934 | 34.0 | 8117 | 1.1853 | 0.616 | |
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| 0.5233 | 35.0 | 8356 | 1.2018 | 0.6055 | |
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| 0.4896 | 36.0 | 8595 | 1.1585 | 0.6108 | |
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| 0.5122 | 37.0 | 8833 | 1.1874 | 0.6146 | |
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| 0.4726 | 38.0 | 9072 | 1.1608 | 0.6193 | |
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| 0.4372 | 39.0 | 9311 | 1.2403 | 0.6132 | |
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| 0.498 | 40.0 | 9550 | 1.1752 | 0.6201 | |
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| 0.4813 | 41.0 | 9788 | 1.2005 | 0.6166 | |
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| 0.4762 | 42.0 | 10027 | 1.2285 | 0.6022 | |
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| 0.4852 | 43.0 | 10266 | 1.2192 | 0.6119 | |
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| 0.4332 | 44.0 | 10505 | 1.2391 | 0.6218 | |
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| 0.3998 | 45.0 | 10743 | 1.1779 | 0.6196 | |
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| 0.4467 | 46.0 | 10982 | 1.2048 | 0.6284 | |
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| 0.4332 | 47.0 | 11221 | 1.2302 | 0.6188 | |
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| 0.4529 | 48.0 | 11460 | 1.2220 | 0.6188 | |
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| 0.4281 | 49.0 | 11698 | 1.2013 | 0.624 | |
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| 0.4199 | 49.84 | 11900 | 1.2164 | 0.6152 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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