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NiharGupte
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resnet-50-finetuned-student_kaggle

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  1. README.md +10 -10
  2. model.safetensors +1 -1
README.md CHANGED
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9874213836477987
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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-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0354
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- - Accuracy: 0.9874
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  ## Model description
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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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- | 0.5044 | 1.0 | 47 | 0.2463 | 0.8978 |
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- | 0.2076 | 2.0 | 94 | 0.1007 | 0.9717 |
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- | 0.2115 | 3.0 | 141 | 0.0480 | 0.9874 |
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- | 0.1361 | 4.0 | 188 | 0.0387 | 0.9874 |
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- | 0.1359 | 5.0 | 235 | 0.0354 | 0.9874 |
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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: 0.1949685534591195
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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-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2966756414651073587838976.0000
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+ - Accuracy: 0.1950
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  ## Model description
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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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+ | 2231665653697395759775744.0000 | 1.0 | 47 | 2703493994833503873662976.0000 | 0.1950 |
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+ | 2448978214010634004070400.0000 | 2.0 | 94 | 2805605946653523096633344.0000 | 0.1950 |
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+ | 2364532939574307232677888.0000 | 3.0 | 141 | 2845180265529529270796288.0000 | 0.1950 |
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+ | 2331862372313962142236672.0000 | 4.0 | 188 | 3271042952136692586250240.0000 | 0.1950 |
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+ | 2584276319587858445762560.0000 | 5.0 | 235 | 2966756414651073587838976.0000 | 0.1950 |
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  ### Framework versions
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