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Prot10
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vit-base-patch16-224-for-pre_evaluation

Image Classification
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PyTorch
vit
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  2. pytorch_model.bin +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 [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.4792
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- - Accuracy: 0.3310
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  ## Model description
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@@ -46,15 +46,40 @@ 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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- | 1.5912 | 0.93 | 10 | 1.5084 | 0.3217 |
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- | 1.4776 | 1.95 | 21 | 1.4916 | 0.3100 |
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- | 1.4576 | 2.79 | 30 | 1.4792 | 0.3310 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.6627
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+ - Accuracy: 0.4242
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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: 30
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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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+ | 1.6266 | 0.93 | 10 | 1.5308 | 0.3007 |
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+ | 1.4973 | 1.95 | 21 | 1.5000 | 0.3124 |
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+ | 1.4636 | 2.98 | 32 | 1.4730 | 0.3450 |
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+ | 1.4185 | 4.0 | 43 | 1.5052 | 0.3054 |
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+ | 1.4147 | 4.93 | 53 | 1.4456 | 0.3497 |
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+ | 1.337 | 5.95 | 64 | 1.4736 | 0.3427 |
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+ | 1.2869 | 6.98 | 75 | 1.4170 | 0.3800 |
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+ | 1.241 | 8.0 | 86 | 1.5078 | 0.3730 |
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+ | 1.2261 | 8.93 | 96 | 1.4629 | 0.3893 |
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+ | 1.1967 | 9.95 | 107 | 1.4139 | 0.4103 |
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+ | 1.1153 | 10.98 | 118 | 1.4338 | 0.4196 |
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+ | 1.063 | 12.0 | 129 | 1.4643 | 0.3939 |
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+ | 1.0434 | 12.93 | 139 | 1.4726 | 0.4079 |
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+ | 0.9849 | 13.95 | 150 | 1.5287 | 0.4149 |
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+ | 0.9285 | 14.98 | 161 | 1.5530 | 0.3986 |
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+ | 0.8724 | 16.0 | 172 | 1.5016 | 0.3939 |
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+ | 0.8063 | 16.93 | 182 | 1.5185 | 0.4056 |
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+ | 0.8206 | 17.95 | 193 | 1.5448 | 0.4172 |
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+ | 0.7396 | 18.98 | 204 | 1.5540 | 0.4033 |
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+ | 0.7437 | 20.0 | 215 | 1.5796 | 0.4009 |
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+ | 0.7103 | 20.93 | 225 | 1.6218 | 0.4033 |
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+ | 0.6861 | 21.95 | 236 | 1.6126 | 0.4266 |
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+ | 0.6798 | 22.98 | 247 | 1.6051 | 0.4266 |
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+ | 0.6358 | 24.0 | 258 | 1.6141 | 0.4103 |
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+ | 0.6189 | 24.93 | 268 | 1.6706 | 0.4056 |
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+ | 0.6176 | 25.95 | 279 | 1.6404 | 0.4219 |
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+ | 0.5756 | 26.98 | 290 | 1.6603 | 0.4172 |
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+ | 0.5887 | 27.91 | 300 | 1.6627 | 0.4242 |
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  ### Framework versions
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