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resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.9

Image Classification
Transformers
PyTorch
resnet
Generated from Trainer
Inference Endpoints
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  1. README.md +116 -0
  2. config.json +64 -0
  3. pytorch_model.bin +3 -0
  4. test-logits.npz +3 -0
  5. test-references.npz +3 -0
  6. training_args.bin +3 -0
  7. validation-logits.npz +3 -0
  8. validation-references.npz +3 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: microsoft/resnet-50
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.9
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+ results: []
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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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+ # resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.9
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+
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+ This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.8862
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+ - Accuracy: 0.675
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+ - Brier Loss: 0.4233
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+ - Nll: 2.4267
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+ - F1 Micro: 0.675
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+ - F1 Macro: 0.6266
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+ - Ece: 0.2528
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+ - Aurc: 0.1205
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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: 0.0001
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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: 50
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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 | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
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+ | No log | 1.0 | 13 | 2.1186 | 0.165 | 0.8967 | 8.5414 | 0.165 | 0.1128 | 0.2087 | 0.8330 |
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+ | No log | 2.0 | 26 | 2.1139 | 0.14 | 0.8960 | 8.0889 | 0.14 | 0.0907 | 0.1924 | 0.8318 |
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+ | No log | 3.0 | 39 | 2.0743 | 0.195 | 0.8880 | 6.6316 | 0.195 | 0.1098 | 0.2224 | 0.7879 |
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+ | No log | 4.0 | 52 | 2.0101 | 0.205 | 0.8741 | 6.0411 | 0.205 | 0.0851 | 0.2448 | 0.7302 |
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+ | No log | 5.0 | 65 | 1.9697 | 0.22 | 0.8650 | 5.8808 | 0.22 | 0.1090 | 0.2441 | 0.7307 |
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+ | No log | 6.0 | 78 | 1.8642 | 0.27 | 0.8396 | 6.0693 | 0.27 | 0.1370 | 0.2742 | 0.6623 |
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+ | No log | 7.0 | 91 | 1.7716 | 0.35 | 0.8100 | 5.7342 | 0.35 | 0.1964 | 0.3131 | 0.4496 |
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+ | No log | 8.0 | 104 | 1.7580 | 0.33 | 0.8084 | 5.8902 | 0.33 | 0.1762 | 0.3185 | 0.5663 |
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+ | No log | 9.0 | 117 | 1.7346 | 0.425 | 0.8000 | 5.5871 | 0.425 | 0.2645 | 0.3466 | 0.3888 |
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+ | No log | 10.0 | 130 | 1.6557 | 0.365 | 0.7744 | 5.2246 | 0.3650 | 0.2256 | 0.2890 | 0.5081 |
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+ | No log | 11.0 | 143 | 1.5067 | 0.46 | 0.7014 | 4.7492 | 0.46 | 0.3053 | 0.3024 | 0.2923 |
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+ | No log | 12.0 | 156 | 1.5340 | 0.425 | 0.7212 | 4.4923 | 0.425 | 0.2746 | 0.2833 | 0.3650 |
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+ | No log | 13.0 | 169 | 1.5064 | 0.495 | 0.7111 | 4.1576 | 0.495 | 0.3443 | 0.3225 | 0.2907 |
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+ | No log | 14.0 | 182 | 1.4767 | 0.54 | 0.6972 | 3.7984 | 0.54 | 0.3804 | 0.3381 | 0.2831 |
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+ | No log | 15.0 | 195 | 1.3709 | 0.525 | 0.6453 | 3.7435 | 0.525 | 0.3771 | 0.3188 | 0.2541 |
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+ | No log | 16.0 | 208 | 1.3204 | 0.535 | 0.6223 | 3.4971 | 0.535 | 0.3919 | 0.3115 | 0.2424 |
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+ | No log | 17.0 | 221 | 1.4782 | 0.465 | 0.7008 | 3.4793 | 0.465 | 0.3731 | 0.3311 | 0.4138 |
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+ | No log | 18.0 | 234 | 1.3456 | 0.49 | 0.6523 | 3.3409 | 0.49 | 0.3839 | 0.2832 | 0.3570 |
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+ | No log | 19.0 | 247 | 1.2137 | 0.625 | 0.5708 | 3.4778 | 0.625 | 0.5087 | 0.3030 | 0.1904 |
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+ | No log | 20.0 | 260 | 1.3527 | 0.565 | 0.6484 | 3.3840 | 0.565 | 0.4761 | 0.3402 | 0.3349 |
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+ | No log | 21.0 | 273 | 1.1692 | 0.6 | 0.5633 | 2.9586 | 0.6 | 0.4932 | 0.3138 | 0.2299 |
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+ | No log | 22.0 | 286 | 1.1144 | 0.65 | 0.5253 | 2.9930 | 0.65 | 0.5281 | 0.2768 | 0.1585 |
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+ | No log | 23.0 | 299 | 1.0749 | 0.635 | 0.5048 | 2.8481 | 0.635 | 0.5404 | 0.2378 | 0.1642 |
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+ | No log | 24.0 | 312 | 1.0619 | 0.665 | 0.5018 | 2.7665 | 0.665 | 0.5653 | 0.2741 | 0.1533 |
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+ | No log | 25.0 | 325 | 1.0733 | 0.68 | 0.5036 | 2.6592 | 0.68 | 0.5960 | 0.2948 | 0.1633 |
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+ | No log | 26.0 | 338 | 1.0319 | 0.655 | 0.4930 | 2.6467 | 0.655 | 0.5786 | 0.2598 | 0.1576 |
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+ | No log | 27.0 | 351 | 1.0147 | 0.665 | 0.4805 | 2.6123 | 0.665 | 0.5877 | 0.2406 | 0.1405 |
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+ | No log | 28.0 | 364 | 0.9862 | 0.675 | 0.4734 | 2.4990 | 0.675 | 0.5876 | 0.2512 | 0.1474 |
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+ | No log | 29.0 | 377 | 0.9816 | 0.685 | 0.4696 | 2.5984 | 0.685 | 0.6131 | 0.2446 | 0.1428 |
