Instructions to use ProbeX/Model-J__ResNet__model_idx_0652 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_0652 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_0652") 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_0652") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0652", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a7de1cbb095ebe84d612c439f89d5d5c6ccb54060d34c3cdd270321626c85dd
- Size of remote file:
- 171 MB
- SHA256:
- 09854410b34941996cd0d41eefcfb760dfe1482f086dfb92cd047f715d871bef
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.