Instructions to use ProbeX/Model-J__ResNet__model_idx_0218 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_0218 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_0218") 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_0218") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0218", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 70e631fb1e6f12167b49cb23f3ff0ef74672cacda2f3947e2132c6db5acef7e9
- Size of remote file:
- 171 MB
- SHA256:
- 74840a6c40c3c59acea414c919690994142d61184091d1db1bdd38668caa5d6d
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