Instructions to use ProbeX/Model-J__ResNet__model_idx_0180 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_0180 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_0180") 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_0180") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0180", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2d946e6696f8f2556a0cc303a937dacca4b68305e030d435b103d44e703c6190
- Size of remote file:
- 5.37 kB
- SHA256:
- 7853b0f0998330258c61cbae901ca623294e7a23a96f3959489a2d6a2fb713c6
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