---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-101-finetuned_resnet101-sgd-optimizer20-autotags
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8847619047619047
---

<!-- 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. -->

# resnet-101-finetuned_resnet101-sgd-optimizer20-autotags

This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3318
- Accuracy: 0.8848

## 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: 0.1
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1302        | 0.99  | 65   | 1.0040          | 0.6724   |
| 1.1708        | 1.99  | 130  | 1.4856          | 0.5495   |
| 1.141         | 2.99  | 195  | 1.1486          | 0.6352   |
| 1.0119        | 3.99  | 260  | 0.8829          | 0.7314   |
| 0.8091        | 4.99  | 325  | 0.8301          | 0.7419   |
| 0.7878        | 5.99  | 390  | 0.8121          | 0.7333   |
| 0.6827        | 6.99  | 455  | 0.6047          | 0.7990   |
| 0.5525        | 7.99  | 520  | 0.6028          | 0.8048   |
| 0.5787        | 8.99  | 585  | 0.5183          | 0.8352   |
| 0.4797        | 9.99  | 650  | 0.4737          | 0.8543   |
| 0.4224        | 10.99 | 715  | 0.4943          | 0.8305   |
| 0.4389        | 11.99 | 780  | 0.4162          | 0.8629   |
| 0.4142        | 12.99 | 845  | 0.4000          | 0.8629   |
| 0.3144        | 13.99 | 910  | 0.3833          | 0.8695   |
| 0.2915        | 14.99 | 975  | 0.3688          | 0.8733   |
| 0.3302        | 15.99 | 1040 | 0.3643          | 0.8810   |
| 0.2954        | 16.99 | 1105 | 0.3446          | 0.8867   |
| 0.2186        | 17.99 | 1170 | 0.3571          | 0.8905   |
| 0.1812        | 18.99 | 1235 | 0.3334          | 0.8886   |
| 0.1911        | 19.99 | 1300 | 0.3318          | 0.8848   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2