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ebayes
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tree-crown-latest

Image Classification
Transformers
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Model card Files
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  1. README.md +224 -0
  2. config.json +48 -0
  3. model.safetensors +3 -0
  4. preprocessor_config.json +36 -0
  5. 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: ebayes/tree-crown-latest
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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: 0.95
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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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+ # ebayes/tree-crown-latest
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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: 0.1368
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+ - Accuracy: 0.95
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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: 2e-05
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+ - train_batch_size: 10
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+ - eval_batch_size: 4
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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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+ - num_epochs: 150
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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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+ | No log | 1.0 | 17 | 2.0491 | 0.45 |
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+ | No log | 2.0 | 34 | 1.7960 | 0.45 |
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+ | No log | 3.0 | 51 | 1.6265 | 0.5 |
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+ | No log | 4.0 | 68 | 1.4328 | 0.6 |
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+ | No log | 5.0 | 85 | 1.3004 | 0.7 |
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+ | No log | 6.0 | 102 | 1.1381 | 0.8 |
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+ | No log | 7.0 | 119 | 1.0114 | 0.9 |
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+ | No log | 8.0 | 136 | 0.9116 | 0.9 |
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+ | No log | 9.0 | 153 | 0.8490 | 0.9 |
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+ | No log | 10.0 | 170 | 0.7989 | 0.9 |
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+ | No log | 11.0 | 187 | 0.7392 | 0.9 |
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+ | No log | 12.0 | 204 | 0.6834 | 0.9 |
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+ | No log | 13.0 | 221 | 0.6688 | 0.9 |
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+ | No log | 14.0 | 238 | 0.6311 | 0.9 |
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+ | No log | 15.0 | 255 | 0.5847 | 0.9 |
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+ | No log | 16.0 | 272 | 0.5544 | 0.9 |
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+ | No log | 17.0 | 289 | 0.5521 | 0.9 |
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+ | No log | 18.0 | 306 | 0.5319 | 0.9 |
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+ | No log | 19.0 | 323 | 0.5228 | 0.9 |
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+ | No log | 20.0 | 340 | 0.4746 | 0.95 |
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+ | No log | 21.0 | 357 | 0.4913 | 0.95 |
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+ | No log | 22.0 | 374 | 0.4453 | 0.9 |
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+ | No log | 23.0 | 391 | 0.4333 | 0.95 |
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+ | No log | 24.0 | 408 | 0.4124 | 0.95 |
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+ | No log | 25.0 | 425 | 0.4303 | 0.95 |
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+ | No log | 26.0 | 442 | 0.4094 | 0.95 |
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+ | No log | 27.0 | 459 | 0.3597 | 0.95 |
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+ | No log | 28.0 | 476 | 0.3644 | 0.95 |
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+ | No log | 29.0 | 493 | 0.3723 | 0.95 |
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+ | 0.6158 | 30.0 | 510 | 0.3200 | 0.95 |
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+ | 0.6158 | 31.0 | 527 | 0.3223 | 0.95 |
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+ | 0.6158 | 32.0 | 544 | 0.3119 | 0.95 |
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+ | 0.6158 | 33.0 | 561 | 0.3002 | 0.95 |
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+ | 0.6158 | 34.0 | 578 | 0.2867 | 0.95 |
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+ | 0.6158 | 35.0 | 595 | 0.3419 | 0.9 |
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+ | 0.6158 | 36.0 | 612 | 0.3020 | 0.9 |
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+ | 0.6158 | 37.0 | 629 | 0.2393 | 0.95 |
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+ | 0.6158 | 38.0 | 646 | 0.3202 | 0.95 |
