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louislu9911
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resnet-152-finetuned-cassava-leaf-disease

Image Classification
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resnet
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Eval Results
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  1. README.md +34 -7
  2. model.safetensors +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: 0.3560747663551402
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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 [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 4.1197
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- - Accuracy: 0.3561
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  ## Model description
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@@ -61,15 +61,42 @@ The following hyperparameters were used during training:
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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: 3
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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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- | 6.4984 | 0.99 | 24 | 5.4849 | 0.0771 |
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- | 5.2526 | 1.98 | 48 | 4.2884 | 0.3290 |
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- | 4.118 | 2.97 | 72 | 4.1197 | 0.3561 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.7995327102803739
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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 [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5757
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+ - Accuracy: 0.7995
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  ## Model description
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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: 30
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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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+ | 7.2158 | 0.99 | 24 | 6.8151 | 0.0014 |
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+ | 6.6136 | 1.98 | 48 | 5.6994 | 0.0463 |
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+ | 4.7064 | 2.97 | 72 | 3.3529 | 0.4556 |
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+ | 3.0888 | 4.0 | 97 | 2.1476 | 0.5921 |
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+ | 1.8458 | 4.99 | 121 | 1.5884 | 0.6285 |
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+ | 1.4168 | 5.98 | 145 | 1.2460 | 0.6589 |
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+ | 1.1951 | 6.97 | 169 | 1.0658 | 0.6869 |
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+ | 0.9871 | 8.0 | 194 | 0.9436 | 0.7037 |
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+ | 0.9247 | 8.99 | 218 | 0.8640 | 0.7238 |
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+ | 0.826 | 9.98 | 242 | 0.7977 | 0.7318 |
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+ | 0.7964 | 10.97 | 266 | 0.7539 | 0.7509 |
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+ | 0.7168 | 12.0 | 291 | 0.7179 | 0.7514 |
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+ | 0.6893 | 12.99 | 315 | 0.6952 | 0.7631 |
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+ | 0.687 | 13.98 | 339 | 0.6813 | 0.7640 |
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+ | 0.6496 | 14.97 | 363 | 0.6577 | 0.7692 |
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+ | 0.6477 | 16.0 | 388 | 0.6434 | 0.7813 |
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+ | 0.5992 | 16.99 | 412 | 0.6389 | 0.7771 |
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+ | 0.6059 | 17.98 | 436 | 0.6236 | 0.7907 |
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+ | 0.5993 | 18.97 | 460 | 0.6181 | 0.7935 |
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+ | 0.5802 | 20.0 | 485 | 0.6115 | 0.7911 |
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+ | 0.5905 | 20.99 | 509 | 0.6005 | 0.7930 |
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+ | 0.5825 | 21.98 | 533 | 0.5945 | 0.7953 |
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+ | 0.5837 | 22.97 | 557 | 0.5882 | 0.7981 |
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+ | 0.5501 | 24.0 | 582 | 0.5848 | 0.8023 |
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+ | 0.5593 | 24.99 | 606 | 0.5861 | 0.7963 |
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+ | 0.5454 | 25.98 | 630 | 0.5776 | 0.8014 |
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+ | 0.5656 | 26.97 | 654 | 0.5793 | 0.7977 |
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+ | 0.5708 | 28.0 | 679 | 0.5815 | 0.8005 |
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+ | 0.5541 | 28.99 | 703 | 0.5749 | 0.8033 |
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+ | 0.5439 | 29.69 | 720 | 0.5757 | 0.7995 |
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  ### Framework versions
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