Instructions to use ProbeX/Model-J__ResNet__model_idx_0010 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_0010 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_0010") 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_0010") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0010") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2da01dcadba43747b7c935ef612b8d86ea63c41ffa0a7bd2899b2c7a8c16f861
- Size of remote file:
- 171 MB
- SHA256:
- 3a065876941716170829b13eef1be13a2b1f2ac12cda2e91bf88f51df2275868
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