Instructions to use ProbeX/Model-J__ResNet__model_idx_0299 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_0299 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_0299") 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_0299") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0299", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b18a23fb9b4aa7b3703a0e6bb40131cacf63a9d5c379481adf0ad0337af7e1e
- Size of remote file:
- 171 MB
- SHA256:
- 067c8c953d50f626fe8966c0a2fe50ce971f55b689ffa79a9d646ff2be1d9504
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