Instructions to use ProbeX/Model-J__ResNet__model_idx_0941 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_0941 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_0941") 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_0941") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0941", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3076f5b3f1dff71924197c124cbc8d33302319fa97569f2f776b022530c287f2
- Size of remote file:
- 5.37 kB
- SHA256:
- c0054b03926eba7ffa58fc0b78d63d81ec5e7e2eca0d325c71248babdebdc611
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