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--- |
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library_name: transformers |
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license: other |
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base_model: apple/mobilevit-small |
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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: my_awesome_food_model |
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results: [] |
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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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# my_awesome_food_model |
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This model is a fine-tuned version of [apple/mobilevit-small](https://huggingface.co./apple/mobilevit-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4411 |
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- Accuracy: 0.86 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:| |
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| 4.2774 | 0.992 | 31 | 4.2776 | 0.563 | |
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| 4.2309 | 1.984 | 62 | 4.2272 | 0.616 | |
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| 4.1622 | 2.976 | 93 | 4.1229 | 0.646 | |
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| 4.045 | 4.0 | 125 | 3.9633 | 0.663 | |
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| 3.87 | 4.992 | 156 | 3.7407 | 0.68 | |
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| 3.6564 | 5.984 | 187 | 3.5054 | 0.694 | |
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| 3.4188 | 6.976 | 218 | 3.1871 | 0.681 | |
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| 3.0118 | 8.0 | 250 | 2.8682 | 0.682 | |
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| 2.6634 | 8.992 | 281 | 2.5058 | 0.668 | |
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| 2.3342 | 9.984 | 312 | 2.1583 | 0.687 | |
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| 2.0751 | 10.9760 | 343 | 1.8516 | 0.708 | |
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| 1.8887 | 12.0 | 375 | 1.6293 | 0.737 | |
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| 1.7637 | 12.992 | 406 | 1.5026 | 0.733 | |
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| 1.6451 | 13.984 | 437 | 1.3837 | 0.745 | |
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| 1.4845 | 14.9760 | 468 | 1.2238 | 0.765 | |
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| 1.3586 | 16.0 | 500 | 1.1391 | 0.775 | |
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| 1.2649 | 16.992 | 531 | 0.9872 | 0.784 | |
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| 1.1469 | 17.984 | 562 | 0.9524 | 0.77 | |
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| 1.0319 | 18.976 | 593 | 0.8425 | 0.797 | |
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| 0.9926 | 20.0 | 625 | 0.8079 | 0.791 | |
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| 0.9592 | 20.992 | 656 | 0.7261 | 0.808 | |
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| 0.8481 | 21.984 | 687 | 0.7273 | 0.799 | |
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| 0.8027 | 22.976 | 718 | 0.6501 | 0.807 | |
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| 0.8258 | 24.0 | 750 | 0.6499 | 0.818 | |
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| 0.7785 | 24.992 | 781 | 0.6178 | 0.83 | |
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| 0.7881 | 25.984 | 812 | 0.6305 | 0.827 | |
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| 0.7034 | 26.976 | 843 | 0.6201 | 0.829 | |
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| 0.705 | 28.0 | 875 | 0.5611 | 0.842 | |
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| 0.6981 | 28.992 | 906 | 0.5357 | 0.846 | |
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| 0.675 | 29.984 | 937 | 0.5622 | 0.844 | |
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| 0.6339 | 30.976 | 968 | 0.5188 | 0.847 | |
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| 0.611 | 32.0 | 1000 | 0.4712 | 0.879 | |
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| 0.6036 | 32.992 | 1031 | 0.5218 | 0.847 | |
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| 0.5909 | 33.984 | 1062 | 0.5201 | 0.84 | |
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| 0.5695 | 34.976 | 1093 | 0.4935 | 0.854 | |
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| 0.5752 | 36.0 | 1125 | 0.4752 | 0.858 | |
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| 0.5487 | 36.992 | 1156 | 0.5121 | 0.837 | |
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| 0.5538 | 37.984 | 1187 | 0.5111 | 0.852 | |
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| 0.5267 | 38.976 | 1218 | 0.4884 | 0.853 | |
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| 0.4907 | 40.0 | 1250 | 0.4921 | 0.859 | |
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| 0.4901 | 40.992 | 1281 | 0.5001 | 0.849 | |
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| 0.5134 | 41.984 | 1312 | 0.4409 | 0.865 | |
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| 0.5019 | 42.976 | 1343 | 0.4730 | 0.863 | |
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| 0.4642 | 44.0 | 1375 | 0.4619 | 0.865 | |
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| 0.4778 | 44.992 | 1406 | 0.4911 | 0.861 | |
