Instructions to use ProbeX/Model-J__ResNet__model_idx_0396 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_0396 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_0396") 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_0396") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0396", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 53060077657093a292cb3637be89fc7af10e5d878d42177c68b147aba1e5bf1f
- Size of remote file:
- 171 MB
- SHA256:
- 2104ff6a93a79e2234cafca96f052f47689538062f71b496197c33366fc5c363
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