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:
- 8c570ea55d01a62b7335cecc2df634c1926afb49504a1dab640d406a721b93b0
- Size of remote file:
- 3.12 kB
- SHA256:
- 0dbb99b4e0798ed0833aca8e3b1332eddc82e3ed0f88326b20beabd7f7ec69d9
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