Instructions to use ProbeX/Model-J__ResNet__model_idx_0367 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_0367 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_0367") 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_0367") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0367", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 586487549b402facc3aa1c951dac9d6260f66ee7b92bfc63b8a838dbd870c1d8
- Size of remote file:
- 171 MB
- SHA256:
- 236d18fd50984efb25180ca7abf50e43a12188f76d43efdf711fcb4c39640075
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