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+ | 0.6158 | 39.0 | 663 | 0.2727 | 0.95 |
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+ | 0.6158 | 40.0 | 680 | 0.2691 | 0.95 |
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+ | 0.6158 | 41.0 | 697 | 0.3346 | 0.9 |
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+ | 0.6158 | 42.0 | 714 | 0.2446 | 0.95 |
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+ | 0.6158 | 43.0 | 731 | 0.3373 | 0.9 |
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+ | 0.6158 | 44.0 | 748 | 0.2904 | 0.95 |
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+ | 0.6158 | 45.0 | 765 | 0.2307 | 0.95 |
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+ | 0.6158 | 46.0 | 782 | 0.2346 | 0.95 |
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+ | 0.6158 | 47.0 | 799 | 0.2314 | 0.95 |
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+ | 0.6158 | 48.0 | 816 | 0.2209 | 0.95 |
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+ | 0.6158 | 49.0 | 833 | 0.2233 | 0.95 |
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+ | 0.6158 | 50.0 | 850 | 0.2225 | 0.95 |
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+ | 0.6158 | 51.0 | 867 | 0.2326 | 0.95 |
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+ | 0.6158 | 52.0 | 884 | 0.2233 | 0.95 |
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+ | 0.6158 | 53.0 | 901 | 0.2248 | 0.95 |
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+ | 0.6158 | 54.0 | 918 | 0.2268 | 0.95 |
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+ | 0.6158 | 55.0 | 935 | 0.2130 | 0.95 |
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+ | 0.6158 | 56.0 | 952 | 0.2164 | 0.95 |
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+ | 0.6158 | 57.0 | 969 | 0.1972 | 0.95 |
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+ | 0.6158 | 58.0 | 986 | 0.2374 | 0.95 |
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+ | 0.1237 | 59.0 | 1003 | 0.2425 | 0.95 |
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+ | 0.1237 | 60.0 | 1020 | 0.1907 | 0.95 |
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+ | 0.1237 | 61.0 | 1037 | 0.3103 | 0.9 |
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+ | 0.1237 | 62.0 | 1054 | 0.2309 | 0.95 |
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+ | 0.1237 | 63.0 | 1071 | 0.1982 | 0.95 |
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+ | 0.1237 | 64.0 | 1088 | 0.2661 | 0.9 |
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+ | 0.1237 | 65.0 | 1105 | 0.1739 | 0.95 |
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+ | 0.1237 | 66.0 | 1122 | 0.1958 | 0.95 |
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+ | 0.1237 | 67.0 | 1139 | 0.1729 | 0.95 |
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+ | 0.1237 | 68.0 | 1156 | 0.1884 | 0.95 |
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+ | 0.1237 | 69.0 | 1173 | 0.1958 | 0.95 |
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+ | 0.1237 | 70.0 | 1190 | 0.1949 | 0.95 |
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+ | 0.1237 | 71.0 | 1207 | 0.1700 | 0.95 |
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+ | 0.1237 | 72.0 | 1224 | 0.1770 | 0.95 |
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+ | 0.1237 | 73.0 | 1241 | 0.1789 | 0.95 |
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+ | 0.1237 | 74.0 | 1258 | 0.2202 | 0.95 |
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+ | 0.1237 | 75.0 | 1275 | 0.2005 | 0.95 |
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+ | 0.1237 | 76.0 | 1292 | 0.1734 | 0.95 |
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+ | 0.1237 | 77.0 | 1309 | 0.1633 | 0.95 |
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+ | 0.1237 | 78.0 | 1326 | 0.1468 | 0.95 |
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+ | 0.1237 | 79.0 | 1343 | 0.1619 | 0.95 |
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+ | 0.1237 | 80.0 | 1360 | 0.1706 | 0.95 |
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+ | 0.1237 | 81.0 | 1377 | 0.1745 | 0.95 |
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+ | 0.1237 | 82.0 | 1394 | 0.2146 | 0.95 |
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+ | 0.1237 | 83.0 | 1411 | 0.1990 | 0.95 |
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+ | 0.1237 | 84.0 | 1428 | 0.1682 | 0.95 |
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+ | 0.1237 | 85.0 | 1445 | 0.1891 | 0.95 |
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+ | 0.1237 | 86.0 | 1462 | 0.1646 | 0.95 |
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+ | 0.1237 | 87.0 | 1479 | 0.2234 | 0.95 |
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+ | 0.1237 | 88.0 | 1496 | 0.2469 | 0.9 |
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+ | 0.0723 | 89.0 | 1513 | 0.1513 | 0.95 |
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+ | 0.0723 | 90.0 | 1530 | 0.1638 | 0.95 |
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+ | 0.0723 | 91.0 | 1547 | 0.1706 | 0.95 |
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+ | 0.0723 | 92.0 | 1564 | 0.1578 | 0.95 |
