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vit-base_rvl-cdip-tiny_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5

Image Classification
Transformers
PyTorch
vit
generated_from_trainer
Inference Endpoints
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+ ---
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+ license: apache-2.0
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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: vit-base_rvl-cdip-tiny_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5
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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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+ # vit-base_rvl-cdip-tiny_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5
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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 None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.2802
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+ - Accuracy: 0.5747
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+ - Brier Loss: 0.6822
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+ - Nll: 3.2886
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+ - F1 Micro: 0.5747
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+ - F1 Macro: 0.5757
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+ - Ece: 0.2786
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+ - Aurc: 0.2132
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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 | 250 | 4.1512 | 0.1727 | 0.9045 | 5.5051 | 0.1727 | 0.0947 | 0.0704 | 0.7164 |
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+ | 4.2402 | 2.0 | 500 | 3.8933 | 0.216 | 0.8775 | 4.1816 | 0.216 | 0.1697 | 0.0699 | 0.6624 |
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+ | 4.2402 | 3.0 | 750 | 3.4256 | 0.3207 | 0.8113 | 3.6783 | 0.3207 | 0.2567 | 0.0645 | 0.5125 |
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+ | 3.5189 | 4.0 | 1000 | 3.1611 | 0.3673 | 0.7763 | 3.6447 | 0.3673 | 0.3039 | 0.0797 | 0.4450 |
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+ | 3.5189 | 5.0 | 1250 | 2.7791 | 0.4253 | 0.7216 | 3.1536 | 0.4253 | 0.3860 | 0.0982 | 0.3729 |
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+ | 2.7963 | 6.0 | 1500 | 2.6525 | 0.4323 | 0.7004 | 3.0187 | 0.4323 | 0.4117 | 0.0992 | 0.3440 |
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+ | 2.7963 | 7.0 | 1750 | 2.3623 | 0.5005 | 0.6489 | 2.8371 | 0.5005 | 0.4747 | 0.1076 | 0.2843 |
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+ | 2.3741 | 8.0 | 2000 | 2.4259 | 0.4798 | 0.6704 | 2.9344 | 0.4798 | 0.4680 | 0.1164 | 0.3045 |
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+ | 2.3741 | 9.0 | 2250 | 2.3034 | 0.5005 | 0.6431 | 2.8598 | 0.5005 | 0.4892 | 0.1306 | 0.2683 |
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+ | 2.0855 | 10.0 | 2500 | 2.1550 | 0.5298 | 0.6264 | 2.6847 | 0.5298 | 0.5164 | 0.1413 | 0.2480 |
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+ | 2.0855 | 11.0 | 2750 | 2.0891 | 0.5455 | 0.6162 | 2.6978 | 0.5455 | 0.5330 | 0.1428 | 0.2343 |
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+ | 1.8265 | 12.0 | 3000 | 2.2045 | 0.5252 | 0.6627 | 2.7900 | 0.5252 | 0.5045 | 0.1997 | 0.2507 |
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+ | 1.8265 | 13.0 | 3250 | 2.0080 | 0.5597 | 0.5948 | 2.7128 | 0.5597 | 0.5564 | 0.1389 | 0.2145 |
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+ | 1.6099 | 14.0 | 3500 | 2.1966 | 0.5353 | 0.6594 | 2.8505 | 0.5353 | 0.5198 | 0.1984 | 0.2581 |
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+ | 1.6099 | 15.0 | 3750 | 2.0788 | 0.547 | 0.6191 | 2.7214 | 0.547 | 0.5419 | 0.1729 | 0.2294 |
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+ | 1.4149 | 16.0 | 4000 | 2.0634 | 0.5485 | 0.6235 | 2.7486 | 0.5485 | 0.5491 | 0.1872 | 0.2225 |
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+ | 1.4149 | 17.0 | 4250 | 2.0722 | 0.5597 | 0.6241 | 2.7989 | 0.5597 | 0.5574 | 0.1912 | 0.2189 |
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+ | 1.2282 | 18.0 | 4500 | 2.1226 | 0.557 | 0.6327 | 2.9138 | 0.557 | 0.5584 | 0.2016 | 0.2205 |
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+ | 1.2282 | 19.0 | 4750 | 2.1013 | 0.5577 | 0.6326 | 2.8846 | 0.5577 | 0.5574 | 0.2051 | 0.2200 |
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+ | 1.0543 | 20.0 | 5000 | 2.1902 | 0.5637 | 0.6519 | 2.9362 | 0.5637 | 0.5556 | 0.2261 | 0.2273 |
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+ | 1.0543 | 21.0 | 5250 | 2.2291 | 0.5603 | 0.6620 | 2.9256 | 0.5603 | 0.5532 | 0.2469 | 0.2350 |
