Instructions to use ilyesdjerfaf/vit-base-patch16-224-in21k-quickdraw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ilyesdjerfaf/vit-base-patch16-224-in21k-quickdraw with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ilyesdjerfaf/vit-base-patch16-224-in21k-quickdraw") 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("ilyesdjerfaf/vit-base-patch16-224-in21k-quickdraw") model = AutoModelForImageClassification.from_pretrained("ilyesdjerfaf/vit-base-patch16-224-in21k-quickdraw") - Notebooks
- Google Colab
- Kaggle
Model Details
Model Description
This model was fine-tuned on a sample of the quickdraw dataset (20 labels with 250 images each)
- Developed by: Ilan Aliouchouche & Ilyes Djerfaf
- Finetuned from model: google/vit-base-patch16-224-in21k
Model Sources
- Repository: https://github.com/mlengineershub/QuickDraw-ML
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