Instructions to use ProbeX/Model-J__ResNet__model_idx_0892 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_0892 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_0892") 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_0892") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0892", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- abd12a24a64c004b4eb6799c0b6e4a2cf19ba475d81245e366010c65240a4e19
- Size of remote file:
- 171 MB
- SHA256:
- a31d005c5f8501a94a47e2124ed87f5e3127683771ff4b4b30733f7aa4a013f6
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