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JorgeGIT
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finetuned-Leukemia-cell

Image Classification
Transformers
TensorBoard
Safetensors
convnext
Generated from Trainer
Eval Results
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  1. README.md +21 -10
  2. model.safetensors +1 -1
  3. runs/Dec02_22-22-33_245c84bfb6d0/events.out.tfevents.1701555765.245c84bfb6d0.770.0 +3 -0
  4. runs/Dec02_22-27-55_245c84bfb6d0/events.out.tfevents.1701556079.245c84bfb6d0.770.1 +3 -0
  5. training_args.bin +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9736842105263158
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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
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0777
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- - Accuracy: 0.9737
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  ## Model description
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@@ -58,19 +58,30 @@ 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: 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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- | 0.3279 | 2.94 | 100 | 0.2461 | 0.9098 |
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- | 0.205 | 5.88 | 200 | 0.1707 | 0.9436 |
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- | 0.1434 | 8.82 | 300 | 0.1257 | 0.9586 |
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- | 0.0828 | 11.76 | 400 | 0.1377 | 0.9699 |
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- | 0.0469 | 14.71 | 500 | 0.1476 | 0.9511 |
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- | 0.107 | 17.65 | 600 | 0.0777 | 0.9737 |
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9661654135338346
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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 [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1249
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+ - Accuracy: 0.9662
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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: 50
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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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+ | 0.3396 | 2.94 | 100 | 0.2611 | 0.9060 |
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+ | 0.2488 | 5.88 | 200 | 0.2651 | 0.9173 |
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+ | 0.1783 | 8.82 | 300 | 0.1906 | 0.9323 |
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+ | 0.0837 | 11.76 | 400 | 0.1773 | 0.9511 |
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+ | 0.0934 | 14.71 | 500 | 0.2027 | 0.9361 |
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+ | 0.1283 | 17.65 | 600 | 0.0602 | 0.9737 |
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+ | 0.06 | 20.59 | 700 | 0.1383 | 0.9624 |
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+ | 0.024 | 23.53 | 800 | 0.0773 | 0.9737 |
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+ | 0.0446 | 26.47 | 900 | 0.1669 | 0.9549 |
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+ | 0.0342 | 29.41 | 1000 | 0.1320 | 0.9624 |
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+ | 0.0458 | 32.35 | 1100 | 0.1128 | 0.9662 |
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+ | 0.0394 | 35.29 | 1200 | 0.2099 | 0.9436 |
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+ | 0.0593 | 38.24 | 1300 | 0.0890 | 0.9774 |
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+ | 0.0346 | 41.18 | 1400 | 0.1216 | 0.9662 |
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+ | 0.0535 | 44.12 | 1500 | 0.1303 | 0.9662 |
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+ | 0.0139 | 47.06 | 1600 | 0.1195 | 0.9624 |
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+ | 0.0476 | 50.0 | 1700 | 0.1249 | 0.9662 |
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  ### Framework versions
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