Instructions to use ProbeX/Model-J__ResNet__model_idx_0914 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_0914 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_0914") 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_0914") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0914", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df0cdefddbfa94b7b84a176eee9d7ccd23426a91a1e074fc286b188fa5d1ce11
- Size of remote file:
- 171 MB
- SHA256:
- 4222d12a55ee514321afa56fbf8b6a209d28c6931beb6674b2c93b3df9d177a0
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