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ViT-NIH-Chest-X-ray-dataset-small

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  1. README.md +29 -27
  2. model.safetensors +1 -1
README.md CHANGED
@@ -3,7 +3,6 @@ library_name: transformers
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  license: apache-2.0
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  base_model: Sohaibsoussi/ViT-NIH-Chest-X-ray-dataset-small
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  tags:
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- - image-classification
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  - generated_from_trainer
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  metrics:
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  - accuracy
@@ -17,10 +16,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # ViT-NIH-Chest-X-ray-dataset-small
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- This model is a fine-tuned version of [Sohaibsoussi/ViT-NIH-Chest-X-ray-dataset-small](https://huggingface.co/Sohaibsoussi/ViT-NIH-Chest-X-ray-dataset-small) on the Sohaibsoussi/NIH-Chest-X-ray-dataset-small dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2988
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- - Accuracy: 0.2299
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  ## Model description
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@@ -45,39 +44,42 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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- - num_epochs: 8
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  - mixed_precision_training: Native AMP
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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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- | 0.2128 | 0.3690 | 100 | 0.2092 | 0.0 |
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- | 0.1848 | 0.7380 | 200 | 0.1909 | 0.3821 |
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- | 0.171 | 1.1070 | 300 | 0.1967 | 0.5387 |
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- | 0.1772 | 1.4760 | 400 | 0.1932 | 0.5451 |
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- | 0.1629 | 1.8450 | 500 | 0.1842 | 0.4486 |
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- | 0.1942 | 2.2140 | 600 | 0.1770 | 0.4197 |
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- | 0.1714 | 2.5830 | 700 | 0.1797 | 0.5023 |
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- | 0.1832 | 2.9520 | 800 | 0.1730 | 0.3688 |
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- | 0.1766 | 3.3210 | 900 | 0.1755 | 0.3428 |
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- | 0.1697 | 3.6900 | 1000 | 0.1601 | 0.5168 |
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- | 0.1568 | 4.0590 | 1100 | 0.1577 | 0.5353 |
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- | 0.1484 | 4.4280 | 1200 | 0.1514 | 0.4919 |
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- | 0.1483 | 4.7970 | 1300 | 0.1482 | 0.5699 |
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- | 0.1301 | 5.1661 | 1400 | 0.1315 | 0.5434 |
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- | 0.1149 | 5.5351 | 1500 | 0.1294 | 0.5584 |
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- | 0.1448 | 5.9041 | 1600 | 0.1266 | 0.5416 |
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- | 0.1035 | 6.2731 | 1700 | 0.1151 | 0.6017 |
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- | 0.1048 | 6.6421 | 1800 | 0.1060 | 0.6046 |
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- | 0.1168 | 7.0111 | 1900 | 0.1007 | 0.6173 |
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- | 0.1104 | 7.3801 | 2000 | 0.0949 | 0.6445 |
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- | 0.0873 | 7.7491 | 2100 | 0.0923 | 0.6526 |
 
 
 
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  ### Framework versions
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  - Transformers 4.46.3
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- - Pytorch 2.5.1+cu121
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  - Datasets 3.1.0
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  - Tokenizers 0.20.3
 
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  license: apache-2.0
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  base_model: Sohaibsoussi/ViT-NIH-Chest-X-ray-dataset-small
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  tags:
 
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  - generated_from_trainer
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  metrics:
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  - accuracy
 
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  # ViT-NIH-Chest-X-ray-dataset-small
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+ This model is a fine-tuned version of [Sohaibsoussi/ViT-NIH-Chest-X-ray-dataset-small](https://huggingface.co/Sohaibsoussi/ViT-NIH-Chest-X-ray-dataset-small) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0013
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+ - Accuracy: 1.0
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  ## Model description
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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+ - num_epochs: 9
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  - mixed_precision_training: Native AMP
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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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+ | 0.0271 | 0.3690 | 100 | 0.0347 | 0.8584 |
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+ | 0.0334 | 0.7380 | 200 | 0.0291 | 0.8624 |
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+ | 0.0438 | 1.1070 | 300 | 0.0352 | 0.8607 |
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+ | 0.0215 | 1.4760 | 400 | 0.0319 | 0.8746 |
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+ | 0.0267 | 1.8450 | 500 | 0.0277 | 0.8798 |
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+ | 0.0266 | 2.2140 | 600 | 0.0177 | 0.9116 |
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+ | 0.014 | 2.5830 | 700 | 0.0127 | 0.9497 |
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+ | 0.0207 | 2.9520 | 800 | 0.0144 | 0.9410 |
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+ | 0.0115 | 3.3210 | 900 | 0.0097 | 0.9653 |
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+ | 0.0113 | 3.6900 | 1000 | 0.0077 | 0.9711 |
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+ | 0.0054 | 4.0590 | 1100 | 0.0068 | 0.9844 |
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+ | 0.0047 | 4.4280 | 1200 | 0.0046 | 0.9850 |
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+ | 0.0056 | 4.7970 | 1300 | 0.0040 | 0.9902 |
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+ | 0.0026 | 5.1661 | 1400 | 0.0032 | 0.9925 |
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+ | 0.0037 | 5.5351 | 1500 | 0.0027 | 0.9936 |
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+ | 0.0039 | 5.9041 | 1600 | 0.0023 | 0.9977 |
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+ | 0.0019 | 6.2731 | 1700 | 0.0019 | 0.9971 |
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+ | 0.0019 | 6.6421 | 1800 | 0.0017 | 0.9988 |
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+ | 0.0016 | 7.0111 | 1900 | 0.0015 | 1.0 |
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+ | 0.002 | 7.3801 | 2000 | 0.0014 | 1.0 |
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+ | 0.0013 | 7.7491 | 2100 | 0.0014 | 1.0 |
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+ | 0.0015 | 8.1181 | 2200 | 0.0013 | 1.0 |
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+ | 0.0011 | 8.4871 | 2300 | 0.0013 | 1.0 |
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+ | 0.0013 | 8.8561 | 2400 | 0.0013 | 1.0 |
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  ### Framework versions
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  - Transformers 4.46.3
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+ - Pytorch 2.4.0
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  - Datasets 3.1.0
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  - Tokenizers 0.20.3
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