---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- clothes-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-clothes-classification
  results: []
---

<!-- 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. -->

# vit-clothes-classification

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the DBQ/Matches.Fashion.Product.prices.France dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2328
- Accuracy: 0.6395

## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.0975        | 0.5714 | 500  | 1.2619          | 0.6111   |
| 0.8315        | 1.1429 | 1000 | 1.3133          | 0.6322   |
| 0.7266        | 1.7143 | 1500 | 1.2077          | 0.6356   |
| 0.5451        | 2.2857 | 2000 | 1.2895          | 0.6556   |
| 0.4287        | 2.8571 | 2500 | 1.2736          | 0.6644   |
| 0.2554        | 3.4286 | 3000 | 1.3801          | 0.6767   |
| 0.2265        | 4.0    | 3500 | 1.4924          | 0.6656   |
| 0.0738        | 4.5714 | 4000 | 1.6321          | 0.68     |
| 0.0761        | 5.1429 | 4500 | 1.6676          | 0.6767   |
| 0.0251        | 5.7143 | 5000 | 1.6911          | 0.7056   |
| 0.0147        | 6.2857 | 5500 | 1.7312          | 0.7      |
| 0.0051        | 6.8571 | 6000 | 1.7282          | 0.6922   |
| 0.0028        | 7.4286 | 6500 | 1.7679          | 0.6967   |
| 0.0017        | 8.0    | 7000 | 1.7642          | 0.6989   |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1