
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5

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: 2.9013
- Accuracy: 0.7933
- Brier Loss: 0.3080
- Nll: 1.8102
- F1 Micro: 0.7932
- F1 Macro: 0.7937
- Ece: 0.0719
- Aurc: 0.0635

## 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   | 6.0054          | 0.098    | 0.9327     | 9.3196 | 0.0980   | 0.0481   | 0.0462 | 0.8670 |
| 6.0141        | 2.0   | 500   | 5.4713          | 0.2195   | 0.8933     | 5.2235 | 0.2195   | 0.1452   | 0.1046 | 0.7129 |
| 6.0141        | 3.0   | 750   | 4.4006          | 0.4535   | 0.7034     | 3.0178 | 0.4535   | 0.4351   | 0.1373 | 0.3334 |
| 4.5079        | 4.0   | 1000  | 3.8431          | 0.59     | 0.5686     | 2.5843 | 0.59     | 0.5822   | 0.1309 | 0.2072 |
| 4.5079        | 5.0   | 1250  | 3.5315          | 0.6552   | 0.4864     | 2.4330 | 0.6552   | 0.6537   | 0.1048 | 0.1504 |
| 3.5028        | 6.0   | 1500  | 3.2850          | 0.707    | 0.4163     | 2.2375 | 0.707    | 0.7082   | 0.0790 | 0.1111 |
| 3.5028        | 7.0   | 1750  | 3.0974          | 0.7312   | 0.3721     | 2.0933 | 0.7312   | 0.7328   | 0.0452 | 0.0899 |
| 3.0599        | 8.0   | 2000  | 3.0385          | 0.7455   | 0.3561     | 2.0148 | 0.7455   | 0.7456   | 0.0432 | 0.0838 |
| 3.0599        | 9.0   | 2250  | 2.9978          | 0.7565   | 0.3432     | 1.9780 | 0.7565   | 0.7572   | 0.0437 | 0.0777 |
| 2.8562        | 10.0  | 2500  | 2.9853          | 0.7622   | 0.3397     | 1.9176 | 0.7622   | 0.7619   | 0.0495 | 0.0751 |
| 2.8562        | 11.0  | 2750  | 2.9803          | 0.7615   | 0.3385     | 1.9327 | 0.7615   | 0.7627   | 0.0547 | 0.0760 |
| 2.7414        | 12.0  | 3000  | 2.9711          | 0.7658   | 0.3322     | 1.9439 | 0.7658   | 0.7661   | 0.0495 | 0.0740 |
| 2.7414        | 13.0  | 3250  | 2.9618          | 0.771    | 0.3276     | 1.8599 | 0.771    | 0.7718   | 0.0548 | 0.0704 |
| 2.6658        | 14.0  | 3500  | 2.9534          | 0.7762   | 0.3252     | 1.8935 | 0.7762   | 0.7770   | 0.0581 | 0.0699 |
| 2.6658        | 15.0  | 3750  | 2.9568          | 0.776    | 0.3248     | 1.8836 | 0.776    | 0.7776   | 0.0588 | 0.0699 |
| 2.6197        | 16.0  | 4000  | 2.9196          | 0.7812   | 0.3169     | 1.8338 | 0.7812   | 0.7814   | 0.0601 | 0.0655 |
| 2.6197        | 17.0  | 4250  | 2.9267          | 0.7785   | 0.3202     | 1.8430 | 0.7785   | 0.7783   | 0.0647 | 0.0677 |
| 2.5794        | 18.0  | 4500  | 2.9189          | 0.779    | 0.3155     | 1.8279 | 0.779    | 0.7794   | 0.0631 | 0.0661 |
| 2.5794        | 19.0  | 4750  | 2.9324          | 0.7823   | 0.3177     | 1.8508 | 0.7823   | 0.7823   | 0.0665 | 0.0669 |
| 2.5553        | 20.0  | 5000  | 2.9192          | 0.7837   | 0.3146     | 1.8312 | 0.7837   | 0.7840   | 0.0641 | 0.0654 |
| 2.5553        | 21.0  | 5250  | 2.9160          | 0.7817   | 0.3140     | 1.8366 | 0.7817   | 0.7828   | 0.0682 | 0.0658 |
| 2.53          | 22.0  | 5500  | 2.9172          | 0.7837   | 0.3139     | 1.8138 | 0.7837   | 0.7842   | 0.0602 | 0.0652 |
| 2.53          | 23.0  | 5750  | 2.9132          | 0.7875   | 0.3134     | 1.8254 | 0.7875   | 0.7877   | 0.0656 | 0.0646 |
| 2.5127        | 24.0  | 6000  | 2.9108          | 0.7875   | 0.3132     | 1.8367 | 0.7875   | 0.7869   | 0.0669 | 0.0652 |
| 2.5127        | 25.0  | 6250  | 2.9272          | 0.7837   | 0.3139     | 1.8551 | 0.7837   | 0.7843   | 0.0632 | 0.0653 |
