Instructions to use ProbeX/Model-J__ResNet__model_idx_0149 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_0149 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_0149") 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_0149") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0149", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5c1c2522ad444c3e59ed226562431983bf783bd9b931f8f1c3ffe8b696370c6c
- Size of remote file:
- 5.37 kB
- SHA256:
- e46a1e49e21d69b02990c248617fa605f5c61ece60c08fbc88f71bf1c990de05
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