Instructions to use ProbeX/Model-J__ResNet__model_idx_0754 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_0754 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_0754") 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_0754") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0754", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7eac409c86e673f483988f0024aa3303dfd660ed04fbadac59fae4dbb39d9266
- Size of remote file:
- 5.37 kB
- SHA256:
- 06f1424643baaaa30d05e56089b74dfbc29d78222747fb521251d583c4e0e81b
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