Instructions to use ProbeX/Model-J__ResNet__model_idx_0099 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_0099 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_0099") 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_0099") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0099", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e35bc42ac1e8de321cb390c2e8466a2e259be20cd46165bd78ef42da9c97f523
- Size of remote file:
- 5.37 kB
- SHA256:
- ed8d3b291facb6aea312c51f1835f9f63f0ef9fa48563ac5542dd817757b6010
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