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# vit-base_rvl-cdip-tiny_rvl_cdip-NK1000_hint_rand

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: 75.5808
- Accuracy: 0.583
- Brier Loss: 0.7311
- Nll: 3.9633
- F1 Micro: 0.583
- F1 Macro: 0.5838
- Ece: 0.3399
- Aurc: 0.2128

## 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   | 78.0119         | 0.1285   | 0.9098     | 6.7342 | 0.1285   | 0.0748   | 0.0496 | 0.7634 |
| 77.7969       | 2.0   | 500   | 77.3633         | 0.1595   | 0.8985     | 5.2942 | 0.1595   | 0.1038   | 0.0509 | 0.7216 |
| 77.7969       | 3.0   | 750   | 76.6773         | 0.2545   | 0.8551     | 3.9015 | 0.2545   | 0.2006   | 0.0741 | 0.5967 |
| 76.735        | 4.0   | 1000  | 76.1721         | 0.312    | 0.8123     | 3.4141 | 0.312    | 0.2785   | 0.0855 | 0.5018 |
| 76.735        | 5.0   | 1250  | 76.0027         | 0.3703   | 0.7573     | 3.2539 | 0.3703   | 0.3299   | 0.0764 | 0.4161 |
| 75.8262       | 6.0   | 1500  | 76.3256         | 0.4143   | 0.7290     | 3.1129 | 0.4143   | 0.3995   | 0.0835 | 0.3792 |
| 75.8262       | 7.0   | 1750  | 75.5753         | 0.4575   | 0.6838     | 2.8940 | 0.4575   | 0.4421   | 0.0595 | 0.3262 |
| 75.3656       | 8.0   | 2000  | 75.2875         | 0.475    | 0.6554     | 2.7996 | 0.4750   | 0.4596   | 0.0715 | 0.2976 |
| 75.3656       | 9.0   | 2250  | 75.3849         | 0.4833   | 0.6446     | 2.7232 | 0.4833   | 0.4523   | 0.0651 | 0.2885 |
| 75.0748       | 10.0  | 2500  | 75.3431         | 0.5172   | 0.6173     | 2.6664 | 0.5172   | 0.4905   | 0.0563 | 0.2606 |
| 75.0748       | 11.0  | 2750  | 75.0478         | 0.5357   | 0.5982     | 2.7014 | 0.5357   | 0.5207   | 0.0550 | 0.2384 |
| 74.821        | 12.0  | 3000  | 75.1324         | 0.5325   | 0.5973     | 2.6161 | 0.5325   | 0.5202   | 0.0569 | 0.2402 |
| 74.821        | 13.0  | 3250  | 75.0049         | 0.528    | 0.5996     | 2.6859 | 0.528    | 0.5157   | 0.0657 | 0.2408 |
| 74.613        | 14.0  | 3500  | 74.8702         | 0.5453   | 0.5881     | 2.7150 | 0.5453   | 0.5455   | 0.0661 | 0.2302 |
| 74.613        | 15.0  | 3750  | 74.8427         | 0.5595   | 0.5697     | 2.5605 | 0.5595   | 0.5479   | 0.0765 | 0.2117 |
| 74.421        | 16.0  | 4000  | 74.9157         | 0.5503   | 0.5829     | 2.7215 | 0.5503   | 0.5524   | 0.0765 | 0.2219 |
| 74.421        | 17.0  | 4250  | 74.9051         | 0.5633   | 0.5816     | 2.6715 | 0.5633   | 0.5577   | 0.0924 | 0.2186 |
| 74.2453       | 18.0  | 4500  | 74.9910         | 0.5733   | 0.5722     | 2.6963 | 0.5733   | 0.5717   | 0.0930 | 0.2107 |
| 74.2453       | 19.0  | 4750  | 74.8632         | 0.5575   | 0.5892     | 2.6981 | 0.5575   | 0.5549   | 0.1073 | 0.2198 |
| 74.0712       | 20.0  | 5000  | 74.8128         | 0.5757   | 0.5794     | 2.7227 | 0.5757   | 0.5697   | 0.1235 | 0.2083 |
| 74.0712       | 21.0  | 5250  | 74.7545         | 0.575    | 0.5794     | 2.7000 | 0.575    | 0.5700   | 0.1372 | 0.2015 |
| 73.9033       | 22.0  | 5500  | 74.7493         | 0.5737   | 0.5841     | 2.7996 | 0.5737   | 0.5806   | 0.1341 | 0.2073 |
| 73.9033       | 23.0  | 5750  | 74.7641         | 0.582    | 0.5831     | 2.7846 | 0.582    | 0.5780   | 0.1576 | 0.1985 |
| 73.7364       | 24.0  | 6000  | 74.8125         | 0.5807   | 0.5944     | 2.8725 | 0.5807   | 0.5767   | 0.1719 | 0.2015 |
