Instructions to use ProbeX/Model-J__ResNet__model_idx_0481 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_0481 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_0481") 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_0481") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0481", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 856d91e4d69ed7006a24a8a79f9bad201ed5d7f4e457b23a2498ec1a9aeb9fc8
- Size of remote file:
- 5.37 kB
- SHA256:
- b1f176d58ffbe525a6b65cbba05e6d0e1c1c2b08e28f4dc166002b3ba17fef35
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