Instructions to use ProbeX/Model-J__ResNet__model_idx_0750 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_0750 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_0750") 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_0750") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0750", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e019930eb194b6cc61782aca42d66fae79746a172695dec4d5b818a394d170e
- Size of remote file:
- 171 MB
- SHA256:
- 1d8e10f5d6102e4c27f2ab2298a1bf4c2139dde4e0626be8ce461e7ad35285e2
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