Instructions to use ProbeX/Model-J__ResNet__model_idx_0230 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_0230 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_0230") 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_0230") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0230", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4111f65d522dd183dccf90da7afeb43cf581088a8f8767ef4a87ed4a10371162
- Size of remote file:
- 5.37 kB
- SHA256:
- eea00de7a30dd4a2165621b65da2c036d9344edfa799d43865bfe2c341ce1ca0
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