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Aruno
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gemini-beauty

Image Classification
Transformers
TensorBoard
Safetensors
mobilenet_v2
Generated from Trainer
Eval Results (legacy)
Model card Files
xet
 Metrics Community
Aruno commited on
Commit
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End of training

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Files changed (3) hide show
  1. README.md +12 -5
  2. model.safetensors +1 -1
  3. runs/Jan11_16-21-53_arnaud-Mouse-GPU/events.out.tfevents.1704957714.arnaud-Mouse-GPU.17544.0 +2 -2
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: 0.4199070160608622
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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 is a fine-tuned version of [](https://huggingface.co/) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.2337
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- - Accuracy: 0.4199
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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: 1
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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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- | 1.3729 | 1.0 | 148 | 1.2337 | 0.4199 |
 
 
 
 
 
 
 
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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: 0.5179628064243449
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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 [](https://huggingface.co/) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.1160
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+ - Accuracy: 0.5180
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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: 8
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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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+ | 1.3947 | 1.0 | 148 | 1.2066 | 0.4372 |
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+ | 1.3332 | 2.0 | 296 | 1.1703 | 0.4734 |
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+ | 1.2637 | 3.0 | 444 | 1.1678 | 0.4780 |
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+ | 1.2277 | 4.0 | 592 | 1.1359 | 0.4996 |
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+ | 1.2704 | 5.0 | 740 | 1.1407 | 0.5002 |
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+ | 1.2099 | 6.0 | 888 | 1.1332 | 0.5131 |
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+ | 1.1858 | 7.0 | 1036 | 1.1704 | 0.4803 |
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+ | 1.156 | 8.0 | 1184 | 1.1160 | 0.5180 |
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  ### Framework versions
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