---
license: apache-2.0
base_model: facebook/convnextv2-nano-22k-384
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: convnext-nano-20ep
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: vuongnhathien/30VNFoods
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8702380952380953
---

<!-- 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. -->

# convnext-nano-20ep

This model is a fine-tuned version of [facebook/convnextv2-nano-22k-384](https://huggingface.co/facebook/convnextv2-nano-22k-384) on the vuongnhathien/30VNFoods dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4812
- Accuracy: 0.8702

## 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: 0.0003
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5831        | 1.0   | 275  | 0.5660          | 0.8278   |
| 0.3159        | 2.0   | 550  | 0.5093          | 0.8529   |
| 0.1892        | 3.0   | 825  | 0.4719          | 0.8779   |
| 0.1111        | 4.0   | 1100 | 0.5067          | 0.8755   |
| 0.0886        | 5.0   | 1375 | 0.5278          | 0.8708   |
| 0.0697        | 6.0   | 1650 | 0.6000          | 0.8628   |
| 0.0396        | 7.0   | 1925 | 0.6158          | 0.8736   |
| 0.0386        | 8.0   | 2200 | 0.6448          | 0.8684   |
| 0.0323        | 9.0   | 2475 | 0.5637          | 0.8915   |
| 0.0157        | 10.0  | 2750 | 0.5845          | 0.8958   |
| 0.0067        | 11.0  | 3025 | 0.5574          | 0.9018   |
| 0.005         | 12.0  | 3300 | 0.5378          | 0.9034   |
| 0.0031        | 13.0  | 3575 | 0.5526          | 0.9014   |
| 0.0023        | 14.0  | 3850 | 0.5419          | 0.9093   |
| 0.0026        | 15.0  | 4125 | 0.5323          | 0.9113   |
| 0.0024        | 16.0  | 4400 | 0.5298          | 0.9117   |
| 0.0019        | 17.0  | 4675 | 0.5323          | 0.9121   |
| 0.002         | 18.0  | 4950 | 0.5315          | 0.9125   |
| 0.0012        | 19.0  | 5225 | 0.5314          | 0.9121   |
| 0.0019        | 20.0  | 5500 | 0.5315          | 0.9117   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2