Instructions to use ProbeX/Model-J__ResNet__model_idx_0900 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_0900 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_0900") 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_0900") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0900", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 586b330925148eddcae46a94775b86ac084b827f55f8d48f4ae642a1082e49a9
- Size of remote file:
- 171 MB
- SHA256:
- 83a832bae59a1a8f694f7abdd74bd7903a4a90bccdc7f3470b2d611f2c56786a
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