# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("HZhang729/nike_image_classification")
model = AutoModelForImageClassification.from_pretrained("HZhang729/nike_image_classification")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Model Name: Nike Shoes Recognizer
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.
Model Type: Image Classification
Model Architecture: Vision Transformer (ViT)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="HZhang729/nike_image_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")