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Image Classification
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Safetensors
resnet
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Eval Results
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  1. README.md +55 -53
  2. model.safetensors +1 -1
README.md CHANGED
@@ -23,7 +23,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.98
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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
@@ -33,8 +33,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0291
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- - Accuracy: 0.98
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  ## Model description
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@@ -66,56 +66,58 @@ The following hyperparameters were used during training:
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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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- | 3.0641 | 0.9524 | 15 | 2.8352 | 0.105 |
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- | 2.5528 | 1.9683 | 31 | 2.1193 | 0.37 |
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- | 2.0947 | 2.9841 | 47 | 1.2896 | 0.765 |
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- | 1.2275 | 4.0 | 63 | 0.6390 | 0.92 |
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- | 0.9026 | 4.9524 | 78 | 0.3464 | 0.915 |
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- | 0.4393 | 5.9683 | 94 | 0.1923 | 0.955 |
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- | 0.3047 | 6.9841 | 110 | 0.1146 | 0.97 |
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- | 0.2818 | 8.0 | 126 | 0.0957 | 0.975 |
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- | 0.1879 | 8.9524 | 141 | 0.0669 | 0.985 |
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- | 0.1641 | 9.9683 | 157 | 0.1301 | 0.965 |
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- | 0.1596 | 10.9841 | 173 | 0.0442 | 0.99 |
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- | 0.1934 | 12.0 | 189 | 0.0271 | 0.99 |
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- | 0.1716 | 12.9524 | 204 | 0.0643 | 0.98 |
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- | 0.0969 | 13.9683 | 220 | 0.0410 | 0.985 |
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- | 0.0992 | 14.9841 | 236 | 0.0381 | 0.97 |
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- | 0.0855 | 16.0 | 252 | 0.0700 | 0.975 |
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- | 0.1222 | 16.9524 | 267 | 0.0241 | 0.985 |
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- | 0.0943 | 17.9683 | 283 | 0.0223 | 0.995 |
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- | 0.0904 | 18.9841 | 299 | 0.0686 | 0.97 |
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- | 0.0851 | 20.0 | 315 | 0.0341 | 0.99 |
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- | 0.1098 | 20.9524 | 330 | 0.0183 | 0.995 |
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- | 0.0734 | 21.9683 | 346 | 0.0339 | 0.985 |
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- | 0.0795 | 22.9841 | 362 | 0.0261 | 0.985 |
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- | 0.08 | 24.0 | 378 | 0.0422 | 0.98 |
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- | 0.0785 | 24.9524 | 393 | 0.0471 | 0.99 |
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- | 0.0589 | 25.9683 | 409 | 0.0072 | 1.0 |
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- | 0.0586 | 26.9841 | 425 | 0.0239 | 0.995 |
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- | 0.066 | 28.0 | 441 | 0.0198 | 0.99 |
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- | 0.0493 | 28.9524 | 456 | 0.0337 | 0.985 |
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- | 0.0665 | 29.9683 | 472 | 0.0304 | 0.985 |
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- | 0.0691 | 30.9841 | 488 | 0.0371 | 0.98 |
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- | 0.0696 | 32.0 | 504 | 0.0191 | 0.99 |
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- | 0.0651 | 32.9524 | 519 | 0.0216 | 0.98 |
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- | 0.0477 | 33.9683 | 535 | 0.0114 | 1.0 |
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- | 0.0438 | 34.9841 | 551 | 0.0184 | 0.995 |
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- | 0.0576 | 36.0 | 567 | 0.0374 | 0.985 |
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- | 0.0448 | 36.9524 | 582 | 0.0433 | 0.99 |
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- | 0.0448 | 37.9683 | 598 | 0.0429 | 0.975 |
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- | 0.0385 | 38.9841 | 614 | 0.0164 | 0.995 |
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- | 0.0387 | 40.0 | 630 | 0.0205 | 0.99 |
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- | 0.0462 | 40.9524 | 645 | 0.0295 | 0.98 |
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- | 0.0685 | 41.9683 | 661 | 0.0239 | 0.985 |
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- | 0.0568 | 42.9841 | 677 | 0.0384 | 0.98 |
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- | 0.0441 | 44.0 | 693 | 0.0255 | 0.99 |
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- | 0.0599 | 44.9524 | 708 | 0.0253 | 0.985 |
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- | 0.0524 | 45.9683 | 724 | 0.0466 | 0.98 |
