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# resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6048
- Accuracy: 0.7867
- Brier Loss: 0.3046
- Nll: 2.0167
- F1 Micro: 0.7868
- F1 Macro: 0.7867
- Ece: 0.0468
- Aurc: 0.0597

## 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   | 4.1589          | 0.1305   | 0.9320     | 7.8922 | 0.1305   | 0.0928   | 0.0637 | 0.8337 |
| 4.1546        | 2.0   | 500   | 3.6898          | 0.3515   | 0.8840     | 4.7696 | 0.3515   | 0.3150   | 0.2354 | 0.5486 |
| 4.1546        | 3.0   | 750   | 2.3450          | 0.4863   | 0.6606     | 3.2068 | 0.4863   | 0.4495   | 0.0978 | 0.2927 |
| 2.419         | 4.0   | 1000  | 1.5206          | 0.6125   | 0.5126     | 2.7884 | 0.6125   | 0.5996   | 0.0512 | 0.1677 |
| 2.419         | 5.0   | 1250  | 1.2545          | 0.6593   | 0.4574     | 2.6041 | 0.6593   | 0.6524   | 0.0483 | 0.1337 |
| 1.1615        | 6.0   | 1500  | 0.9718          | 0.704    | 0.4062     | 2.4047 | 0.704    | 0.7017   | 0.0506 | 0.1043 |
| 1.1615        | 7.0   | 1750  | 0.8636          | 0.73     | 0.3760     | 2.1975 | 0.7300   | 0.7304   | 0.0522 | 0.0902 |
| 0.7217        | 8.0   | 2000  | 0.7892          | 0.737    | 0.3632     | 2.1583 | 0.737    | 0.7377   | 0.0551 | 0.0835 |
| 0.7217        | 9.0   | 2250  | 0.7438          | 0.754    | 0.3470     | 2.0559 | 0.754    | 0.7531   | 0.0534 | 0.0766 |
| 0.5268        | 10.0  | 2500  | 0.7322          | 0.758    | 0.3443     | 2.1043 | 0.7580   | 0.7584   | 0.0510 | 0.0742 |
| 0.5268        | 11.0  | 2750  | 0.7003          | 0.7632   | 0.3335     | 2.0510 | 0.7632   | 0.7639   | 0.0472 | 0.0697 |
| 0.4197        | 12.0  | 3000  | 0.6921          | 0.7665   | 0.3325     | 2.0569 | 0.7665   | 0.7668   | 0.0568 | 0.0694 |
| 0.4197        | 13.0  | 3250  | 0.7003          | 0.7618   | 0.3330     | 2.0293 | 0.7618   | 0.7618   | 0.0465 | 0.0721 |
| 0.3575        | 14.0  | 3500  | 0.6681          | 0.7728   | 0.3244     | 2.0037 | 0.7728   | 0.7739   | 0.0505 | 0.0664 |
| 0.3575        | 15.0  | 3750  | 0.6862          | 0.7718   | 0.3279     | 2.0294 | 0.7717   | 0.7727   | 0.0442 | 0.0693 |
| 0.3181        | 16.0  | 4000  | 0.6681          | 0.7738   | 0.3246     | 2.0559 | 0.7738   | 0.7739   | 0.0509 | 0.0671 |
| 0.3181        | 17.0  | 4250  | 0.6473          | 0.7775   | 0.3177     | 1.9978 | 0.7775   | 0.7784   | 0.0494 | 0.0644 |
| 0.2874        | 18.0  | 4500  | 0.6448          | 0.78     | 0.3172     | 2.0396 | 0.78     | 0.7805   | 0.0495 | 0.0651 |
| 0.2874        | 19.0  | 4750  | 0.6484          | 0.779    | 0.3153     | 2.0251 | 0.779    | 0.7790   | 0.0519 | 0.0636 |
| 0.2691        | 20.0  | 5000  | 0.6430          | 0.7768   | 0.3164     | 2.0897 | 0.7768   | 0.7771   | 0.0489 | 0.0635 |
| 0.2691        | 21.0  | 5250  | 0.6363          | 0.78     | 0.3145     | 2.0663 | 0.78     | 0.7802   | 0.0476 | 0.0640 |
| 0.2509        | 22.0  | 5500  | 0.6327          | 0.782    | 0.3127     | 2.0358 | 0.782    | 0.7820   | 0.0440 | 0.0634 |
| 0.2509        | 23.0  | 5750  | 0.6287          | 0.7863   | 0.3113     | 2.0157 | 0.7863   | 0.7865   | 0.0463 | 0.0630 |
| 0.2393        | 24.0  | 6000  | 0.6315          | 0.7778   | 0.3137     | 2.0623 | 0.7778   | 0.7773   | 0.0492 | 0.0633 |
