---
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-50
  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: 0.9310344827586207
---

<!-- 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

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: 0.6922
- Accuracy: 0.9310

## 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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log        | 0.9655 | 7    | 0.6922          | 0.9310   |
| 0.6927        | 1.9310 | 14   | 0.6895          | 0.9310   |
| 0.6916        | 2.8966 | 21   | 0.6878          | 0.9310   |
| 0.6916        | 4.0    | 29   | 0.6853          | 0.9310   |
| 0.6899        | 4.9655 | 36   | 0.6839          | 0.9310   |
| 0.6878        | 5.9310 | 43   | 0.6811          | 0.9310   |
| 0.6868        | 6.8966 | 50   | 0.6826          | 0.9310   |
| 0.6868        | 8.0    | 58   | 0.6804          | 0.9310   |
| 0.6864        | 8.9655 | 65   | 0.6801          | 0.9310   |
| 0.686         | 9.6552 | 70   | 0.6800          | 0.9310   |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1