Instructions to use ProbeX/Model-J__ResNet__model_idx_0329 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_0329 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_0329") 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_0329") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0329", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 085f6408490b9c917e54470b56477ae1fadf216beea5fc295d79c87a4ac242c4
- Size of remote file:
- 171 MB
- SHA256:
- e059a87e429e4db078c81914262f1e670a03198a27a82040e711b334b347a074
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