Instructions to use vuongnhathien/swin-tiny-test-evaluate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vuongnhathien/swin-tiny-test-evaluate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="vuongnhathien/swin-tiny-test-evaluate") 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/swin-tiny-test-evaluate") model = AutoModelForImageClassification.from_pretrained("vuongnhathien/swin-tiny-test-evaluate") - Notebooks
- Google Colab
- Kaggle
swin-tiny-test-evaluate
This model is a fine-tuned version of vuongnhathien/SwinV2-30VNFood on the vuongnhathien/30VNFoods dataset.
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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
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Model tree for vuongnhathien/swin-tiny-test-evaluate
Base model
microsoft/swinv2-tiny-patch4-window16-256 Finetuned
vuongnhathien/SwinV2-30VNFood