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raoulmago
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image_classification

Image Classification
Transformers
TensorBoard
Safetensors
swin
Generated from Trainer
Inference Endpoints
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End of training

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  1. README.md +53 -6
  2. model.safetensors +1 -1
  3. runs/Feb19_16-30-40_fda5917c3ade/events.out.tfevents.1708360241.fda5917c3ade.550.0 +2 -2
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/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6465
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- - Accuracy: 0.9583
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  ## Model description
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@@ -46,15 +46,62 @@ 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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- | No log | 1.0 | 1 | 0.9491 | 0.75 |
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- | No log | 2.0 | 2 | 0.7344 | 0.9167 |
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- | No log | 3.0 | 3 | 0.6465 | 0.9583 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0002
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+ - Accuracy: 1.0
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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: 50
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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 | 1 | 1.0978 | 0.5 |
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+ | No log | 2.0 | 2 | 1.0528 | 0.5833 |
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+ | No log | 3.0 | 3 | 0.9606 | 0.625 |
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+ | No log | 4.0 | 4 | 0.8337 | 0.7083 |
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+ | No log | 5.0 | 5 | 0.6860 | 0.75 |
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+ | No log | 6.0 | 6 | 0.5366 | 0.9167 |
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+ | No log | 7.0 | 7 | 0.3971 | 0.9583 |
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+ | No log | 8.0 | 8 | 0.2704 | 1.0 |
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+ | No log | 9.0 | 9 | 0.1702 | 1.0 |
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+ | 0.1887 | 10.0 | 10 | 0.1046 | 1.0 |
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+ | 0.1887 | 11.0 | 11 | 0.0633 | 1.0 |
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+ | 0.1887 | 12.0 | 12 | 0.0420 | 1.0 |
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+ | 0.1887 | 13.0 | 13 | 0.0324 | 1.0 |
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+ | 0.1887 | 14.0 | 14 | 0.0298 | 1.0 |
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+ | 0.1887 | 15.0 | 15 | 0.0260 | 1.0 |
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+ | 0.1887 | 16.0 | 16 | 0.0229 | 1.0 |
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+ | 0.1887 | 17.0 | 17 | 0.0180 | 1.0 |
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+ | 0.1887 | 18.0 | 18 | 0.0119 | 1.0 |
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+ | 0.1887 | 19.0 | 19 | 0.0072 | 1.0 |
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+ | 0.0083 | 20.0 | 20 | 0.0045 | 1.0 |
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+ | 0.0083 | 21.0 | 21 | 0.0022 | 1.0 |
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+ | 0.0083 | 22.0 | 22 | 0.0012 | 1.0 |
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+ | 0.0083 | 23.0 | 23 | 0.0007 | 1.0 |
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+ | 0.0083 | 24.0 | 24 | 0.0005 | 1.0 |
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+ | 0.0083 | 25.0 | 25 | 0.0004 | 1.0 |
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+ | 0.0083 | 26.0 | 26 | 0.0003 | 1.0 |
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+ | 0.0083 | 27.0 | 27 | 0.0003 | 1.0 |
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+ | 0.0083 | 28.0 | 28 | 0.0002 | 1.0 |
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+ | 0.0083 | 29.0 | 29 | 0.0002 | 1.0 |
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+ | 0.0009 | 30.0 | 30 | 0.0002 | 1.0 |
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+ | 0.0009 | 31.0 | 31 | 0.0002 | 1.0 |
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+ | 0.0009 | 32.0 | 32 | 0.0002 | 1.0 |
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+ | 0.0009 | 33.0 | 33 | 0.0002 | 1.0 |
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+ | 0.0009 | 34.0 | 34 | 0.0002 | 1.0 |
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+ | 0.0009 | 35.0 | 35 | 0.0002 | 1.0 |
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+ | 0.0009 | 36.0 | 36 | 0.0002 | 1.0 |
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+ | 0.0009 | 37.0 | 37 | 0.0002 | 1.0 |
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+ | 0.0009 | 38.0 | 38 | 0.0002 | 1.0 |
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+ | 0.0009 | 39.0 | 39 | 0.0002 | 1.0 |
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+ | 0.0003 | 40.0 | 40 | 0.0002 | 1.0 |
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+ | 0.0003 | 41.0 | 41 | 0.0002 | 1.0 |
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+ | 0.0003 | 42.0 | 42 | 0.0002 | 1.0 |
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+ | 0.0003 | 43.0 | 43 | 0.0002 | 1.0 |
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+ | 0.0003 | 44.0 | 44 | 0.0002 | 1.0 |
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+ | 0.0003 | 45.0 | 45 | 0.0002 | 1.0 |
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+ | 0.0003 | 46.0 | 46 | 0.0002 | 1.0 |
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+ | 0.0003 | 47.0 | 47 | 0.0002 | 1.0 |
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+ | 0.0003 | 48.0 | 48 | 0.0002 | 1.0 |
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+ | 0.0003 | 49.0 | 49 | 0.0002 | 1.0 |
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+ | 0.0002 | 50.0 | 50 | 0.0002 | 1.0 |
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  ### Framework versions
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