Instructions to use ProbeX/Model-J__ResNet__model_idx_0702 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_0702 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_0702") 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_0702") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0702") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 04eefdc22f243c063da1e5fda1fceb92ba69b7ee7d624aa9ae90bf7bb2dd5d4c
- Size of remote file:
- 5.37 kB
- SHA256:
- a4b9b70c62b4bc87d563881754bbfa65612e436ccce5166085d2730032b99733
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