Instructions to use ProbeX/Model-J__ResNet__model_idx_0997 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_0997 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_0997") 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_0997") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0997", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d3cac981413d73428c40932c76fac359073d6b318f30a57584f31d1bcac56de
- Size of remote file:
- 171 MB
- SHA256:
- 4581ae3011962eafac80cddd8265dd23db1b15ba7f7f9b835ca750e272362b08
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.