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# resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_CEKD_t1.0_a0.5_rand

This model is a fine-tuned version of [bdpc/resnet101_rvl-cdip](https://huggingface.co/bdpc/resnet101_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5803
- Accuracy: 0.755
- Brier Loss: 0.3388
- Nll: 2.0737
- F1 Micro: 0.755
- F1 Macro: 0.7552
- Ece: 0.0476
- Aurc: 0.0744

## 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   | 2.0995          | 0.2357   | 0.8533     | 3.3592 | 0.2357   | 0.1996   | 0.0692 | 0.5667 |
| 2.1408        | 2.0   | 500   | 1.9262          | 0.309    | 0.8322     | 3.1505 | 0.309    | 0.2850   | 0.1062 | 0.5376 |
| 2.1408        | 3.0   | 750   | 1.7386          | 0.3882   | 0.7711     | 2.9990 | 0.3882   | 0.3744   | 0.0840 | 0.4488 |
| 1.5977        | 4.0   | 1000  | 1.7847          | 0.3885   | 0.7913     | 2.9899 | 0.3885   | 0.3901   | 0.1236 | 0.4675 |
| 1.5977        | 5.0   | 1250  | 1.9515          | 0.3852   | 0.8032     | 3.2494 | 0.3852   | 0.3563   | 0.1897 | 0.4332 |
| 1.2396        | 6.0   | 1500  | 1.4812          | 0.4918   | 0.6698     | 2.8748 | 0.4918   | 0.4603   | 0.1135 | 0.2969 |
| 1.2396        | 7.0   | 1750  | 0.9703          | 0.6342   | 0.4956     | 2.5074 | 0.6342   | 0.6278   | 0.0552 | 0.1583 |
| 0.8921        | 8.0   | 2000  | 0.8731          | 0.6522   | 0.4638     | 2.3758 | 0.6522   | 0.6529   | 0.0503 | 0.1375 |
| 0.8921        | 9.0   | 2250  | 0.8524          | 0.6763   | 0.4479     | 2.3526 | 0.6763   | 0.6703   | 0.0533 | 0.1284 |
| 0.6456        | 10.0  | 2500  | 0.8349          | 0.6753   | 0.4449     | 2.3791 | 0.6753   | 0.6731   | 0.0534 | 0.1266 |
| 0.6456        | 11.0  | 2750  | 0.8366          | 0.6855   | 0.4410     | 2.3328 | 0.6855   | 0.6840   | 0.0606 | 0.1256 |
| 0.4631        | 12.0  | 3000  | 0.8733          | 0.6787   | 0.4503     | 2.3609 | 0.6787   | 0.6744   | 0.0814 | 0.1300 |
| 0.4631        | 13.0  | 3250  | 0.8087          | 0.6877   | 0.4278     | 2.3090 | 0.6877   | 0.6836   | 0.0671 | 0.1149 |
| 0.3462        | 14.0  | 3500  | 0.7505          | 0.7107   | 0.4037     | 2.2700 | 0.7107   | 0.7112   | 0.0461 | 0.1028 |
| 0.3462        | 15.0  | 3750  | 0.7638          | 0.708    | 0.4031     | 2.3569 | 0.708    | 0.7070   | 0.0615 | 0.1001 |
| 0.277         | 16.0  | 4000  | 0.8430          | 0.686    | 0.4364     | 2.4615 | 0.686    | 0.6876   | 0.0587 | 0.1229 |
| 0.277         | 17.0  | 4250  | 0.7287          | 0.7185   | 0.3946     | 2.2848 | 0.7185   | 0.7172   | 0.0474 | 0.0992 |
| 0.2452        | 18.0  | 4500  | 0.7114          | 0.7175   | 0.3886     | 2.2763 | 0.7175   | 0.7174   | 0.0551 | 0.0957 |
| 0.2452        | 19.0  | 4750  | 0.6867          | 0.73     | 0.3762     | 2.2263 | 0.7300   | 0.7300   | 0.0483 | 0.0916 |
| 0.2209        | 20.0  | 5000  | 0.7205          | 0.722    | 0.3918     | 2.2900 | 0.722    | 0.7235   | 0.0515 | 0.0993 |
| 0.2209        | 21.0  | 5250  | 0.6912          | 0.7338   | 0.3732     | 2.2467 | 0.7338   | 0.7333   | 0.0505 | 0.0892 |
| 0.2048        | 22.0  | 5500  | 0.7503          | 0.7073   | 0.4025     | 2.3059 | 0.7073   | 0.7067   | 0.0517 | 0.1025 |
| 0.2048        | 23.0  | 5750  | 0.6887          | 0.724    | 0.3822     | 2.2328 | 0.724    | 0.7249   | 0.0470 | 0.0930 |
| 0.1941        | 24.0  | 6000  | 0.6539          | 0.7345   | 0.3664     | 2.1801 | 0.7345   | 0.7348   | 0.0479 | 0.0853 |