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- | 0.0586 | 46.9841 | 740 | 0.0527 | 0.975 |
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- | 0.0578 | 47.6190 | 750 | 0.0291 | 0.98 |
 
 
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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: 1.0
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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-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder 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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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 4.1894 | 1.0 | 47 | 4.0055 | 0.0517 |
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+ | 3.42 | 2.0 | 94 | 3.0416 | 0.325 |
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+ | 2.3936 | 3.0 | 141 | 1.8062 | 0.6783 |
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+ | 1.5314 | 4.0 | 188 | 0.9055 | 0.8467 |
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+ | 0.8617 | 5.0 | 235 | 0.4033 | 0.9167 |
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+ | 0.5096 | 6.0 | 282 | 0.1903 | 0.9567 |
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+ | 0.3393 | 7.0 | 329 | 0.0881 | 0.98 |
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+ | 0.2624 | 8.0 | 376 | 0.0777 | 0.975 |
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+ | 0.1734 | 9.0 | 423 | 0.0481 | 0.985 |
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+ | 0.142 | 10.0 | 470 | 0.0458 | 0.9917 |
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+ | 0.1467 | 11.0 | 517 | 0.0453 | 0.985 |
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+ | 0.1089 | 12.0 | 564 | 0.0149 | 0.9967 |
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+ | 0.0912 | 13.0 | 611 | 0.0126 | 0.9967 |
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+ | 0.0797 | 14.0 | 658 | 0.0152 | 0.9983 |
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+ | 0.0981 | 15.0 | 705 | 0.0056 | 1.0 |
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+ | 0.0702 | 16.0 | 752 | 0.0081 | 0.9983 |
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+ | 0.0785 | 17.0 | 799 | 0.0046 | 1.0 |
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+ | 0.0453 | 18.0 | 846 | 0.0084 | 0.9983 |
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+ | 0.0585 | 19.0 | 893 | 0.0218 | 0.9917 |
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+ | 0.0563 | 20.0 | 940 | 0.0023 | 1.0 |
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+ | 0.0505 | 21.0 | 987 | 0.0091 | 0.9967 |
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+ | 0.042 | 22.0 | 1034 | 0.0133 | 0.995 |
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+ | 0.0327 | 23.0 | 1081 | 0.0077 | 0.9983 |
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+ | 0.0234 | 24.0 | 1128 | 0.0019 | 1.0 |
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+ | 0.0291 | 25.0 | 1175 | 0.0014 | 1.0 |
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+ | 0.0397 | 26.0 | 1222 | 0.0024 | 1.0 |
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+ | 0.0484 | 27.0 | 1269 | 0.0087 | 0.995 |
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+ | 0.037 | 28.0 | 1316 | 0.0034 | 0.9983 |
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+ | 0.0254 | 29.0 | 1363 | 0.0008 | 1.0 |
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+ | 0.0252 | 30.0 | 1410 | 0.0010 | 1.0 |
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+ | 0.0346 | 31.0 | 1457 | 0.0067 | 1.0 |
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+ | 0.0244 | 32.0 | 1504 | 0.0028 | 0.9983 |
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+ | 0.0282 | 33.0 | 1551 | 0.0031 | 1.0 |
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+ | 0.0273 | 34.0 | 1598 | 0.0018 | 0.9983 |
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+ | 0.0291 | 35.0 | 1645 | 0.0040 | 0.9983 |
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+ | 0.0346 | 36.0 | 1692 | 0.0010 | 1.0 |
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+ | 0.0213 | 37.0 | 1739 | 0.0098 | 0.9967 |
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+ | 0.02 | 38.0 | 1786 | 0.0047 | 0.9983 |
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+ | 0.017 | 39.0 | 1833 | 0.0004 | 1.0 |
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+ | 0.0255 | 40.0 | 1880 | 0.0018 | 0.9983 |
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+ | 0.0386 | 41.0 | 1927 | 0.0046 | 0.9983 |
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+ | 0.0222 | 42.0 | 1974 | 0.0054 | 0.9983 |
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+ | 0.0173 | 43.0 | 2021 | 0.0054 | 0.9983 |
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+ | 0.0264 | 44.0 | 2068 | 0.0054 | 0.9983 |
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+ | 0.0164 | 45.0 | 2115 | 0.0013 | 1.0 |
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+ | 0.0166 | 46.0 | 2162 | 0.0014 | 1.0 |
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+ | 0.0169 | 47.0 | 2209 | 0.0003 | 1.0 |
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+ | 0.0256 | 48.0 | 2256 | 0.0002 | 1.0 |
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+ | 0.0224 | 49.0 | 2303 | 0.0062 | 0.9983 |
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+ | 0.02 | 50.0 | 2350 | 0.0002 | 1.0 |
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  ### Framework versions
model.safetensors CHANGED
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