---
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: resnet101_rvl-cdip-_rvl_cdip-NK1000__CEKD_t2.5_a0.5
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# resnet101_rvl-cdip-_rvl_cdip-NK1000__CEKD_t2.5_a0.5

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: 0.6065
- Accuracy: 0.7915
- Brier Loss: 0.3054
- Nll: 1.9957
- F1 Micro: 0.7915
- F1 Macro: 0.7910
- Ece: 0.0453
- Aurc: 0.0607

## 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   | 4.1565          | 0.1378   | 0.9318     | 7.9039 | 0.1378   | 0.1073   | 0.0673 | 0.8326 |
| 4.1485        | 2.0   | 500   | 3.6932          | 0.3235   | 0.8832     | 5.1525 | 0.3235   | 0.2725   | 0.2044 | 0.5507 |
| 4.1485        | 3.0   | 750   | 2.3374          | 0.4725   | 0.6611     | 3.3127 | 0.4725   | 0.4311   | 0.0839 | 0.2921 |
| 2.392         | 4.0   | 1000  | 1.6516          | 0.588    | 0.5470     | 2.8681 | 0.588    | 0.5789   | 0.0620 | 0.1929 |
| 2.392         | 5.0   | 1250  | 1.3260          | 0.6488   | 0.4782     | 2.6378 | 0.6488   | 0.6444   | 0.0486 | 0.1458 |
| 1.1422        | 6.0   | 1500  | 1.0390          | 0.702    | 0.4156     | 2.4086 | 0.702    | 0.7029   | 0.0576 | 0.1097 |
| 1.1422        | 7.0   | 1750  | 0.8420          | 0.7288   | 0.3738     | 2.2222 | 0.7288   | 0.7300   | 0.0553 | 0.0888 |
| 0.708         | 8.0   | 2000  | 0.7753          | 0.7398   | 0.3586     | 2.1518 | 0.7398   | 0.7396   | 0.0587 | 0.0826 |
| 0.708         | 9.0   | 2250  | 0.7797          | 0.7462   | 0.3580     | 2.1095 | 0.7462   | 0.7457   | 0.0581 | 0.0820 |
| 0.5195        | 10.0  | 2500  | 0.7101          | 0.7602   | 0.3404     | 2.0711 | 0.7602   | 0.7612   | 0.0473 | 0.0733 |
| 0.5195        | 11.0  | 2750  | 0.6971          | 0.7645   | 0.3338     | 2.0649 | 0.7645   | 0.7653   | 0.0541 | 0.0715 |
| 0.4176        | 12.0  | 3000  | 0.6936          | 0.7712   | 0.3302     | 2.0265 | 0.7712   | 0.7708   | 0.0515 | 0.0702 |
| 0.4176        | 13.0  | 3250  | 0.6991          | 0.7662   | 0.3346     | 2.0582 | 0.7663   | 0.7657   | 0.0581 | 0.0723 |
| 0.3573        | 14.0  | 3500  | 0.6672          | 0.7722   | 0.3246     | 2.0053 | 0.7722   | 0.7723   | 0.0551 | 0.0683 |
| 0.3573        | 15.0  | 3750  | 0.6735          | 0.777    | 0.3244     | 2.0387 | 0.777    | 0.7782   | 0.0488 | 0.0671 |
| 0.3193        | 16.0  | 4000  | 0.6567          | 0.776    | 0.3216     | 2.0256 | 0.776    | 0.7773   | 0.0499 | 0.0678 |
| 0.3193        | 17.0  | 4250  | 0.6498          | 0.78     | 0.3184     | 1.9865 | 0.78     | 0.7802   | 0.0477 | 0.0662 |
| 0.2893        | 18.0  | 4500  | 0.6763          | 0.7755   | 0.3264     | 2.0844 | 0.7755   | 0.7755   | 0.0531 | 0.0697 |
| 0.2893        | 19.0  | 4750  | 0.6519          | 0.7815   | 0.3183     | 2.0458 | 0.7815   | 0.7817   | 0.0513 | 0.0658 |
| 0.271         | 20.0  | 5000  | 0.6432          | 0.7823   | 0.3147     | 2.0291 | 0.7823   | 0.7827   | 0.0440 | 0.0645 |
| 0.271         | 21.0  | 5250  | 0.6456          | 0.781    | 0.3156     | 2.0493 | 0.7810   | 0.7813   | 0.0487 | 0.0652 |
| 0.2516        | 22.0  | 5500  | 0.6336          | 0.7823   | 0.3144     | 1.9829 | 0.7823   | 0.7822   | 0.0522 | 0.0642 |
| 0.2516        | 23.0  | 5750  | 0.6333          | 0.7837   | 0.3128     | 2.0196 | 0.7837   | 0.7836   | 0.0492 | 0.0641 |
| 0.2397        | 24.0  | 6000  | 0.6337          | 0.7817   | 0.3147     | 2.0180 | 0.7817   | 0.7815   | 0.0494 | 0.0644 |
