Instructions to use ProbeX/Model-J__ResNet__model_idx_0722 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_0722 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_0722") 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_0722") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0722", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a4bf084d48ab713d20ed5ee608c1540b2d7ac13345fca555e6b8fb02312a35af
- Size of remote file:
- 171 MB
- SHA256:
- 173c03faf46e6283c4c92487eff6e52bcf9edd89619cb7207bd86d91ae753249
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