Instructions to use ProbeX/Model-J__ResNet__model_idx_0695 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_0695 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_0695") 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_0695") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0695", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2a2ee03acb39afc975c582ed9be8ee038859ac5b83f7df0f8897353d2c99be9
- Size of remote file:
- 171 MB
- SHA256:
- e24e23a42f2aea2255a959284c871cc7ff91ea38bb3737177cdfbcbb706afb51
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