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resnet-50-image-classification

Image Classification
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Safetensors
resnet
Generated from Trainer
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  2. model.safetensors +1 -1
README.md CHANGED
@@ -17,8 +17,8 @@ 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 None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.0258
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- - Accuracy: 0.7053
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  ## Model description
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@@ -44,14 +44,52 @@ 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_steps: 2000
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- - num_epochs: 2
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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 | 338 | 2.2755 | 0.5597 |
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- | 2.2796 | 2.0 | 676 | 2.0258 | 0.7053 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3093
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+ - Accuracy: 0.9408
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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_steps: 2000
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+ - num_epochs: 40
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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 | 338 | 2.2768 | 0.5172 |
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+ | 2.2806 | 2.0 | 676 | 2.0111 | 0.6903 |
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+ | 1.8538 | 3.0 | 1014 | 1.2525 | 0.7467 |
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+ | 1.8538 | 4.0 | 1352 | 0.6251 | 0.8578 |
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+ | 0.8758 | 5.0 | 1690 | 0.3761 | 0.8967 |
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+ | 0.4181 | 6.0 | 2028 | 0.2852 | 0.9144 |
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+ | 0.4181 | 7.0 | 2366 | 0.2492 | 0.9244 |
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+ | 0.2458 | 8.0 | 2704 | 0.2169 | 0.9364 |
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+ | 0.1721 | 9.0 | 3042 | 0.2121 | 0.9358 |
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+ | 0.1721 | 10.0 | 3380 | 0.2052 | 0.9403 |
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+ | 0.1089 | 11.0 | 3718 | 0.2075 | 0.9414 |
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+ | 0.0783 | 12.0 | 4056 | 0.2164 | 0.9367 |
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+ | 0.0783 | 13.0 | 4394 | 0.2274 | 0.9381 |
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+ | 0.0573 | 14.0 | 4732 | 0.2196 | 0.9433 |
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+ | 0.0465 | 15.0 | 5070 | 0.2415 | 0.9381 |
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+ | 0.0465 | 16.0 | 5408 | 0.2370 | 0.9433 |
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+ | 0.0375 | 17.0 | 5746 | 0.2521 | 0.94 |
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+ | 0.0288 | 18.0 | 6084 | 0.2533 | 0.9411 |
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+ | 0.0288 | 19.0 | 6422 | 0.2608 | 0.9381 |
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+ | 0.0253 | 20.0 | 6760 | 0.2602 | 0.9397 |
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+ | 0.0207 | 21.0 | 7098 | 0.2712 | 0.94 |
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+ | 0.0207 | 22.0 | 7436 | 0.2584 | 0.9408 |
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+ | 0.0187 | 23.0 | 7774 | 0.2703 | 0.9419 |
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+ | 0.012 | 24.0 | 8112 | 0.2772 | 0.9422 |
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+ | 0.012 | 25.0 | 8450 | 0.2712 | 0.9419 |
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+ | 0.0174 | 26.0 | 8788 | 0.2774 | 0.9422 |
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+ | 0.0137 | 27.0 | 9126 | 0.2857 | 0.9414 |
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+ | 0.0137 | 28.0 | 9464 | 0.2796 | 0.9428 |
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+ | 0.0111 | 29.0 | 9802 | 0.3008 | 0.9367 |
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+ | 0.0106 | 30.0 | 10140 | 0.2938 | 0.9369 |
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+ | 0.0106 | 31.0 | 10478 | 0.2863 | 0.9406 |
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+ | 0.0079 | 32.0 | 10816 | 0.2903 | 0.9425 |
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+ | 0.0078 | 33.0 | 11154 | 0.2961 | 0.9419 |
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+ | 0.0078 | 34.0 | 11492 | 0.2882 | 0.9417 |
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+ | 0.0056 | 35.0 | 11830 | 0.2974 | 0.9406 |
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+ | 0.0041 | 36.0 | 12168 | 0.2997 | 0.9419 |
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+ | 0.0039 | 37.0 | 12506 | 0.3123 | 0.9367 |
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+ | 0.0039 | 38.0 | 12844 | 0.3009 | 0.9408 |
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+ | 0.0036 | 39.0 | 13182 | 0.3009 | 0.9422 |
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+ | 0.0055 | 40.0 | 13520 | 0.3093 | 0.9408 |
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  ### Framework versions
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