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JorgeGIT
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finetuned-Leukemia-cell

Image Classification
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convnext
Generated from Trainer
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  1. README.md +8 -5
  2. all_results.json +11 -11
  3. eval_results.json +6 -6
  4. model.safetensors +1 -1
  5. runs/Nov30_23-24-53_6c26dad0aabb/events.out.tfevents.1701388197.6c26dad0aabb.7066.4 +3 -0
  6. runs/Nov30_23-50-18_6c26dad0aabb/events.out.tfevents.1701388227.6c26dad0aabb.7066.5 +3 -0
  7. train_results.json +6 -6
  8. trainer_state.json +161 -104
  9. training_args.bin +1 -1
README.md CHANGED
@@ -32,7 +32,7 @@ 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-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: 0.0870
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  - Accuracy: 0.9774
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  ## Model description
@@ -58,16 +58,19 @@ The following hyperparameters were used during training:
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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: 10
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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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- | 0.1606 | 2.94 | 100 | 0.1829 | 0.9511 |
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- | 0.1895 | 5.88 | 200 | 0.1441 | 0.9662 |
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- | 0.0423 | 8.82 | 300 | 0.0870 | 0.9774 |
 
 
 
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  ### Framework versions
 
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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.
34
  It achieves the following results on the evaluation set:
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+ - Loss: 0.1073
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  - Accuracy: 0.9774
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  ## Model description
 
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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: 20
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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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+ | 0.0673 | 2.94 | 100 | 0.1144 | 0.9699 |
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+ | 0.0639 | 5.88 | 200 | 0.1431 | 0.9549 |
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+ | 0.0491 | 8.82 | 300 | 0.2439 | 0.9436 |
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+ | 0.0532 | 11.76 | 400 | 0.1389 | 0.9624 |
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+ | 0.0636 | 14.71 | 500 | 0.1537 | 0.9624 |
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+ | 0.0493 | 17.65 | 600 | 0.1073 | 0.9774 |
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  ### Framework versions
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