Instructions to use ProbeX/Model-J__ResNet__model_idx_0801 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_0801 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_0801") 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_0801") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0801", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fbf0145d7f0d7061a3ac8751c873406b160e6c7f37ef0bdf1d7d8e2d9402981b
- Size of remote file:
- 171 MB
- SHA256:
- 19cba8eed6dc6688949a56be24cca4bcd38c5fdd66f198e667228dae931ccdb0
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