Instructions to use ProbeX/Model-J__ResNet__model_idx_0752 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_0752 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_0752") 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_0752") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0752", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb18f68e229193fe304b3110ff56e8b39047c066681d66dc38fee8f23aa9dfee
- Size of remote file:
- 5.37 kB
- SHA256:
- 2138b10a8bf58941e6dc753d88482a29c5014cfc80bcbec19374264a03d83d3f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.