---
license: apache-2.0
base_model: microsoft/resnet-101
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: resnet101_rvl-cdip
  results: []
---

<!-- 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

This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6158
- Accuracy: 0.8210
- Brier Loss: 0.2556
- Nll: 1.7696
- F1 Micro: 0.8210
- F1 Macro: 0.8209
- Ece: 0.0176
- Aurc: 0.0418

## 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: 2e-05
- 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: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Brier Loss | Nll    | F1 Micro | F1 Macro | Ece    | Aurc   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 1.3521        | 1.0   | 5000  | 1.2626          | 0.6133   | 0.5108     | 2.7262 | 0.6133   | 0.6042   | 0.0455 | 0.1644 |
| 0.942         | 2.0   | 10000 | 0.9005          | 0.7318   | 0.3723     | 2.2139 | 0.7318   | 0.7293   | 0.0174 | 0.0862 |
| 0.7983        | 3.0   | 15000 | 0.7691          | 0.7723   | 0.3198     | 2.0444 | 0.7723   | 0.7714   | 0.0139 | 0.0641 |
| 0.7167        | 4.0   | 20000 | 0.7048          | 0.7924   | 0.2931     | 1.9414 | 0.7924   | 0.7931   | 0.0135 | 0.0541 |
| 0.6656        | 5.0   | 25000 | 0.6658          | 0.8052   | 0.2770     | 1.8581 | 0.8052   | 0.8056   | 0.0108 | 0.0486 |
| 0.6252        | 6.0   | 30000 | 0.6415          | 0.8117   | 0.2670     | 1.8157 | 0.8117   | 0.8112   | 0.0128 | 0.0455 |
| 0.6038        | 7.0   | 35000 | 0.6269          | 0.8176   | 0.2607     | 1.7833 | 0.8176   | 0.8180   | 0.0144 | 0.0432 |
| 0.5784        | 8.0   | 40000 | 0.6217          | 0.8195   | 0.2583     | 1.7723 | 0.8195   | 0.8195   | 0.0151 | 0.0425 |
| 0.5583        | 9.0   | 45000 | 0.6150          | 0.8214   | 0.2553     | 1.7719 | 0.8214   | 0.8214   | 0.0164 | 0.0415 |
| 0.5519        | 10.0  | 50000 | 0.6158          | 0.8210   | 0.2556     | 1.7696 | 0.8210   | 0.8209   | 0.0176 | 0.0418 |


### Framework versions

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