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

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.
It achieves the following results on the evaluation set:
- Loss: 2.2802
- Accuracy: 0.5747
- Brier Loss: 0.6822
- Nll: 3.2886
- F1 Micro: 0.5747
- F1 Macro: 0.5757
- Ece: 0.2786
- Aurc: 0.2132

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Brier Loss | Nll    | F1 Micro | F1 Macro | Ece    | Aurc   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log        | 1.0   | 250   | 4.1512          | 0.1727   | 0.9045     | 5.5051 | 0.1727   | 0.0947   | 0.0704 | 0.7164 |
| 4.2402        | 2.0   | 500   | 3.8933          | 0.216    | 0.8775     | 4.1816 | 0.216    | 0.1697   | 0.0699 | 0.6624 |
| 4.2402        | 3.0   | 750   | 3.4256          | 0.3207   | 0.8113     | 3.6783 | 0.3207   | 0.2567   | 0.0645 | 0.5125 |
| 3.5189        | 4.0   | 1000  | 3.1611          | 0.3673   | 0.7763     | 3.6447 | 0.3673   | 0.3039   | 0.0797 | 0.4450 |
| 3.5189        | 5.0   | 1250  | 2.7791          | 0.4253   | 0.7216     | 3.1536 | 0.4253   | 0.3860   | 0.0982 | 0.3729 |
| 2.7963        | 6.0   | 1500  | 2.6525          | 0.4323   | 0.7004     | 3.0187 | 0.4323   | 0.4117   | 0.0992 | 0.3440 |
| 2.7963        | 7.0   | 1750  | 2.3623          | 0.5005   | 0.6489     | 2.8371 | 0.5005   | 0.4747   | 0.1076 | 0.2843 |
| 2.3741        | 8.0   | 2000  | 2.4259          | 0.4798   | 0.6704     | 2.9344 | 0.4798   | 0.4680   | 0.1164 | 0.3045 |
| 2.3741        | 9.0   | 2250  | 2.3034          | 0.5005   | 0.6431     | 2.8598 | 0.5005   | 0.4892   | 0.1306 | 0.2683 |
| 2.0855        | 10.0  | 2500  | 2.1550          | 0.5298   | 0.6264     | 2.6847 | 0.5298   | 0.5164   | 0.1413 | 0.2480 |
| 2.0855        | 11.0  | 2750  | 2.0891          | 0.5455   | 0.6162     | 2.6978 | 0.5455   | 0.5330   | 0.1428 | 0.2343 |
| 1.8265        | 12.0  | 3000  | 2.2045          | 0.5252   | 0.6627     | 2.7900 | 0.5252   | 0.5045   | 0.1997 | 0.2507 |
| 1.8265        | 13.0  | 3250  | 2.0080          | 0.5597   | 0.5948     | 2.7128 | 0.5597   | 0.5564   | 0.1389 | 0.2145 |
| 1.6099        | 14.0  | 3500  | 2.1966          | 0.5353   | 0.6594     | 2.8505 | 0.5353   | 0.5198   | 0.1984 | 0.2581 |
| 1.6099        | 15.0  | 3750  | 2.0788          | 0.547    | 0.6191     | 2.7214 | 0.547    | 0.5419   | 0.1729 | 0.2294 |
| 1.4149        | 16.0  | 4000  | 2.0634          | 0.5485   | 0.6235     | 2.7486 | 0.5485   | 0.5491   | 0.1872 | 0.2225 |
| 1.4149        | 17.0  | 4250  | 2.0722          | 0.5597   | 0.6241     | 2.7989 | 0.5597   | 0.5574   | 0.1912 | 0.2189 |
| 1.2282        | 18.0  | 4500  | 2.1226          | 0.557    | 0.6327     | 2.9138 | 0.557    | 0.5584   | 0.2016 | 0.2205 |
| 1.2282        | 19.0  | 4750  | 2.1013          | 0.5577   | 0.6326     | 2.8846 | 0.5577   | 0.5574   | 0.2051 | 0.2200 |
| 1.0543        | 20.0  | 5000  | 2.1902          | 0.5637   | 0.6519     | 2.9362 | 0.5637   | 0.5556   | 0.2261 | 0.2273 |
| 1.0543        | 21.0  | 5250  | 2.2291          | 0.5603   | 0.6620     | 2.9256 | 0.5603   | 0.5532   | 0.2469 | 0.2350 |
| 0.8882        | 22.0  | 5500  | 2.2152          | 0.5605   | 0.6613     | 3.0823 | 0.5605   | 0.5563   | 0.2397 | 0.2234 |
| 0.8882        | 23.0  | 5750  | 2.2309          | 0.5617   | 0.6600     | 3.1164 | 0.5617   | 0.5571   | 0.2520 | 0.2252 |
| 0.7308        | 24.0  | 6000  | 2.2332          | 0.5655   | 0.6631     | 3.1202 | 0.5655   | 0.5661   | 0.2502 | 0.2241 |
