Instructions to use ProbeX/Model-J__ResNet__model_idx_0002 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_0002 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_0002") 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_0002") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0002", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c87f24d52d2792f9593a5c4f3f33033ff8eb6873d7cf5304297f4be7f482b7e8
- Size of remote file:
- 171 MB
- SHA256:
- a4128fcfe78a4f02aebb7d6853b55c549fcd93f593b9998219d089742b57bb5c
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