Instructions to use ProbeX/Model-J__ResNet__model_idx_0495 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_0495 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_0495") 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_0495") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0495", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 53c8ef4d60bbdf38ce9ad5df102c891e87145a1cf277664ce0cf3a7464daec2a
- Size of remote file:
- 171 MB
- SHA256:
- ff5e838aab4d1ff1788905bd709f28bba1a13914e957bdba56b2e56d4c046242
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