Instructions to use ProbeX/Model-J__ResNet__model_idx_0937 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_0937 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_0937") 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_0937") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0937", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c620e641b4b2f6dcb446fab7cd32082d857a695f1ae83ad4a7f26cf29ea3930a
- Size of remote file:
- 171 MB
- SHA256:
- e348555e2f0da3ec137d0e9abfc34d066ccccf4531a1eb30790bd7001873d967
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