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+ | 0.0723 | 93.0 | 1581 | 0.1465 | 0.95 |
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+ | 0.0723 | 94.0 | 1598 | 0.1433 | 0.95 |
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+ | 0.0723 | 95.0 | 1615 | 0.1438 | 0.95 |
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+ | 0.0723 | 96.0 | 1632 | 0.1543 | 0.95 |
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+ | 0.0723 | 97.0 | 1649 | 0.1528 | 0.95 |
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+ | 0.0723 | 98.0 | 1666 | 0.1807 | 0.95 |
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+ | 0.0723 | 99.0 | 1683 | 0.2142 | 0.95 |
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+ | 0.0723 | 100.0 | 1700 | 0.2056 | 0.95 |
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+ | 0.0723 | 101.0 | 1717 | 0.1817 | 0.95 |
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+ | 0.0723 | 102.0 | 1734 | 0.2271 | 0.95 |
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+ | 0.0723 | 103.0 | 1751 | 0.2560 | 0.9 |
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+ | 0.0723 | 104.0 | 1768 | 0.1631 | 0.95 |
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+ | 0.0723 | 105.0 | 1785 | 0.1828 | 0.95 |
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+ | 0.0723 | 106.0 | 1802 | 0.2608 | 0.95 |
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+ | 0.0723 | 107.0 | 1819 | 0.2562 | 0.95 |
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+ | 0.0723 | 108.0 | 1836 | 0.1666 | 0.95 |
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+ | 0.0723 | 109.0 | 1853 | 0.1619 | 0.95 |
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+ | 0.0723 | 110.0 | 1870 | 0.1504 | 0.95 |
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+ | 0.0723 | 111.0 | 1887 | 0.1433 | 0.95 |
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+ | 0.0723 | 112.0 | 1904 | 0.1457 | 0.95 |
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+ | 0.0723 | 113.0 | 1921 | 0.1288 | 1.0 |
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+ | 0.0723 | 114.0 | 1938 | 0.1401 | 1.0 |
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+ | 0.0723 | 115.0 | 1955 | 0.1281 | 0.95 |
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+ | 0.0723 | 116.0 | 1972 | 0.1267 | 0.95 |
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+ | 0.0723 | 117.0 | 1989 | 0.1288 | 0.95 |
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+ | 0.051 | 118.0 | 2006 | 0.1473 | 0.95 |
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+ | 0.051 | 119.0 | 2023 | 0.1106 | 1.0 |
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+ | 0.051 | 120.0 | 2040 | 0.1097 | 1.0 |
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+ | 0.051 | 121.0 | 2057 | 0.1379 | 1.0 |
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+ | 0.051 | 122.0 | 2074 | 0.1347 | 1.0 |
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+ | 0.051 | 123.0 | 2091 | 0.1302 | 0.95 |
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+ | 0.051 | 124.0 | 2108 | 0.1599 | 0.95 |
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+ | 0.051 | 125.0 | 2125 | 0.1574 | 0.95 |
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+ | 0.051 | 126.0 | 2142 | 0.1541 | 0.95 |
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+ | 0.051 | 127.0 | 2159 | 0.1517 | 0.95 |
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+ | 0.051 | 128.0 | 2176 | 0.1462 | 0.95 |
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+ | 0.051 | 129.0 | 2193 | 0.1574 | 0.95 |
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+ | 0.051 | 130.0 | 2210 | 0.1598 | 0.95 |
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+ | 0.051 | 131.0 | 2227 | 0.1520 | 0.95 |
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+ | 0.051 | 132.0 | 2244 | 0.1595 | 0.95 |
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+ | 0.051 | 133.0 | 2261 | 0.1555 | 0.95 |
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+ | 0.051 | 134.0 | 2278 | 0.1515 | 0.95 |
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+ | 0.051 | 135.0 | 2295 | 0.1686 | 0.95 |
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+ | 0.051 | 136.0 | 2312 | 0.1670 | 0.95 |
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+ | 0.051 | 137.0 | 2329 | 0.1533 | 0.95 |
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+ | 0.051 | 138.0 | 2346 | 0.1472 | 0.95 |
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+ | 0.051 | 139.0 | 2363 | 0.1530 | 0.95 |
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+ | 0.051 | 140.0 | 2380 | 0.1563 | 0.95 |
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+ | 0.051 | 141.0 | 2397 | 0.1500 | 0.95 |
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+ | 0.051 | 142.0 | 2414 | 0.1462 | 0.95 |
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+ | 0.051 | 143.0 | 2431 | 0.1432 | 0.95 |
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+ | 0.051 | 144.0 | 2448 | 0.1417 | 0.95 |
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+ | 0.051 | 145.0 | 2465 | 0.1414 | 0.95 |
