Instructions to use ProbeX/Model-J__ResNet__model_idx_0799 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_0799 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_0799") 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_0799") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0799", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b70628db8f2e49f4537409bb1a3316966d6aa75d1c94e3c0d7166af8f90d722e
- Size of remote file:
- 171 MB
- SHA256:
- 4b91022ea1739546538afcaf8171d4d3555407a1242c3c39154609977ba2738c
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