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ViT_bloodmnist

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  1. README.md +19 -19
  2. config.json +1 -1
  3. model.safetensors +1 -1
  4. runs/Oct23_09-38-36_bc829b7a483b/events.out.tfevents.1729676326.bc829b7a483b.906.0 +3 -0
  5. runs/Oct23_09-38-36_bc829b7a483b/events.out.tfevents.1729680816.bc829b7a483b.906.1 +3 -0
  6. training_args.bin +1 -1
README.md CHANGED
@@ -24,10 +24,10 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9701841566793336
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  - name: F1
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  type: f1
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- value: 0.9693863873037792
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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
@@ -37,9 +37,9 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.0876
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- - Accuracy: 0.9702
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- - F1: 0.9694
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  ## Model description
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@@ -70,21 +70,21 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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- | 0.2696 | 1.0 | 374 | 0.1176 | 0.9609 | 0.9573 |
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- | 0.0905 | 2.0 | 748 | 0.0769 | 0.9743 | 0.9718 |
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- | 0.043 | 3.0 | 1122 | 0.1077 | 0.9667 | 0.9680 |
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- | 0.0247 | 4.0 | 1496 | 0.0915 | 0.9737 | 0.9728 |
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- | 0.0069 | 5.0 | 1870 | 0.1082 | 0.9749 | 0.9748 |
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- | 0.0023 | 6.0 | 2244 | 0.1226 | 0.9749 | 0.9747 |
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- | 0.0004 | 7.0 | 2618 | 0.1067 | 0.9796 | 0.9786 |
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- | 0.0001 | 8.0 | 2992 | 0.1107 | 0.9772 | 0.9765 |
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- | 0.0001 | 9.0 | 3366 | 0.1105 | 0.9772 | 0.9765 |
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- | 0.0001 | 10.0 | 3740 | 0.1104 | 0.9772 | 0.9765 |
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  ### Framework versions
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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
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9748611517100263
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  - name: F1
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  type: f1
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+ value: 0.97180354304681
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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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  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: 0.0879
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+ - Accuracy: 0.9749
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+ - F1: 0.9718
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | 0.2747 | 1.0 | 374 | 0.0930 | 0.9696 | 0.9652 |
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+ | 0.0955 | 2.0 | 748 | 0.0998 | 0.9702 | 0.9670 |
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+ | 0.0405 | 3.0 | 1122 | 0.0812 | 0.9743 | 0.9725 |
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+ | 0.0194 | 4.0 | 1496 | 0.0829 | 0.9796 | 0.9784 |
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+ | 0.0081 | 5.0 | 1870 | 0.1328 | 0.9720 | 0.9696 |
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+ | 0.0026 | 6.0 | 2244 | 0.1252 | 0.9743 | 0.9735 |
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+ | 0.0004 | 7.0 | 2618 | 0.0997 | 0.9790 | 0.9778 |
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+ | 0.0001 | 8.0 | 2992 | 0.1049 | 0.9784 | 0.9768 |
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+ | 0.0001 | 9.0 | 3366 | 0.1072 | 0.9778 | 0.9761 |
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+ | 0.0001 | 10.0 | 3740 | 0.1077 | 0.9778 | 0.9761 |
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  ### Framework versions
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+ - Transformers 4.44.2
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+ - Pytorch 2.4.1+cu121
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+ - Datasets 3.0.2
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+ - Tokenizers 0.19.1
config.json CHANGED
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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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  "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": "4.44.2"
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  }
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