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jolieee
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image_classification

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
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End of training

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  1. README.md +14 -7
  2. pytorch_model.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: 0.4625
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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 [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: 1.4566
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- - Accuracy: 0.4625
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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: 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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- | No log | 1.0 | 40 | 1.5451 | 0.3937 |
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- | No log | 2.0 | 80 | 1.4845 | 0.4562 |
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- | No log | 3.0 | 120 | 1.4430 | 0.4875 |
 
 
 
 
 
 
 
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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: 0.53125
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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-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: 1.2576
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+ - Accuracy: 0.5312
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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: 10
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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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+ | No log | 1.0 | 40 | 1.4319 | 0.45 |
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+ | No log | 2.0 | 80 | 1.3869 | 0.4562 |
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+ | No log | 3.0 | 120 | 1.3152 | 0.4813 |
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+ | No log | 4.0 | 160 | 1.3110 | 0.4875 |
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+ | No log | 5.0 | 200 | 1.3177 | 0.4938 |
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+ | No log | 6.0 | 240 | 1.3252 | 0.4688 |
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+ | No log | 7.0 | 280 | 1.2458 | 0.5437 |
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+ | No log | 8.0 | 320 | 1.2710 | 0.525 |
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+ | No log | 9.0 | 360 | 1.3238 | 0.5062 |
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+ | No log | 10.0 | 400 | 1.3074 | 0.5125 |
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  ### Framework versions
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