---
library_name: transformers
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: cat_dog_classifier_with_small_datasest
  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: 0.95
---

<!-- 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. -->

# cat_dog_classifier_with_small_datasest

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: 0.1369
- Accuracy: 0.95

## 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: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 70   | 0.5422          | 0.8571   |
| No log        | 2.0   | 140  | 0.5221          | 0.8786   |
| No log        | 3.0   | 210  | 0.4977          | 0.8571   |
| No log        | 4.0   | 280  | 0.4617          | 0.8786   |
| No log        | 5.0   | 350  | 0.3932          | 0.9143   |
| No log        | 6.0   | 420  | 0.3411          | 0.9143   |
| No log        | 7.0   | 490  | 0.2884          | 0.9143   |
| 0.4971        | 8.0   | 560  | 0.2429          | 0.9286   |
| 0.4971        | 9.0   | 630  | 0.2151          | 0.9429   |
| 0.4971        | 10.0  | 700  | 0.1962          | 0.9286   |
| 0.4971        | 11.0  | 770  | 0.1727          | 0.9357   |
| 0.4971        | 12.0  | 840  | 0.1676          | 0.95     |
| 0.4971        | 13.0  | 910  | 0.1764          | 0.9286   |
| 0.4971        | 14.0  | 980  | 0.1565          | 0.9429   |
| 0.2878        | 15.0  | 1050 | 0.1578          | 0.9429   |
| 0.2878        | 16.0  | 1120 | 0.1577          | 0.9429   |
| 0.2878        | 17.0  | 1190 | 0.1393          | 0.9429   |
| 0.2878        | 18.0  | 1260 | 0.1472          | 0.9429   |
| 0.2878        | 19.0  | 1330 | 0.1315          | 0.95     |
| 0.2878        | 20.0  | 1400 | 0.1369          | 0.95     |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0