Instructions to use ProbeX/Model-J__ResNet__model_idx_0285 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_0285 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_0285") 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_0285") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0285", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- db65c3dc168b8ee4d48c15253df0a630f9365ce6bd4960a372c46a6b35c8f9d9
- Size of remote file:
- 5.37 kB
- SHA256:
- 50c738e31ec41ca75e066f079be3f03676c8d37c63f476de98c5da4245603648
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