Instructions to use hf-tiny-model-private/tiny-random-CvtForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-CvtForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/tiny-random-CvtForImageClassification") 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("hf-tiny-model-private/tiny-random-CvtForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-CvtForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 96f734d5d76a0cffd73ddd94d07827058dd5609b774cb5a1b847a2620b12e5a4
- Size of remote file:
- 5.26 MB
- SHA256:
- 4d4b9340eda27c85f50b954855dae07e857c41945a0d21002262850075bd154a
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