| 2.4979        | 26.0  | 6500  | 2.9157          | 0.7867   | 0.3128     | 1.8101 | 0.7868   | 0.7876   | 0.0655 | 0.0647 |
| 2.4979        | 27.0  | 6750  | 2.9031          | 0.785    | 0.3112     | 1.8089 | 0.785    | 0.7856   | 0.0688 | 0.0639 |
| 2.4814        | 28.0  | 7000  | 2.9094          | 0.7875   | 0.3110     | 1.8594 | 0.7875   | 0.7880   | 0.0677 | 0.0646 |
| 2.4814        | 29.0  | 7250  | 2.9110          | 0.7885   | 0.3116     | 1.8150 | 0.7885   | 0.7891   | 0.0696 | 0.0639 |
| 2.4741        | 30.0  | 7500  | 2.9039          | 0.7877   | 0.3091     | 1.8471 | 0.7877   | 0.7887   | 0.0694 | 0.0632 |
| 2.4741        | 31.0  | 7750  | 2.9029          | 0.7907   | 0.3087     | 1.7604 | 0.7907   | 0.7917   | 0.0691 | 0.0633 |
| 2.4626        | 32.0  | 8000  | 2.8983          | 0.7877   | 0.3094     | 1.8191 | 0.7877   | 0.7884   | 0.0677 | 0.0625 |
| 2.4626        | 33.0  | 8250  | 2.9024          | 0.7897   | 0.3088     | 1.8025 | 0.7897   | 0.7905   | 0.0720 | 0.0635 |
| 2.4558        | 34.0  | 8500  | 2.9055          | 0.792    | 0.3070     | 1.7869 | 0.792    | 0.7920   | 0.0667 | 0.0628 |
| 2.4558        | 35.0  | 8750  | 2.9055          | 0.788    | 0.3104     | 1.8349 | 0.788    | 0.7883   | 0.0733 | 0.0645 |
| 2.4481        | 36.0  | 9000  | 2.9061          | 0.7887   | 0.3078     | 1.7840 | 0.7887   | 0.7898   | 0.0676 | 0.0642 |
| 2.4481        | 37.0  | 9250  | 2.9086          | 0.7917   | 0.3102     | 1.7942 | 0.7917   | 0.7923   | 0.0716 | 0.0644 |
| 2.4422        | 38.0  | 9500  | 2.9067          | 0.7897   | 0.3084     | 1.7915 | 0.7897   | 0.7900   | 0.0704 | 0.0637 |
| 2.4422        | 39.0  | 9750  | 2.9080          | 0.7927   | 0.3092     | 1.7951 | 0.7927   | 0.7930   | 0.0709 | 0.0631 |
| 2.4386        | 40.0  | 10000 | 2.9064          | 0.7943   | 0.3084     | 1.8079 | 0.7943   | 0.7949   | 0.0734 | 0.0635 |
| 2.4386        | 41.0  | 10250 | 2.8990          | 0.792    | 0.3056     | 1.7918 | 0.792    | 0.7924   | 0.0699 | 0.0623 |
| 2.4312        | 42.0  | 10500 | 2.9057          | 0.7893   | 0.3090     | 1.7892 | 0.7893   | 0.7901   | 0.0735 | 0.0641 |
| 2.4312        | 43.0  | 10750 | 2.8998          | 0.7923   | 0.3079     | 1.7909 | 0.7923   | 0.7932   | 0.0707 | 0.0630 |
| 2.4294        | 44.0  | 11000 | 2.9108          | 0.7905   | 0.3090     | 1.8220 | 0.7905   | 0.7916   | 0.0773 | 0.0636 |
| 2.4294        | 45.0  | 11250 | 2.9030          | 0.7927   | 0.3086     | 1.8126 | 0.7927   | 0.7932   | 0.0710 | 0.0631 |
| 2.4282        | 46.0  | 11500 | 2.9033          | 0.7915   | 0.3077     | 1.8234 | 0.7915   | 0.7920   | 0.0712 | 0.0631 |
| 2.4282        | 47.0  | 11750 | 2.8975          | 0.7957   | 0.3063     | 1.8070 | 0.7957   | 0.7968   | 0.0702 | 0.0630 |
| 2.4246        | 48.0  | 12000 | 2.9049          | 0.7935   | 0.3085     | 1.8090 | 0.7935   | 0.7944   | 0.0722 | 0.0635 |
| 2.4246        | 49.0  | 12250 | 2.9020          | 0.792    | 0.3075     | 1.8233 | 0.792    | 0.7927   | 0.0700 | 0.0638 |
| 2.4227        | 50.0  | 12500 | 2.9013          | 0.7933   | 0.3080     | 1.8102 | 0.7932   | 0.7937   | 0.0719 | 0.0635 |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/resnet-50", "model-index": [{"name": "resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5", "results": []}]}
transformers pytorch resnet image-classification generated_from_trainer base_model:microsoft/resnet-50 license:apache-2.0 autotrain_compatible endpoints_compatible region:us