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Wound-Image-classification

Image Classification
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Safetensors
vit
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  1. README.md +7 -67
  2. runs/Mar12_06-29-58_ff168037f406/events.out.tfevents.1710224998.ff168037f406.34.2 +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 [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2911
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- - Accuracy: 0.9473
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  ## Model description
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@@ -46,76 +46,16 @@ 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: 64
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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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- | 2.0537 | 1.0 | 147 | 1.6140 | 0.5544 |
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- | 1.1475 | 2.0 | 294 | 0.9298 | 0.7483 |
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- | 0.7369 | 3.0 | 441 | 0.7248 | 0.7823 |
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- | 0.5192 | 4.0 | 588 | 0.6039 | 0.8027 |
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- | 0.4271 | 5.0 | 735 | 0.6372 | 0.8078 |
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- | 0.4291 | 6.0 | 882 | 0.6003 | 0.8180 |
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- | 0.4395 | 7.0 | 1029 | 0.7019 | 0.7959 |
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- | 0.3927 | 8.0 | 1176 | 0.6414 | 0.7959 |
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- | 0.3743 | 9.0 | 1323 | 0.6824 | 0.8180 |
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- | 0.2937 | 10.0 | 1470 | 0.5196 | 0.8537 |
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- | 0.2839 | 11.0 | 1617 | 0.3767 | 0.9014 |
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- | 0.2329 | 12.0 | 1764 | 0.5219 | 0.8673 |
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- | 0.2475 | 13.0 | 1911 | 0.6006 | 0.8401 |
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- | 0.2188 | 14.0 | 2058 | 0.5661 | 0.8469 |
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- | 0.2095 | 15.0 | 2205 | 0.3878 | 0.8929 |
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- | 0.1848 | 16.0 | 2352 | 0.6360 | 0.8248 |
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- | 0.2041 | 17.0 | 2499 | 0.4145 | 0.9014 |
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- | 0.17 | 18.0 | 2646 | 0.4387 | 0.8980 |
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- | 0.1774 | 19.0 | 2793 | 0.4279 | 0.8759 |
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- | 0.1681 | 20.0 | 2940 | 0.4969 | 0.8537 |
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- | 0.1876 | 21.0 | 3087 | 0.2964 | 0.9286 |
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- | 0.1334 | 22.0 | 3234 | 0.4209 | 0.8861 |
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- | 0.1756 | 23.0 | 3381 | 0.4484 | 0.8912 |
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- | 0.1548 | 24.0 | 3528 | 0.3929 | 0.8980 |
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- | 0.1413 | 25.0 | 3675 | 0.4843 | 0.8912 |
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- | 0.1177 | 26.0 | 3822 | 0.4585 | 0.8912 |
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- | 0.114 | 27.0 | 3969 | 0.4408 | 0.8912 |
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- | 0.1091 | 28.0 | 4116 | 0.3761 | 0.9031 |
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- | 0.1113 | 29.0 | 4263 | 0.3766 | 0.9201 |
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- | 0.1186 | 30.0 | 4410 | 0.3889 | 0.9014 |
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- | 0.1214 | 31.0 | 4557 | 0.2984 | 0.9320 |
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- | 0.1126 | 32.0 | 4704 | 0.2682 | 0.9235 |
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- | 0.0892 | 33.0 | 4851 | 0.4274 | 0.9048 |
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- | 0.1035 | 34.0 | 4998 | 0.3356 | 0.9252 |
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- | 0.0873 | 35.0 | 5145 | 0.3848 | 0.9218 |
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- | 0.0664 | 36.0 | 5292 | 0.3866 | 0.9184 |
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- | 0.1018 | 37.0 | 5439 | 0.3761 | 0.9167 |
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- | 0.0715 | 38.0 | 5586 | 0.3070 | 0.9303 |
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- | 0.0684 | 39.0 | 5733 | 0.2990 | 0.9269 |
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- | 0.0754 | 40.0 | 5880 | 0.3333 | 0.9388 |
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- | 0.0667 | 41.0 | 6027 | 0.3535 | 0.9269 |
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- | 0.0733 | 42.0 | 6174 | 0.3097 | 0.9252 |
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- | 0.0688 | 43.0 | 6321 | 0.2885 | 0.9388 |
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- | 0.0692 | 44.0 | 6468 | 0.4060 | 0.9201 |
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- | 0.0533 | 45.0 | 6615 | 0.3958 | 0.9252 |
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- | 0.0676 | 46.0 | 6762 | 0.3517 | 0.9303 |
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- | 0.0437 | 47.0 | 6909 | 0.3602 | 0.9269 |
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- | 0.0603 | 48.0 | 7056 | 0.3761 | 0.9167 |
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- | 0.0457 | 49.0 | 7203 | 0.2960 | 0.9354 |
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- | 0.0584 | 50.0 | 7350 | 0.2682 | 0.9473 |
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- | 0.0554 | 51.0 | 7497 | 0.3691 | 0.9218 |
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- | 0.0502 | 52.0 | 7644 | 0.3292 | 0.9303 |
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- | 0.0483 | 53.0 | 7791 | 0.3140 | 0.9422 |
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- | 0.0246 | 54.0 | 7938 | 0.3333 | 0.9354 |
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- | 0.0359 | 55.0 | 8085 | 0.3608 | 0.9405 |
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- | 0.0507 | 56.0 | 8232 | 0.3366 | 0.9337 |
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- | 0.0558 | 57.0 | 8379 | 0.2612 | 0.9422 |
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- | 0.0336 | 58.0 | 8526 | 0.2656 | 0.9354 |
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- | 0.0244 | 59.0 | 8673 | 0.2705 | 0.9524 |
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- | 0.0222 | 60.0 | 8820 | 0.2624 | 0.9422 |
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- | 0.0302 | 61.0 | 8967 | 0.2808 | 0.9456 |
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- | 0.03 | 62.0 | 9114 | 0.3013 | 0.9388 |
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- | 0.0252 | 63.0 | 9261 | 0.2705 | 0.9507 |
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- | 0.0208 | 64.0 | 9408 | 0.2911 | 0.9473 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3558
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+ - Accuracy: 0.8929
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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: 4
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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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+ | 1.3684 | 1.0 | 147 | 0.9023 | 0.7279 |
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+ | 0.6318 | 2.0 | 294 | 0.7183 | 0.7772 |
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+ | 0.412 | 3.0 | 441 | 0.4634 | 0.8554 |
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+ | 0.2348 | 4.0 | 588 | 0.3558 | 0.8929 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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