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resnet-50

Image Classification
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resnet
Generated from Trainer
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End of training

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Files changed (13) hide show
  1. README.md +100 -0
  2. all_results.json +22 -0
  3. config.json +59 -0
  4. eval_results.json +17 -0
  5. model.safetensors +3 -0
  6. preprocessor_config.json +22 -0
  7. runs/Feb02_22-03-34_modal/events.out.tfevents.1738533815.modal.2.0 +3 -0
  8. runs/Feb02_22-03-34_modal/events.out.tfevents.1738533815.modal.2.1 +3 -0
  9. runs/Feb02_22-03-34_modal/events.out.tfevents.1738534603.modal.2.2 +3 -0
  10. runs/Feb02_22-03-34_modal/events.out.tfevents.1738534603.modal.2.3 +3 -0
  11. train_results.json +8 -0
  12. trainer_state.json +3627 -0
  13. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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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: resnet-50
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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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+ # resnet-50
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+
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+ This model was trained from scratch on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.6576
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+ - F1 Macro: 0.2323
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+ - F1 Micro: 0.3485
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+ - F1 Weighted: 0.2841
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+ - Precision Macro: 0.2908
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+ - Precision Micro: 0.3485
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+ - Precision Weighted: 0.3373
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+ - Recall Macro: 0.2776
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+ - Recall Micro: 0.3485
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+ - Recall Weighted: 0.3485
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+ - Accuracy: 0.3485
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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: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 16
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 30
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | F1 Weighted | Precision Macro | Precision Micro | Precision Weighted | Recall Macro | Recall Micro | Recall Weighted | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
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+ | 1.9401 | 1.0 | 29 | 1.9376 | 0.0673 | 0.1364 | 0.0898 | 0.0524 | 0.1364 | 0.0693 | 0.1020 | 0.1364 | 0.1364 | 0.1364 |
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+ | 1.9122 | 2.0 | 58 | 1.9165 | 0.0601 | 0.2045 | 0.0852 | 0.0463 | 0.2045 | 0.0648 | 0.1433 | 0.2045 | 0.2045 | 0.2045 |
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+ | 1.9226 | 3.0 | 87 | 1.8974 | 0.0729 | 0.2197 | 0.1023 | 0.0542 | 0.2197 | 0.0754 | 0.1547 | 0.2197 | 0.2197 | 0.2197 |
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+ | 1.8609 | 4.0 | 116 | 1.8879 | 0.0479 | 0.1894 | 0.0686 | 0.0293 | 0.1894 | 0.0419 | 0.1323 | 0.1894 | 0.1894 | 0.1894 |
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+ | 1.8345 | 5.0 | 145 | 1.8808 | 0.0498 | 0.2045 | 0.0713 | 0.0301 | 0.2045 | 0.0431 | 0.1429 | 0.2045 | 0.2045 | 0.2045 |
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+ | 1.8965 | 6.0 | 174 | 1.8803 | 0.0555 | 0.1894 | 0.0787 | 0.0379 | 0.1894 | 0.0534 | 0.1327 | 0.1894 | 0.1894 | 0.1894 |
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+ | 1.8651 | 7.0 | 203 | 1.8732 | 0.0787 | 0.2273 | 0.1100 | 0.0607 | 0.2273 | 0.0840 | 0.1604 | 0.2273 | 0.2273 | 0.2273 |
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+ | 1.8235 | 8.0 | 232 | 1.8693 | 0.0573 | 0.1970 | 0.0813 | 0.0393 | 0.1970 | 0.0554 | 0.1380 | 0.1970 | 0.1970 | 0.1970 |
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+ | 1.7786 | 9.0 | 261 | 1.8613 | 0.1112 | 0.25 | 0.1502 | 0.2131 | 0.25 | 0.2558 | 0.1808 | 0.25 | 0.25 | 0.25 |
