Instructions to use dima806/shoe_types_image_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/shoe_types_image_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/shoe_types_image_detection") 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("dima806/shoe_types_image_detection") model = AutoModelForImageClassification.from_pretrained("dima806/shoe_types_image_detection") - Notebooks
- Google Colab
- Kaggle
Return shoe type given an image.
See https://www.kaggle.com/code/dima806/shoe-type-image-detection-vit for more details.
Classification report:
precision recall f1-score support
Clog 0.9748 0.9598 0.9672 1169
Brogue 0.9804 0.9812 0.9808 1170
Sneaker 0.9718 0.9735 0.9727 1170
Boat 0.9642 0.9658 0.9650 1170
Ballet Flat 0.9729 0.9837 0.9783 1169
accuracy 0.9728 5848
macro avg 0.9728 0.9728 0.9728 5848
weighted avg 0.9728 0.9728 0.9728 5848
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Model tree for dima806/shoe_types_image_detection
Base model
google/vit-base-patch16-224-in21k