| 0.1941        | 25.0  | 6250  | 0.6466          | 0.7362   | 0.3612     | 2.1945 | 0.7362   | 0.7343   | 0.0501 | 0.0827 |
| 0.1836        | 26.0  | 6500  | 0.6709          | 0.731    | 0.3738     | 2.2298 | 0.731    | 0.7351   | 0.0484 | 0.0900 |
| 0.1836        | 27.0  | 6750  | 0.6462          | 0.7365   | 0.3660     | 2.1816 | 0.7365   | 0.7392   | 0.0498 | 0.0861 |
| 0.1753        | 28.0  | 7000  | 0.6312          | 0.746    | 0.3580     | 2.1495 | 0.746    | 0.7469   | 0.0465 | 0.0816 |
| 0.1753        | 29.0  | 7250  | 0.6152          | 0.7518   | 0.3508     | 2.1226 | 0.7518   | 0.7514   | 0.0390 | 0.0788 |
| 0.1682        | 30.0  | 7500  | 0.6555          | 0.737    | 0.3671     | 2.1765 | 0.737    | 0.7373   | 0.0412 | 0.0856 |
| 0.1682        | 31.0  | 7750  | 0.6287          | 0.741    | 0.3565     | 2.1122 | 0.7410   | 0.7419   | 0.0438 | 0.0807 |
| 0.1622        | 32.0  | 8000  | 0.6226          | 0.7442   | 0.3571     | 2.1540 | 0.7442   | 0.7426   | 0.0445 | 0.0821 |
| 0.1622        | 33.0  | 8250  | 0.6171          | 0.748    | 0.3522     | 2.1308 | 0.748    | 0.7482   | 0.0412 | 0.0793 |
| 0.1565        | 34.0  | 8500  | 0.6127          | 0.7445   | 0.3523     | 2.1276 | 0.7445   | 0.7434   | 0.0402 | 0.0800 |
| 0.1565        | 35.0  | 8750  | 0.6059          | 0.7482   | 0.3476     | 2.1240 | 0.7482   | 0.7481   | 0.0471 | 0.0770 |
| 0.1513        | 36.0  | 9000  | 0.6063          | 0.7462   | 0.3493     | 2.0960 | 0.7462   | 0.7472   | 0.0448 | 0.0790 |
| 0.1513        | 37.0  | 9250  | 0.6078          | 0.746    | 0.3513     | 2.1283 | 0.746    | 0.7461   | 0.0433 | 0.0792 |
| 0.1467        | 38.0  | 9500  | 0.5969          | 0.753    | 0.3448     | 2.1077 | 0.753    | 0.7538   | 0.0364 | 0.0776 |
| 0.1467        | 39.0  | 9750  | 0.6008          | 0.7475   | 0.3474     | 2.1154 | 0.7475   | 0.7485   | 0.0410 | 0.0794 |
| 0.1424        | 40.0  | 10000 | 0.5998          | 0.7518   | 0.3464     | 2.0894 | 0.7518   | 0.7526   | 0.0440 | 0.0783 |
| 0.1424        | 41.0  | 10250 | 0.5904          | 0.7508   | 0.3422     | 2.0606 | 0.7508   | 0.7505   | 0.0455 | 0.0758 |
| 0.1384        | 42.0  | 10500 | 0.5890          | 0.7552   | 0.3410     | 2.0815 | 0.7552   | 0.7548   | 0.0362 | 0.0744 |
| 0.1384        | 43.0  | 10750 | 0.5847          | 0.7562   | 0.3404     | 2.0912 | 0.7562   | 0.7568   | 0.0417 | 0.0747 |
| 0.1348        | 44.0  | 11000 | 0.5856          | 0.7528   | 0.3409     | 2.0675 | 0.7528   | 0.7525   | 0.0431 | 0.0753 |
| 0.1348        | 45.0  | 11250 | 0.5859          | 0.7552   | 0.3409     | 2.0913 | 0.7552   | 0.7551   | 0.0413 | 0.0750 |
| 0.1319        | 46.0  | 11500 | 0.5810          | 0.7528   | 0.3391     | 2.0654 | 0.7528   | 0.7526   | 0.0396 | 0.0747 |
| 0.1319        | 47.0  | 11750 | 0.5803          | 0.7535   | 0.3387     | 2.0705 | 0.7535   | 0.7534   | 0.0444 | 0.0746 |
| 0.13          | 48.0  | 12000 | 0.5819          | 0.7532   | 0.3393     | 2.0563 | 0.7532   | 0.7540   | 0.0390 | 0.0749 |
| 0.13          | 49.0  | 12250 | 0.5800          | 0.756    | 0.3383     | 2.0649 | 0.756    | 0.7562   | 0.0370 | 0.0740 |
| 0.1286        | 50.0  | 12500 | 0.5803          | 0.755    | 0.3388     | 2.0737 | 0.755    | 0.7552   | 0.0476 | 0.0744 |


### Framework versions

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

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