How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-classification", model="vuongnhathien/SwinV2-30VNFood")
pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification

processor = AutoImageProcessor.from_pretrained("vuongnhathien/SwinV2-30VNFood")
model = AutoModelForImageClassification.from_pretrained("vuongnhathien/SwinV2-30VNFood", device_map="auto")
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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
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Evaluation results