Instructions to use mrm8488/data2vec-base-finetuned-imagenet1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/data2vec-base-finetuned-imagenet1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mrm8488/data2vec-base-finetuned-imagenet1k") 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("mrm8488/data2vec-base-finetuned-imagenet1k") model = AutoModelForImageClassification.from_pretrained("mrm8488/data2vec-base-finetuned-imagenet1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f97817ab35e56fde306405599690901dc575ef7fc68f3f01ca6a983a4f8ed368
- Size of remote file:
- 350 MB
- SHA256:
- 8ec32a8c590f346a52a22db9a175c41acb1c4401035a2999172c8c506cdc1356
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