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meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_5

Image Classification
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vit
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  1. README.md +175 -0
  2. config.json +34 -0
  3. model.safetensors +3 -0
  4. preprocessor_config.json +36 -0
  5. runs/Jun15_01-54-21_DESKTOP-QA5IM1O/events.out.tfevents.1718387662.DESKTOP-QA5IM1O.17688.0 +3 -0
  6. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: google/vit-base-patch16-224-in21k
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - imagefolder
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_5
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+ results:
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+ - task:
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+ name: Image Classification
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+ type: image-classification
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+ dataset:
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+ name: imagefolder
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+ type: imagefolder
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+ config: default
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+ split: train
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+ args: default
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9314641744548287
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+ ---
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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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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_5
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+
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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 the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2106
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+ - Accuracy: 0.9315
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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+ - seed: 42
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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: 100
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 1.1058 | 1.0 | 21 | 1.0981 | 0.3707 |
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+ | 1.0889 | 2.0 | 42 | 1.0703 | 0.4673 |
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+ | 1.0551 | 3.0 | 63 | 1.0219 | 0.5265 |
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+ | 0.9782 | 4.0 | 84 | 0.9533 | 0.5389 |
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+ | 0.916 | 5.0 | 105 | 0.8848 | 0.6293 |
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+ | 0.8579 | 6.0 | 126 | 0.7977 | 0.6417 |
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+ | 0.7614 | 7.0 | 147 | 0.8384 | 0.6511 |
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+ | 0.7643 | 8.0 | 168 | 0.6850 | 0.7632 |
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+ | 0.6182 | 9.0 | 189 | 0.7255 | 0.7134 |
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+ | 0.5827 | 10.0 | 210 | 0.7923 | 0.6604 |
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+ | 0.5751 | 11.0 | 231 | 0.6706 | 0.7196 |
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+ | 0.5045 | 12.0 | 252 | 0.5499 | 0.7726 |
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+ | 0.473 | 13.0 | 273 | 0.7024 | 0.6885 |
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+ | 0.4922 | 14.0 | 294 | 0.6377 | 0.7165 |
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+ | 0.4808 | 15.0 | 315 | 0.4886 | 0.8224 |
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+ | 0.4047 | 16.0 | 336 | 0.4426 | 0.8318 |
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+ | 0.3982 | 17.0 | 357 | 0.5656 | 0.7508 |
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+ | 0.3279 | 18.0 | 378 | 0.5265 | 0.7819 |
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+ | 0.3421 | 19.0 | 399 | 0.4606 | 0.8100 |
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+ | 0.2593 | 20.0 | 420 | 0.4638 | 0.8100 |
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+ | 0.2542 | 21.0 | 441 | 0.4388 | 0.8474 |
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+ | 0.2554 | 22.0 | 462 | 0.5237 | 0.8100 |
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+ | 0.2488 | 23.0 | 483 | 0.4238 | 0.8474 |
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+ | 0.2069 | 24.0 | 504 | 0.3515 | 0.8847 |
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+ | 0.295 | 25.0 | 525 | 0.4371 | 0.8318 |
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+ | 0.2213 | 26.0 | 546 | 0.3588 | 0.8847 |
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+ | 0.1961 | 27.0 | 567 | 0.5877 | 0.7975 |
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+ | 0.27 | 28.0 | 588 | 0.3720 | 0.8567 |
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+ | 0.1791 | 29.0 | 609 | 0.2952 | 0.9065 |
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+ | 0.1763 | 30.0 | 630 | 0.3312 | 0.8816 |
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+ | 0.1664 | 31.0 | 651 | 0.3770 | 0.8754 |
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+ | 0.2713 | 32.0 | 672 | 0.4695 | 0.8287 |
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+ | 0.1645 | 33.0 | 693 | 0.4069 | 0.8505 |
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+ | 0.1942 | 34.0 | 714 | 0.4516 | 0.8380 |
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+ | 0.1435 | 35.0 | 735 | 0.2383 | 0.9252 |
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+ | 0.1399 | 36.0 | 756 | 0.4790 | 0.8442 |
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+ | 0.1615 | 37.0 | 777 | 0.3230 | 0.8785 |
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+ | 0.1405 | 38.0 | 798 | 0.2635 | 0.9065 |
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+ | 0.1569 | 39.0 | 819 | 0.4816 | 0.8598 |
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+ | 0.1332 | 40.0 | 840 | 0.2859 | 0.8847 |
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+ | 0.1108 | 41.0 | 861 | 0.3786 | 0.8723 |
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+ | 0.1014 | 42.0 | 882 | 0.3215 | 0.9003 |
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+ | 0.1186 | 43.0 | 903 | 0.3652 | 0.8785 |
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+ | 0.1021 | 44.0 | 924 | 0.2088 | 0.9221 |
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+ | 0.1444 | 45.0 | 945 | 0.3646 | 0.8723 |
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+ | 0.1058 | 46.0 | 966 | 0.3530 | 0.8847 |
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+ | 0.1297 | 47.0 | 987 | 0.4002 | 0.8629 |
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+ | 0.0997 | 48.0 | 1008 | 0.2928 | 0.9034 |
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+ | 0.1246 | 49.0 | 1029 | 0.2772 | 0.9065 |
