An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Paper • 2010.11929 • Published • 21
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("ewanlong/food_type_image_detection")
model = AutoModelForImageClassification.from_pretrained("ewanlong/food_type_image_detection")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Model Card
- Model Name: Food Type Image Detection Vision Transformer
- Original Model: Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224.
- It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository.
- Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded.
- Does not provide any fine-tuned heads, as these were zero'd by Google researchers.
- Model Type: Image Classification
- Model Architecture: Vision Transformer (ViT)
- Fine-tuning:
- Fine-tuned on Food Image Classification Dataset by using 12 varieties of these 35 varieties
- Optimizer: AdamW
- Epochs: 20
- Model Performance: Achieved an accuracy of 96.23% on all of the kinds of Food Image Classification Dataset
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ewanlong/food_type_image_detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")