Instructions to use ProbeX/Model-J__ResNet__model_idx_0021 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0021 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0021") 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("ProbeX/Model-J__ResNet__model_idx_0021") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0021", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f4da292f0bac6dfc7a66cdddfdaedeeeafe166b94c31826c96996fb7f8be6239
- Size of remote file:
- 5.37 kB
- SHA256:
- 15a58ec49603c64223877c4a5389f4f78b7c4d01af4abb28f34ec486dc4c0f14
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