Instructions to use ProbeX/Model-J__ResNet__model_idx_0200 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_0200 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_0200") 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_0200") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0200", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8fb552f87a0da6fb834798ff714976e946da2b2fcfff79120dd88b52b1925b6e
- Size of remote file:
- 171 MB
- SHA256:
- 4006ee156fea310b85dd93ce1b087972c716aecead6625f2176f958c5bb835db
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