| 0.2397        | 25.0  | 6250  | 0.6347          | 0.7857   | 0.3145     | 2.0187 | 0.7857   | 0.7856   | 0.0510 | 0.0641 |
| 0.23          | 26.0  | 6500  | 0.6311          | 0.7815   | 0.3129     | 2.0132 | 0.7815   | 0.7819   | 0.0495 | 0.0637 |
| 0.23          | 27.0  | 6750  | 0.6329          | 0.7853   | 0.3125     | 2.0708 | 0.7853   | 0.7852   | 0.0502 | 0.0635 |
| 0.2191        | 28.0  | 7000  | 0.6222          | 0.786    | 0.3109     | 2.0022 | 0.786    | 0.7856   | 0.0483 | 0.0638 |
| 0.2191        | 29.0  | 7250  | 0.6195          | 0.7863   | 0.3096     | 2.0028 | 0.7863   | 0.7859   | 0.0550 | 0.0620 |
| 0.2155        | 30.0  | 7500  | 0.6196          | 0.7883   | 0.3090     | 1.9972 | 0.7883   | 0.7883   | 0.0486 | 0.0624 |
| 0.2155        | 31.0  | 7750  | 0.6167          | 0.787    | 0.3080     | 2.0173 | 0.787    | 0.7871   | 0.0443 | 0.0623 |
| 0.2074        | 32.0  | 8000  | 0.6143          | 0.7897   | 0.3073     | 2.0223 | 0.7897   | 0.7893   | 0.0443 | 0.0614 |
| 0.2074        | 33.0  | 8250  | 0.6123          | 0.787    | 0.3078     | 1.9869 | 0.787    | 0.7866   | 0.0458 | 0.0619 |
| 0.2028        | 34.0  | 8500  | 0.6137          | 0.7873   | 0.3070     | 1.9883 | 0.7873   | 0.7868   | 0.0457 | 0.0623 |
| 0.2028        | 35.0  | 8750  | 0.6152          | 0.786    | 0.3085     | 2.0108 | 0.786    | 0.7863   | 0.0497 | 0.0626 |
| 0.1982        | 36.0  | 9000  | 0.6133          | 0.7863   | 0.3077     | 2.0205 | 0.7863   | 0.7862   | 0.0515 | 0.0615 |
| 0.1982        | 37.0  | 9250  | 0.6145          | 0.7877   | 0.3081     | 1.9930 | 0.7877   | 0.7879   | 0.0444 | 0.0621 |
| 0.1948        | 38.0  | 9500  | 0.6116          | 0.7857   | 0.3078     | 2.0072 | 0.7857   | 0.7854   | 0.0508 | 0.0619 |
| 0.1948        | 39.0  | 9750  | 0.6090          | 0.788    | 0.3059     | 1.9954 | 0.788    | 0.7882   | 0.0430 | 0.0614 |
| 0.1933        | 40.0  | 10000 | 0.6143          | 0.7897   | 0.3072     | 1.9943 | 0.7897   | 0.7899   | 0.0462 | 0.0618 |
| 0.1933        | 41.0  | 10250 | 0.6061          | 0.7887   | 0.3041     | 1.9900 | 0.7887   | 0.7889   | 0.0439 | 0.0606 |
| 0.1882        | 42.0  | 10500 | 0.6070          | 0.7865   | 0.3058     | 1.9907 | 0.7865   | 0.7868   | 0.0438 | 0.0607 |
| 0.1882        | 43.0  | 10750 | 0.6083          | 0.788    | 0.3054     | 2.0095 | 0.788    | 0.7877   | 0.0489 | 0.0608 |
| 0.1871        | 44.0  | 11000 | 0.6083          | 0.787    | 0.3054     | 1.9828 | 0.787    | 0.7872   | 0.0469 | 0.0607 |
| 0.1871        | 45.0  | 11250 | 0.6092          | 0.7893   | 0.3057     | 2.0140 | 0.7893   | 0.7891   | 0.0483 | 0.0608 |
| 0.1862        | 46.0  | 11500 | 0.6057          | 0.7893   | 0.3053     | 2.0064 | 0.7893   | 0.7890   | 0.0450 | 0.0609 |
| 0.1862        | 47.0  | 11750 | 0.6042          | 0.79     | 0.3044     | 1.9691 | 0.79     | 0.7899   | 0.0435 | 0.0607 |
| 0.1845        | 48.0  | 12000 | 0.6068          | 0.79     | 0.3053     | 2.0052 | 0.79     | 0.7899   | 0.0438 | 0.0608 |
| 0.1845        | 49.0  | 12250 | 0.6081          | 0.7893   | 0.3062     | 2.0117 | 0.7893   | 0.7890   | 0.0485 | 0.0612 |
| 0.1836        | 50.0  | 12500 | 0.6065          | 0.7915   | 0.3054     | 1.9957 | 0.7915   | 0.7910   | 0.0453 | 0.0607 |


### Framework versions

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