metadata
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window16-256
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: SwinV2-30VNFood
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: 0.8771825396825397
SwinV2-30VNFood
This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window16-256 on the vuongnhathien/30VNFoods dataset. It achieves the following results on the evaluation set:
- Loss: 0.4561
- Accuracy: 0.8772
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: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7587 | 1.0 | 275 | 0.5447 | 0.8477 |
| 0.4341 | 2.0 | 550 | 0.4809 | 0.8640 |
| 0.2737 | 3.0 | 825 | 0.4703 | 0.8763 |
| 0.1704 | 4.0 | 1100 | 0.5040 | 0.8791 |
| 0.1225 | 5.0 | 1375 | 0.4893 | 0.8879 |
| 0.0886 | 6.0 | 1650 | 0.5733 | 0.8863 |
| 0.0568 | 7.0 | 1925 | 0.5986 | 0.8803 |
| 0.0407 | 8.0 | 2200 | 0.5664 | 0.8998 |
| 0.0175 | 9.0 | 2475 | 0.5790 | 0.8998 |
| 0.0175 | 10.0 | 2750 | 0.5754 | 0.9038 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2