Instructions to use ProbeX/Model-J__ResNet__model_idx_0772 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_0772 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_0772") 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_0772") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0772", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0bbb7dbd2c4d86b02f66d2a3a0b8e8295d8fa6452e7ed1f46015ecc17ae9821b
- Size of remote file:
- 171 MB
- SHA256:
- ae758fec2c67e0ac082953ca12f00bcbffa3f7339b147464836c2c9119bdc495
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