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| 0.4521 | 45.984 | 1437 | 0.4471 | 0.855 | |
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| 0.473 | 46.976 | 1468 | 0.4661 | 0.864 | |
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| 0.4752 | 48.0 | 1500 | 0.4917 | 0.848 | |
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| 0.4582 | 48.992 | 1531 | 0.4685 | 0.862 | |
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| 0.4282 | 49.984 | 1562 | 0.4314 | 0.875 | |
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| 0.4344 | 50.976 | 1593 | 0.4270 | 0.878 | |
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| 0.4212 | 52.0 | 1625 | 0.4657 | 0.861 | |
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| 0.4245 | 52.992 | 1656 | 0.4608 | 0.857 | |
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| 0.4055 | 53.984 | 1687 | 0.4717 | 0.856 | |
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| 0.3802 | 54.976 | 1718 | 0.4428 | 0.871 | |
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| 0.4221 | 56.0 | 1750 | 0.4088 | 0.88 | |
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| 0.4254 | 56.992 | 1781 | 0.4310 | 0.869 | |
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| 0.3963 | 57.984 | 1812 | 0.4320 | 0.864 | |
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| 0.4375 | 58.976 | 1843 | 0.4404 | 0.876 | |
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| 0.3685 | 60.0 | 1875 | 0.4369 | 0.866 | |
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| 0.3911 | 60.992 | 1906 | 0.4491 | 0.861 | |
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| 0.3761 | 61.984 | 1937 | 0.4509 | 0.86 | |
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| 0.3703 | 62.976 | 1968 | 0.4468 | 0.861 | |
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| 0.3602 | 64.0 | 2000 | 0.4596 | 0.87 | |
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| 0.4034 | 64.992 | 2031 | 0.4232 | 0.86 | |
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| 0.3726 | 65.984 | 2062 | 0.4214 | 0.877 | |
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| 0.4187 | 66.976 | 2093 | 0.4509 | 0.868 | |
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| 0.3858 | 68.0 | 2125 | 0.4067 | 0.878 | |
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| 0.3933 | 68.992 | 2156 | 0.4295 | 0.879 | |
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| 0.3461 | 69.984 | 2187 | 0.4092 | 0.88 | |
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| 0.3909 | 70.976 | 2218 | 0.4518 | 0.862 | |
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| 0.3737 | 72.0 | 2250 | 0.4414 | 0.867 | |
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| 0.344 | 72.992 | 2281 | 0.4335 | 0.869 | |
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| 0.3403 | 73.984 | 2312 | 0.4470 | 0.863 | |
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| 0.3433 | 74.976 | 2343 | 0.4016 | 0.881 | |
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| 0.3292 | 76.0 | 2375 | 0.4565 | 0.861 | |
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| 0.3115 | 76.992 | 2406 | 0.4368 | 0.87 | |
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| 0.3498 | 77.984 | 2437 | 0.4516 | 0.862 | |
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| 0.3456 | 78.976 | 2468 | 0.3779 | 0.889 | |
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| 0.3284 | 80.0 | 2500 | 0.4441 | 0.865 | |
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| 0.3723 | 80.992 | 2531 | 0.4150 | 0.874 | |
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| 0.3269 | 81.984 | 2562 | 0.4491 | 0.864 | |
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| 0.3863 | 82.976 | 2593 | 0.4106 | 0.884 | |
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| 0.3376 | 84.0 | 2625 | 0.4367 | 0.87 | |
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| 0.3794 | 84.992 | 2656 | 0.4282 | 0.863 | |
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| 0.3498 | 85.984 | 2687 | 0.4185 | 0.877 | |
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| 0.293 | 86.976 | 2718 | 0.4207 | 0.87 | |
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| 0.3106 | 88.0 | 2750 | 0.4316 | 0.873 | |
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| 0.3061 | 88.992 | 2781 | 0.4254 | 0.874 | |
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| 0.3235 | 89.984 | 2812 | 0.4251 | 0.878 | |
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| 0.3182 | 90.976 | 2843 | 0.4247 | 0.873 | |
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| 0.3666 | 92.0 | 2875 | 0.4079 | 0.879 | |
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| 0.336 | 92.992 | 2906 | 0.4187 | 0.871 | |
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| 0.3262 | 93.984 | 2937 | 0.4461 | 0.868 | |
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| 0.3504 | 94.976 | 2968 | 0.4713 | 0.852 | |
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| 0.3106 | 96.0 | 3000 | 0.4610 | 0.863 | |
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| 0.2671 | 96.992 | 3031 | 0.4614 | 0.866 | |
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| 0.2929 | 97.984 | 3062 | 0.4278 | 0.872 | |
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| 0.353 | 98.976 | 3093 | 0.4134 | 0.872 | |
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| 0.3331 | 99.2 | 3100 | 0.4411 | 0.86 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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