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+ | 1.9601 | 10.0 | 290 | 1.8535 | 0.1144 | 0.2576 | 0.1549 | 0.1437 | 0.2576 | 0.1793 | 0.1865 | 0.2576 | 0.2576 | 0.2576 |
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+ | 1.7922 | 11.0 | 319 | 1.8492 | 0.1222 | 0.2727 | 0.1652 | 0.1487 | 0.2727 | 0.1860 | 0.1983 | 0.2727 | 0.2727 | 0.2727 |
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+ | 1.8398 | 12.0 | 348 | 1.8497 | 0.1371 | 0.2727 | 0.1810 | 0.1368 | 0.2727 | 0.1724 | 0.2022 | 0.2727 | 0.2727 | 0.2727 |
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+ | 1.8811 | 13.0 | 377 | 1.8354 | 0.1099 | 0.2424 | 0.1484 | 0.1170 | 0.2424 | 0.1490 | 0.1780 | 0.2424 | 0.2424 | 0.2424 |
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+ | 1.7813 | 14.0 | 406 | 1.8299 | 0.1445 | 0.2955 | 0.1925 | 0.1274 | 0.2955 | 0.1643 | 0.2164 | 0.2955 | 0.2955 | 0.2955 |
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+ | 1.8719 | 15.0 | 435 | 1.8213 | 0.1608 | 0.2955 | 0.2083 | 0.1462 | 0.2955 | 0.1838 | 0.2213 | 0.2955 | 0.2955 | 0.2955 |
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+ | 1.7755 | 16.0 | 464 | 1.8057 | 0.1735 | 0.3182 | 0.2247 | 0.1522 | 0.3182 | 0.1921 | 0.2392 | 0.3182 | 0.3182 | 0.3182 |
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+ | 1.7729 | 17.0 | 493 | 1.7964 | 0.1625 | 0.3106 | 0.2129 | 0.1450 | 0.3106 | 0.1843 | 0.2313 | 0.3106 | 0.3106 | 0.3106 |
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+ | 1.687 | 18.0 | 522 | 1.7865 | 0.1719 | 0.3182 | 0.2237 | 0.1576 | 0.3182 | 0.1987 | 0.2381 | 0.3182 | 0.3182 | 0.3182 |
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+ | 1.7207 | 19.0 | 551 | 1.7771 | 0.1823 | 0.3485 | 0.2394 | 0.1572 | 0.3485 | 0.2012 | 0.2592 | 0.3485 | 0.3485 | 0.3485 |
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+ | 1.7066 | 20.0 | 580 | 1.7672 | 0.1857 | 0.3485 | 0.2424 | 0.1578 | 0.3485 | 0.2015 | 0.2607 | 0.3485 | 0.3485 | 0.3485 |
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+ | 1.7726 | 21.0 | 609 | 1.7596 | 0.2147 | 0.3636 | 0.2710 | 0.2530 | 0.3636 | 0.2931 | 0.2766 | 0.3636 | 0.3636 | 0.3636 |
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+ | 1.7349 | 22.0 | 638 | 1.7517 | 0.2081 | 0.3485 | 0.2627 | 0.2145 | 0.3485 | 0.2554 | 0.2660 | 0.3485 | 0.3485 | 0.3485 |
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+ | 1.7956 | 23.0 | 667 | 1.7437 | 0.2018 | 0.3561 | 0.2590 | 0.1970 | 0.3561 | 0.2402 | 0.2687 | 0.3561 | 0.3561 | 0.3561 |
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+ | 1.4672 | 24.0 | 696 | 1.7264 | 0.2033 | 0.3636 | 0.2611 | 0.2975 | 0.3636 | 0.3356 | 0.2740 | 0.3636 | 0.3636 | 0.3636 |
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+ | 1.6008 | 25.0 | 725 | 1.7233 | 0.2323 | 0.3788 | 0.2905 | 0.2533 | 0.3788 | 0.2963 | 0.2898 | 0.3788 | 0.3788 | 0.3788 |
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+ | 1.6899 | 26.0 | 754 | 1.7199 | 0.2261 | 0.3788 | 0.2852 | 0.2426 | 0.3788 | 0.2863 | 0.2887 | 0.3788 | 0.3788 | 0.3788 |
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+ | 1.7073 | 27.0 | 783 | 1.7113 | 0.2171 | 0.3712 | 0.2752 | 0.2305 | 0.3712 | 0.2729 | 0.2819 | 0.3712 | 0.3712 | 0.3712 |
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+ | 1.6558 | 28.0 | 812 | 1.6996 | 0.2311 | 0.3864 | 0.2923 | 0.2212 | 0.3864 | 0.2677 | 0.2955 | 0.3864 | 0.3864 | 0.3864 |
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+ | 1.4732 | 29.0 | 841 | 1.7078 | 0.2320 | 0.3788 | 0.2901 | 0.2301 | 0.3788 | 0.2742 | 0.2909 | 0.3788 | 0.3788 | 0.3788 |
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+ | 1.6134 | 30.0 | 870 | 1.7132 | 0.2248 | 0.3788 | 0.2845 | 0.2245 | 0.3788 | 0.2692 | 0.2887 | 0.3788 | 0.3788 | 0.3788 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.48.2
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+ - Pytorch 2.6.0+cu124
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+ - Datasets 3.2.0
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+ - Tokenizers 0.21.0
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