| 0.7308        | 25.0  | 6250  | 2.3018          | 0.5663   | 0.6762     | 3.2623 | 0.5663   | 0.5652   | 0.2640 | 0.2265 |
| 0.6001        | 26.0  | 6500  | 2.3505          | 0.5547   | 0.6923     | 3.3289 | 0.5547   | 0.5592   | 0.2790 | 0.2279 |
| 0.6001        | 27.0  | 6750  | 2.3821          | 0.5555   | 0.6932     | 3.4374 | 0.5555   | 0.5538   | 0.2827 | 0.2275 |
| 0.4912        | 28.0  | 7000  | 2.3788          | 0.5675   | 0.6915     | 3.3014 | 0.5675   | 0.5637   | 0.2865 | 0.2324 |
| 0.4912        | 29.0  | 7250  | 2.4068          | 0.556    | 0.7028     | 3.4904 | 0.556    | 0.5559   | 0.2906 | 0.2365 |
| 0.4068        | 30.0  | 7500  | 2.4476          | 0.5557   | 0.7044     | 3.4350 | 0.5557   | 0.5572   | 0.2846 | 0.2387 |
| 0.4068        | 31.0  | 7750  | 2.4179          | 0.562    | 0.7021     | 3.4782 | 0.562    | 0.5619   | 0.2911 | 0.2305 |
| 0.3364        | 32.0  | 8000  | 2.3915          | 0.5615   | 0.6961     | 3.4704 | 0.5615   | 0.5623   | 0.2889 | 0.2294 |
| 0.3364        | 33.0  | 8250  | 2.3860          | 0.568    | 0.6957     | 3.4578 | 0.568    | 0.5703   | 0.2869 | 0.2263 |
| 0.2862        | 34.0  | 8500  | 2.4250          | 0.5647   | 0.7022     | 3.4923 | 0.5647   | 0.5638   | 0.2928 | 0.2282 |
| 0.2862        | 35.0  | 8750  | 2.4453          | 0.5587   | 0.7106     | 3.6175 | 0.5587   | 0.5594   | 0.2970 | 0.2306 |
| 0.2397        | 36.0  | 9000  | 2.3919          | 0.5653   | 0.6964     | 3.4399 | 0.5653   | 0.5675   | 0.2881 | 0.2197 |
| 0.2397        | 37.0  | 9250  | 2.3870          | 0.5647   | 0.6995     | 3.4910 | 0.5647   | 0.5657   | 0.2941 | 0.2237 |
| 0.2058        | 38.0  | 9500  | 2.4080          | 0.5663   | 0.7033     | 3.5314 | 0.5663   | 0.5673   | 0.2979 | 0.2271 |
| 0.2058        | 39.0  | 9750  | 2.3727          | 0.5675   | 0.6975     | 3.3806 | 0.5675   | 0.5708   | 0.2930 | 0.2240 |
| 0.1819        | 40.0  | 10000 | 2.3627          | 0.5745   | 0.6913     | 3.4237 | 0.5745   | 0.5751   | 0.2847 | 0.2217 |
| 0.1819        | 41.0  | 10250 | 2.3497          | 0.564    | 0.6952     | 3.3908 | 0.564    | 0.5626   | 0.2931 | 0.2208 |
| 0.1587        | 42.0  | 10500 | 2.3168          | 0.5705   | 0.6842     | 3.3858 | 0.5705   | 0.5725   | 0.2808 | 0.2181 |
| 0.1587        | 43.0  | 10750 | 2.2910          | 0.5715   | 0.6768     | 3.3739 | 0.5715   | 0.5727   | 0.2777 | 0.2127 |
| 0.1402        | 44.0  | 11000 | 2.3053          | 0.5713   | 0.6808     | 3.4128 | 0.5713   | 0.5724   | 0.2793 | 0.2133 |
| 0.1402        | 45.0  | 11250 | 2.3029          | 0.5743   | 0.6848     | 3.3133 | 0.5743   | 0.5750   | 0.2771 | 0.2192 |
| 0.1257        | 46.0  | 11500 | 2.2965          | 0.5695   | 0.6856     | 3.2338 | 0.5695   | 0.5697   | 0.2858 | 0.2158 |
| 0.1257        | 47.0  | 11750 | 2.2823          | 0.5685   | 0.6847     | 3.2705 | 0.5685   | 0.5693   | 0.2828 | 0.2153 |
| 0.1134        | 48.0  | 12000 | 2.2800          | 0.5753   | 0.6803     | 3.2797 | 0.5753   | 0.5759   | 0.2795 | 0.2139 |
| 0.1134        | 49.0  | 12250 | 2.2766          | 0.5733   | 0.6823     | 3.2828 | 0.5733   | 0.5751   | 0.2777 | 0.2135 |
| 0.1039        | 50.0  | 12500 | 2.2802          | 0.5747   | 0.6822     | 3.2886 | 0.5747   | 0.5757   | 0.2786 | 0.2132 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2

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