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+ | No log | 30.0 | 390 | 0.9755 | 0.66 | 0.4698 | 2.5609 | 0.66 | 0.6009 | 0.2562 | 0.1555 |
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+ | No log | 31.0 | 403 | 0.9789 | 0.7 | 0.4601 | 2.6827 | 0.7 | 0.6374 | 0.2667 | 0.1271 |
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+ | No log | 32.0 | 416 | 0.9426 | 0.695 | 0.4501 | 2.5256 | 0.695 | 0.6315 | 0.2560 | 0.1420 |
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+ | No log | 33.0 | 429 | 0.9428 | 0.695 | 0.4461 | 2.6429 | 0.695 | 0.6298 | 0.2250 | 0.1243 |
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+ | No log | 34.0 | 442 | 0.9370 | 0.675 | 0.4455 | 2.5812 | 0.675 | 0.6061 | 0.2523 | 0.1284 |
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+ | No log | 35.0 | 455 | 0.9290 | 0.68 | 0.4391 | 2.3724 | 0.68 | 0.6174 | 0.2459 | 0.1361 |
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+ | No log | 36.0 | 468 | 0.9190 | 0.66 | 0.4393 | 2.3838 | 0.66 | 0.6140 | 0.2201 | 0.1327 |
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+ | No log | 37.0 | 481 | 0.9061 | 0.685 | 0.4310 | 2.3683 | 0.685 | 0.6390 | 0.2222 | 0.1196 |
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+ | No log | 38.0 | 494 | 0.9184 | 0.705 | 0.4387 | 2.5054 | 0.705 | 0.6444 | 0.2479 | 0.1191 |
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+ | 1.0876 | 39.0 | 507 | 0.9185 | 0.685 | 0.4425 | 2.4429 | 0.685 | 0.6327 | 0.2450 | 0.1337 |
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+ | 1.0876 | 40.0 | 520 | 0.9002 | 0.66 | 0.4289 | 2.4439 | 0.66 | 0.6226 | 0.2152 | 0.1302 |
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+ | 1.0876 | 41.0 | 533 | 0.9027 | 0.68 | 0.4319 | 2.3802 | 0.68 | 0.6179 | 0.2247 | 0.1200 |
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+ | 1.0876 | 42.0 | 546 | 0.8977 | 0.68 | 0.4321 | 2.3577 | 0.68 | 0.6195 | 0.2296 | 0.1250 |
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+ | 1.0876 | 43.0 | 559 | 0.8861 | 0.685 | 0.4215 | 2.3150 | 0.685 | 0.6324 | 0.1870 | 0.1198 |
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+ | 1.0876 | 44.0 | 572 | 0.8913 | 0.68 | 0.4235 | 2.4228 | 0.68 | 0.6328 | 0.2260 | 0.1193 |
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+ | 1.0876 | 45.0 | 585 | 0.8895 | 0.675 | 0.4251 | 2.4104 | 0.675 | 0.6264 | 0.2409 | 0.1208 |
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+ | 1.0876 | 46.0 | 598 | 0.8901 | 0.665 | 0.4223 | 2.3598 | 0.665 | 0.6146 | 0.2235 | 0.1208 |
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+ | 1.0876 | 47.0 | 611 | 0.8809 | 0.68 | 0.4206 | 2.3528 | 0.68 | 0.6233 | 0.2306 | 0.1222 |
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+ | 1.0876 | 48.0 | 624 | 0.8845 | 0.69 | 0.4243 | 2.4251 | 0.69 | 0.6362 | 0.2232 | 0.1219 |
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+ | 1.0876 | 49.0 | 637 | 0.8849 | 0.675 | 0.4243 | 2.4261 | 0.675 | 0.6207 | 0.2192 | 0.1242 |
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+ | 1.0876 | 50.0 | 650 | 0.8862 | 0.675 | 0.4233 | 2.4267 | 0.675 | 0.6266 | 0.2528 | 0.1205 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.33.3
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+ - Pytorch 2.2.0.dev20231002
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+ - Datasets 2.7.1
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+ - Tokenizers 0.13.3
config.json ADDED
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+ {
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+ "_name_or_path": "microsoft/resnet-50",
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+ "architectures": [
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+ "ResNetForImageClassification"
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+ ],
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+ 4,
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+ "downsample_in_first_stage": false,
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+ "embedding_size": 64,
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+ "hidden_act": "relu",
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+ "hidden_sizes": [
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+ 256,
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+ 512,
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+ 1024,
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+ 2048
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+ "id2label": {
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+ "0": "ADVE",
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+ "1": "Email",
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+ "2": "Form",
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+ "3": "Letter",
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+ "4": "Memo",
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+ "5": "News",
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+ "layer_type": "bottleneck",
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+ "model_type": "resnet",
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+ "num_channels": 3,
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+ "out_features": [
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+ "stage4"
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+ ],
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+ "out_indices": [
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+ 4
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+ "problem_type": "single_label_classification",
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+ "stage_names": [
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+ "stem",
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+ "stage1",
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+ "stage2",
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+ "stage3",
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+ "stage4"
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+ ],
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.33.3"
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+ }
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