Instructions to use ProbeX/Model-J__ResNet__model_idx_0506 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_0506 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_0506") 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_0506") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0506", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7849ac3b5fa4c80a3cf542028b0e929b2a06ce748dea0d053527d9af9b7ae5ea
- Size of remote file:
- 171 MB
- SHA256:
- bc1b6ad3a682a60b4b19d87ae65c9bad51625f39e0d33006abb11b79d453409c
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