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---
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
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