Instructions to use ProbeX/Model-J__ResNet__model_idx_0344 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_0344 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_0344") 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_0344") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0344", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ab4e673ceb3ad3cd96a05d030aa9ff3e2dc0777e9f7fd8037086d54bba6f2aa9
- Size of remote file:
- 5.37 kB
- SHA256:
- 60d1e1c6b96af7acc6f0930290d501f30a3a7370ea991dda1356f188a70f3295
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.