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+ | 0.051 | 146.0 | 2482 | 0.1362 | 0.95 |
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+ | 0.051 | 147.0 | 2499 | 0.1358 | 0.95 |
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+ | 0.0491 | 148.0 | 2516 | 0.1366 | 0.95 |
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+ | 0.0491 | 149.0 | 2533 | 0.1367 | 0.95 |
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+ | 0.0491 | 150.0 | 2550 | 0.1368 | 0.95 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.40.2
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.19.1
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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": "Cecropia",
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+ "1": "Cecropia_concolor",
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+ "2": "Cecropia_sciadophylla",
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+ "3": "Cecropia_sp_branches",
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+ "4": "Distinctive rosette",
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+ "5": "Euterpe",
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+ "6": "Fallen_tree",
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+ "7": "Flowering_canopy",
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+ "8": "Mauritia_flexuosa",
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+ "9": "Oenocarpus_bacaba"
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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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+ "Cecropia": 0,
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+ "Cecropia_concolor": 1,
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+ "Cecropia_sciadophylla": 2,
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+ "Cecropia_sp_branches": 3,
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+ "Distinctive rosette": 4,
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+ "Euterpe": 5,
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+ "Fallen_tree": 6,
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+ "Flowering_canopy": 7,
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+ "Mauritia_flexuosa": 8,
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+ "Oenocarpus_bacaba": 9
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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": "4.40.2"
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+ }
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+ size 343248584
preprocessor_config.json ADDED
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+ {
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+ "_valid_processor_keys": [
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+ "images",
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+ "do_resize",
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+ "size",
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+ "resample",
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+ "do_rescale",
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+ "rescale_factor",
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+ "do_normalize",
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+ "image_mean",
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+ "image_std",
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+ "return_tensors",
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+ "data_format",
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+ "input_data_format"
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+ ],
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+ "do_normalize": true,
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+ "do_rescale": true,
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+ "do_resize": true,
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+ "image_mean": [
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+ 0.5,
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+ 0.5,
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+ 0.5
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+ ],
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+ "image_processor_type": "ViTImageProcessor",
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+ "image_std": [
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+ 0.5,
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+ 0.5,
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+ 0.5
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+ ],
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+ "resample": 2,
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+ "rescale_factor": 0.00392156862745098,
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+ "size": {
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+ "height": 224,
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+ "width": 224
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+ }
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+ }
training_args.bin ADDED
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