| 73.7364       | 25.0  | 6250  | 74.9721         | 0.573    | 0.6132     | 2.9232 | 0.573    | 0.5734   | 0.1920 | 0.2086 |
| 73.5899       | 26.0  | 6500  | 74.8675         | 0.5823   | 0.6127     | 2.9200 | 0.5823   | 0.5788   | 0.1969 | 0.2059 |
| 73.5899       | 27.0  | 6750  | 74.9213         | 0.5723   | 0.6234     | 3.0482 | 0.5723   | 0.5717   | 0.2138 | 0.2085 |
| 73.4419       | 28.0  | 7000  | 74.9436         | 0.5815   | 0.6324     | 3.0789 | 0.5815   | 0.5803   | 0.2223 | 0.2058 |
| 73.4419       | 29.0  | 7250  | 74.8826         | 0.5747   | 0.6408     | 3.1380 | 0.5747   | 0.5711   | 0.2428 | 0.2044 |
| 73.3198       | 30.0  | 7500  | 75.0310         | 0.5633   | 0.6722     | 3.2517 | 0.5633   | 0.5639   | 0.2571 | 0.2226 |
| 73.3198       | 31.0  | 7750  | 75.0300         | 0.5577   | 0.6795     | 3.3520 | 0.5577   | 0.5627   | 0.2611 | 0.2255 |
| 73.2086       | 32.0  | 8000  | 74.9569         | 0.5793   | 0.6614     | 3.3345 | 0.5793   | 0.5829   | 0.2623 | 0.2070 |
| 73.2086       | 33.0  | 8250  | 75.1474         | 0.5655   | 0.6902     | 3.5319 | 0.5655   | 0.5656   | 0.2780 | 0.2260 |
| 73.1102       | 34.0  | 8500  | 75.1176         | 0.5697   | 0.6926     | 3.5011 | 0.5697   | 0.5685   | 0.2891 | 0.2127 |
| 73.1102       | 35.0  | 8750  | 75.2834         | 0.5673   | 0.7085     | 3.7150 | 0.5673   | 0.5688   | 0.2945 | 0.2210 |
| 73.0239       | 36.0  | 9000  | 75.2426         | 0.566    | 0.7101     | 3.6822 | 0.566    | 0.5679   | 0.3029 | 0.2200 |
| 73.0239       | 37.0  | 9250  | 75.3049         | 0.5743   | 0.7082     | 3.6300 | 0.5743   | 0.5758   | 0.3044 | 0.2185 |
| 72.9631       | 38.0  | 9500  | 75.3404         | 0.5695   | 0.7220     | 3.7386 | 0.5695   | 0.5741   | 0.3177 | 0.2210 |
| 72.9631       | 39.0  | 9750  | 75.4376         | 0.5775   | 0.7181     | 3.8412 | 0.5775   | 0.5784   | 0.3148 | 0.2191 |
| 72.9028       | 40.0  | 10000 | 75.4664         | 0.5777   | 0.7178     | 3.9272 | 0.5777   | 0.5775   | 0.3178 | 0.2233 |
| 72.9028       | 41.0  | 10250 | 75.5305         | 0.5737   | 0.7279     | 3.8240 | 0.5737   | 0.5761   | 0.3271 | 0.2215 |
| 72.8505       | 42.0  | 10500 | 75.4606         | 0.5783   | 0.7225     | 3.8401 | 0.5783   | 0.5805   | 0.3261 | 0.2156 |
| 72.8505       | 43.0  | 10750 | 75.5084         | 0.5793   | 0.7242     | 3.8552 | 0.5793   | 0.5791   | 0.3308 | 0.2115 |
| 72.8091       | 44.0  | 11000 | 75.4797         | 0.5817   | 0.7256     | 3.8946 | 0.5817   | 0.5825   | 0.3340 | 0.2112 |
| 72.8091       | 45.0  | 11250 | 75.5695         | 0.5793   | 0.7297     | 3.9742 | 0.5793   | 0.5809   | 0.3379 | 0.2150 |
| 72.7801       | 46.0  | 11500 | 75.5592         | 0.5807   | 0.7331     | 3.9445 | 0.5807   | 0.5830   | 0.3378 | 0.2151 |
| 72.7801       | 47.0  | 11750 | 75.5976         | 0.5833   | 0.7303     | 3.9669 | 0.5833   | 0.5840   | 0.3380 | 0.2145 |
| 72.7606       | 48.0  | 12000 | 75.5952         | 0.5833   | 0.7320     | 3.9813 | 0.5833   | 0.5847   | 0.3380 | 0.2148 |
| 72.7606       | 49.0  | 12250 | 75.5621         | 0.5843   | 0.7309     | 3.9491 | 0.5843   | 0.5851   | 0.3385 | 0.2127 |
| 72.7486       | 50.0  | 12500 | 75.5808         | 0.583    | 0.7311     | 3.9633 | 0.583    | 0.5838   | 0.3399 | 0.2128 |


### Framework versions

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

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