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jerryteps
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resnet-50

Image Classification
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PyTorch
TensorBoard
Safetensors
resnet
Generated from Trainer
Eval Results (legacy)
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jerryteps commited on
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update model card README.md

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  1. README.md +9 -7
README.md CHANGED
@@ -20,7 +20,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.30788520479242126
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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
@@ -30,8 +30,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model was trained from scratch on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.6948
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- - Accuracy: 0.3079
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  ## Model description
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@@ -59,15 +59,17 @@ 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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- | 1.7973 | 1.0 | 252 | 1.7940 | 0.2477 |
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- | 1.7299 | 2.0 | 505 | 1.7277 | 0.2803 |
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- | 1.7096 | 2.99 | 756 | 1.6948 | 0.3079 |
 
 
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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: 0.4446921147952076
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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 was trained from scratch on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.4667
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+ - Accuracy: 0.4447
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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: 5
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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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+ | 1.7252 | 1.0 | 252 | 1.7069 | 0.2753 |
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+ | 1.6386 | 2.0 | 505 | 1.5799 | 0.3912 |
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+ | 1.617 | 3.0 | 757 | 1.5154 | 0.4274 |
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+ | 1.5795 | 4.0 | 1010 | 1.4836 | 0.4377 |
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+ | 1.5645 | 4.99 | 1260 | 1.4667 | 0.4447 |
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  ### Framework versions