| 0.2393        | 25.0  | 6250  | 0.6345          | 0.7775   | 0.3149     | 2.0397 | 0.7775   | 0.7773   | 0.0514 | 0.0635 |
| 0.2291        | 26.0  | 6500  | 0.6233          | 0.7815   | 0.3102     | 1.9988 | 0.7815   | 0.7816   | 0.0444 | 0.0626 |
| 0.2291        | 27.0  | 6750  | 0.6224          | 0.783    | 0.3095     | 2.0085 | 0.7830   | 0.7830   | 0.0502 | 0.0615 |
| 0.2191        | 28.0  | 7000  | 0.6159          | 0.7835   | 0.3089     | 2.0340 | 0.7835   | 0.7834   | 0.0499 | 0.0614 |
| 0.2191        | 29.0  | 7250  | 0.6203          | 0.7825   | 0.3096     | 2.0280 | 0.7825   | 0.7825   | 0.0480 | 0.0617 |
| 0.2139        | 30.0  | 7500  | 0.6233          | 0.7802   | 0.3093     | 2.0660 | 0.7802   | 0.7805   | 0.0518 | 0.0609 |
| 0.2139        | 31.0  | 7750  | 0.6128          | 0.785    | 0.3049     | 2.0148 | 0.785    | 0.7851   | 0.0471 | 0.0604 |
| 0.2068        | 32.0  | 8000  | 0.6124          | 0.7855   | 0.3064     | 2.0336 | 0.7855   | 0.7855   | 0.0433 | 0.0604 |
| 0.2068        | 33.0  | 8250  | 0.6117          | 0.7835   | 0.3068     | 2.0208 | 0.7835   | 0.7833   | 0.0469 | 0.0604 |
| 0.202         | 34.0  | 8500  | 0.6105          | 0.7857   | 0.3063     | 1.9918 | 0.7857   | 0.7854   | 0.0454 | 0.0611 |
| 0.202         | 35.0  | 8750  | 0.6136          | 0.7877   | 0.3088     | 2.0272 | 0.7877   | 0.7884   | 0.0444 | 0.0607 |
| 0.1974        | 36.0  | 9000  | 0.6095          | 0.786    | 0.3052     | 2.0275 | 0.786    | 0.7862   | 0.0423 | 0.0600 |
| 0.1974        | 37.0  | 9250  | 0.6108          | 0.786    | 0.3077     | 2.0035 | 0.786    | 0.7860   | 0.0477 | 0.0606 |
| 0.1945        | 38.0  | 9500  | 0.6107          | 0.7817   | 0.3078     | 2.0042 | 0.7817   | 0.7820   | 0.0482 | 0.0611 |
| 0.1945        | 39.0  | 9750  | 0.6077          | 0.7875   | 0.3051     | 1.9959 | 0.7875   | 0.7878   | 0.0510 | 0.0599 |
| 0.1919        | 40.0  | 10000 | 0.6099          | 0.7863   | 0.3072     | 2.0323 | 0.7863   | 0.7866   | 0.0468 | 0.0603 |
| 0.1919        | 41.0  | 10250 | 0.6046          | 0.7847   | 0.3046     | 2.0113 | 0.7847   | 0.7850   | 0.0442 | 0.0600 |
| 0.1874        | 42.0  | 10500 | 0.6062          | 0.7865   | 0.3059     | 2.0055 | 0.7865   | 0.7865   | 0.0486 | 0.0598 |
| 0.1874        | 43.0  | 10750 | 0.6051          | 0.787    | 0.3042     | 2.0151 | 0.787    | 0.7870   | 0.0451 | 0.0596 |
| 0.1859        | 44.0  | 11000 | 0.6082          | 0.7855   | 0.3063     | 2.0123 | 0.7855   | 0.7860   | 0.0470 | 0.0600 |
| 0.1859        | 45.0  | 11250 | 0.6066          | 0.7867   | 0.3047     | 2.0000 | 0.7868   | 0.7865   | 0.0479 | 0.0599 |
| 0.1856        | 46.0  | 11500 | 0.6049          | 0.7863   | 0.3054     | 2.0058 | 0.7863   | 0.7861   | 0.0475 | 0.0598 |
| 0.1856        | 47.0  | 11750 | 0.6041          | 0.7887   | 0.3047     | 1.9992 | 0.7887   | 0.7891   | 0.0482 | 0.0595 |
| 0.1842        | 48.0  | 12000 | 0.6063          | 0.7843   | 0.3055     | 2.0346 | 0.7843   | 0.7843   | 0.0480 | 0.0601 |
| 0.1842        | 49.0  | 12250 | 0.6058          | 0.786    | 0.3051     | 2.0319 | 0.786    | 0.7861   | 0.0481 | 0.0598 |
| 0.1829        | 50.0  | 12500 | 0.6048          | 0.7867   | 0.3046     | 2.0167 | 0.7868   | 0.7867   | 0.0468 | 0.0597 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3

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