---
library_name: transformers
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: exp_result1_resnet
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8670306965761512
    - name: Precision
      type: precision
      value: 0.8883902785977921
    - name: Recall
      type: recall
      value: 0.8670306965761512
    - name: F1
      type: f1
      value: 0.8764560257962113
---

<!-- 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. -->

# exp_result1_resnet

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6451
- Accuracy: 0.8670
- Precision: 0.8884
- Recall: 0.8670
- F1: 0.8765

## 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: 24
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2343        | 1.0   | 759  | 0.4630          | 0.8271   | 0.8560    | 0.8271 | 0.8405 |
| 0.0895        | 2.0   | 1518 | 0.5436          | 0.8557   | 0.8589    | 0.8557 | 0.8573 |
| 0.0576        | 3.0   | 2277 | 0.6000          | 0.8709   | 0.8675    | 0.8709 | 0.8692 |
| 0.0442        | 4.0   | 3036 | 0.5490          | 0.8676   | 0.8808    | 0.8676 | 0.8738 |
| 0.0353        | 5.0   | 3795 | 0.6611          | 0.8612   | 0.8758    | 0.8612 | 0.8680 |
| 0.0234        | 6.0   | 4554 | 0.6407          | 0.8556   | 0.8932    | 0.8556 | 0.8710 |
| 0.0232        | 7.0   | 5313 | 0.6700          | 0.8738   | 0.8735    | 0.8738 | 0.8736 |
| 0.0206        | 8.0   | 6072 | 0.6441          | 0.8717   | 0.8925    | 0.8717 | 0.8808 |
| 0.0197        | 9.0   | 6831 | 0.7248          | 0.8517   | 0.8927    | 0.8517 | 0.8684 |
| 0.0161        | 10.0  | 7590 | 0.6451          | 0.8670   | 0.8884    | 0.8670 | 0.8765 |


### Framework versions

- Transformers 4.53.0.dev0
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1