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+ | 0.0989 | 50.0 | 1050 | 0.2459 | 0.9221 |
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+ | 0.0794 | 51.0 | 1071 | 0.1970 | 0.9283 |
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+ | 0.0698 | 52.0 | 1092 | 0.3217 | 0.8847 |
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+ | 0.0767 | 53.0 | 1113 | 0.2706 | 0.9190 |
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+ | 0.0966 | 54.0 | 1134 | 0.2246 | 0.9252 |
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+ | 0.0816 | 55.0 | 1155 | 0.2585 | 0.9065 |
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+ | 0.0732 | 56.0 | 1176 | 0.3289 | 0.8910 |
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+ | 0.0992 | 57.0 | 1197 | 0.2790 | 0.9128 |
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+ | 0.0684 | 58.0 | 1218 | 0.2508 | 0.9252 |
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+ | 0.0972 | 59.0 | 1239 | 0.2558 | 0.9190 |
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+ | 0.0702 | 60.0 | 1260 | 0.2411 | 0.9190 |
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+ | 0.0602 | 61.0 | 1281 | 0.4097 | 0.8660 |
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+ | 0.0912 | 62.0 | 1302 | 0.2274 | 0.9252 |
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+ | 0.0556 | 63.0 | 1323 | 0.1940 | 0.9408 |
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+ | 0.0727 | 64.0 | 1344 | 0.2389 | 0.9190 |
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+ | 0.0657 | 65.0 | 1365 | 0.2964 | 0.9128 |
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+ | 0.0486 | 66.0 | 1386 | 0.2597 | 0.9252 |
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+ | 0.0639 | 67.0 | 1407 | 0.2272 | 0.9346 |
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+ | 0.0614 | 68.0 | 1428 | 0.1927 | 0.9470 |
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+ | 0.0444 | 69.0 | 1449 | 0.2771 | 0.9190 |
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+ | 0.0648 | 70.0 | 1470 | 0.2345 | 0.9283 |
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+ | 0.051 | 71.0 | 1491 | 0.2210 | 0.9159 |
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+ | 0.0514 | 72.0 | 1512 | 0.2260 | 0.9346 |
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+ | 0.0473 | 73.0 | 1533 | 0.2496 | 0.9252 |
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+ | 0.0637 | 74.0 | 1554 | 0.3152 | 0.9128 |
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+ | 0.0538 | 75.0 | 1575 | 0.2527 | 0.9221 |
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+ | 0.0622 | 76.0 | 1596 | 0.2148 | 0.9408 |
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+ | 0.0437 | 77.0 | 1617 | 0.2386 | 0.9190 |
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+ | 0.07 | 78.0 | 1638 | 0.2013 | 0.9315 |
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+ | 0.0599 | 79.0 | 1659 | 0.2532 | 0.9346 |
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+ | 0.0367 | 80.0 | 1680 | 0.1835 | 0.9439 |
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+ | 0.0386 | 81.0 | 1701 | 0.2204 | 0.9283 |
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+ | 0.0372 | 82.0 | 1722 | 0.2417 | 0.9283 |
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+ | 0.0516 | 83.0 | 1743 | 0.3098 | 0.9190 |
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+ | 0.0378 | 84.0 | 1764 | 0.1587 | 0.9533 |
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+ | 0.0371 | 85.0 | 1785 | 0.2041 | 0.9377 |
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+ | 0.0378 | 86.0 | 1806 | 0.2343 | 0.9377 |
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+ | 0.0288 | 87.0 | 1827 | 0.1963 | 0.9439 |
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+ | 0.0272 | 88.0 | 1848 | 0.2122 | 0.9408 |
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+ | 0.0293 | 89.0 | 1869 | 0.0979 | 0.9751 |
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+ | 0.037 | 90.0 | 1890 | 0.2385 | 0.9221 |
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+ | 0.0453 | 91.0 | 1911 | 0.2056 | 0.9439 |
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+ | 0.0478 | 92.0 | 1932 | 0.1861 | 0.9439 |
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+ | 0.0241 | 93.0 | 1953 | 0.2129 | 0.9470 |
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+ | 0.0404 | 94.0 | 1974 | 0.1806 | 0.9502 |
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+ | 0.0224 | 95.0 | 1995 | 0.1698 | 0.9377 |
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+ | 0.0194 | 96.0 | 2016 | 0.1960 | 0.9533 |
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+ | 0.022 | 97.0 | 2037 | 0.2140 | 0.9377 |
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+ | 0.0328 | 98.0 | 2058 | 0.1684 | 0.9502 |
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+ | 0.0443 | 99.0 | 2079 | 0.2536 | 0.9283 |
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+ | 0.0386 | 100.0 | 2100 | 0.2106 | 0.9315 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.2
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+ - Pytorch 2.3.1
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1
config.json ADDED
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+ {
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+ "_name_or_path": "google/vit-base-patch16-224-in21k",
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+ "architectures": [
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+ "ViTForImageClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.0,
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+ "encoder_stride": 16,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.0,
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "fresh1",
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+ "1": "fresh2",
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+ "2": "fresh3"
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+ },
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+ "image_size": 224,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "label2id": {
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+ "fresh1": "0",
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+ "fresh2": "1",
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+ "fresh3": "2"
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "model_type": "vit",
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+ "num_attention_heads": 12,
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+ "num_channels": 3,
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+ "num_hidden_layers": 12,
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+ "patch_size": 16,
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+ "problem_type": "single_label_classification",
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+ "qkv_bias": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.41.2"
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+ }
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