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+ | 0.8882 | 22.0 | 5500 | 2.2152 | 0.5605 | 0.6613 | 3.0823 | 0.5605 | 0.5563 | 0.2397 | 0.2234 |
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+ | 0.8882 | 23.0 | 5750 | 2.2309 | 0.5617 | 0.6600 | 3.1164 | 0.5617 | 0.5571 | 0.2520 | 0.2252 |
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+ | 0.7308 | 24.0 | 6000 | 2.2332 | 0.5655 | 0.6631 | 3.1202 | 0.5655 | 0.5661 | 0.2502 | 0.2241 |
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+ | 0.7308 | 25.0 | 6250 | 2.3018 | 0.5663 | 0.6762 | 3.2623 | 0.5663 | 0.5652 | 0.2640 | 0.2265 |
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+ | 0.6001 | 26.0 | 6500 | 2.3505 | 0.5547 | 0.6923 | 3.3289 | 0.5547 | 0.5592 | 0.2790 | 0.2279 |
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+ | 0.6001 | 27.0 | 6750 | 2.3821 | 0.5555 | 0.6932 | 3.4374 | 0.5555 | 0.5538 | 0.2827 | 0.2275 |
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+ | 0.4912 | 28.0 | 7000 | 2.3788 | 0.5675 | 0.6915 | 3.3014 | 0.5675 | 0.5637 | 0.2865 | 0.2324 |
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+ | 0.4912 | 29.0 | 7250 | 2.4068 | 0.556 | 0.7028 | 3.4904 | 0.556 | 0.5559 | 0.2906 | 0.2365 |
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+ | 0.4068 | 30.0 | 7500 | 2.4476 | 0.5557 | 0.7044 | 3.4350 | 0.5557 | 0.5572 | 0.2846 | 0.2387 |
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+ | 0.4068 | 31.0 | 7750 | 2.4179 | 0.562 | 0.7021 | 3.4782 | 0.562 | 0.5619 | 0.2911 | 0.2305 |
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+ | 0.3364 | 32.0 | 8000 | 2.3915 | 0.5615 | 0.6961 | 3.4704 | 0.5615 | 0.5623 | 0.2889 | 0.2294 |
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+ | 0.3364 | 33.0 | 8250 | 2.3860 | 0.568 | 0.6957 | 3.4578 | 0.568 | 0.5703 | 0.2869 | 0.2263 |
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+ | 0.2862 | 34.0 | 8500 | 2.4250 | 0.5647 | 0.7022 | 3.4923 | 0.5647 | 0.5638 | 0.2928 | 0.2282 |
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+ | 0.2862 | 35.0 | 8750 | 2.4453 | 0.5587 | 0.7106 | 3.6175 | 0.5587 | 0.5594 | 0.2970 | 0.2306 |
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+ | 0.2397 | 36.0 | 9000 | 2.3919 | 0.5653 | 0.6964 | 3.4399 | 0.5653 | 0.5675 | 0.2881 | 0.2197 |
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+ | 0.2397 | 37.0 | 9250 | 2.3870 | 0.5647 | 0.6995 | 3.4910 | 0.5647 | 0.5657 | 0.2941 | 0.2237 |
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+ | 0.2058 | 38.0 | 9500 | 2.4080 | 0.5663 | 0.7033 | 3.5314 | 0.5663 | 0.5673 | 0.2979 | 0.2271 |
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+ | 0.2058 | 39.0 | 9750 | 2.3727 | 0.5675 | 0.6975 | 3.3806 | 0.5675 | 0.5708 | 0.2930 | 0.2240 |
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+ | 0.1819 | 40.0 | 10000 | 2.3627 | 0.5745 | 0.6913 | 3.4237 | 0.5745 | 0.5751 | 0.2847 | 0.2217 |
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+ | 0.1819 | 41.0 | 10250 | 2.3497 | 0.564 | 0.6952 | 3.3908 | 0.564 | 0.5626 | 0.2931 | 0.2208 |
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+ | 0.1587 | 42.0 | 10500 | 2.3168 | 0.5705 | 0.6842 | 3.3858 | 0.5705 | 0.5725 | 0.2808 | 0.2181 |
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+ | 0.1587 | 43.0 | 10750 | 2.2910 | 0.5715 | 0.6768 | 3.3739 | 0.5715 | 0.5727 | 0.2777 | 0.2127 |
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+ | 0.1402 | 44.0 | 11000 | 2.3053 | 0.5713 | 0.6808 | 3.4128 | 0.5713 | 0.5724 | 0.2793 | 0.2133 |
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+ | 0.1402 | 45.0 | 11250 | 2.3029 | 0.5743 | 0.6848 | 3.3133 | 0.5743 | 0.5750 | 0.2771 | 0.2192 |
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+ | 0.1257 | 46.0 | 11500 | 2.2965 | 0.5695 | 0.6856 | 3.2338 | 0.5695 | 0.5697 | 0.2858 | 0.2158 |
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+ | 0.1257 | 47.0 | 11750 | 2.2823 | 0.5685 | 0.6847 | 3.2705 | 0.5685 | 0.5693 | 0.2828 | 0.2153 |
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+ | 0.1134 | 48.0 | 12000 | 2.2800 | 0.5753 | 0.6803 | 3.2797 | 0.5753 | 0.5759 | 0.2795 | 0.2139 |
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+ | 0.1134 | 49.0 | 12250 | 2.2766 | 0.5733 | 0.6823 | 3.2828 | 0.5733 | 0.5751 | 0.2777 | 0.2135 |
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+ | 0.1039 | 50.0 | 12500 | 2.2802 | 0.5747 | 0.6822 | 3.2886 | 0.5747 | 0.5757 | 0.2786 | 0.2132 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.26.1
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+ - Pytorch 1.13.1.post200
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+ - Datasets 2.9.0
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+ - Tokenizers 0.13.2