---
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_trainer
datasets:
- cats_vs_dogs
metrics:
- accuracy
model-index:
- name: resnet-50-finetuned-dog-vs-cat
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: cats_vs_dogs
      type: cats_vs_dogs
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9918838103374626
---

<!-- 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-50-finetuned-dog-vs-cat

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the cats_vs_dogs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0577
- Accuracy: 0.9919

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3357        | 1.0   | 164  | 0.2255          | 0.9868   |
| 0.1683        | 2.0   | 329  | 0.0577          | 0.9919   |
| 0.1448        | 2.99  | 492  | 0.0460          | 0.9919   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2