Instructions to use ProbeX/Model-J__ResNet__model_idx_0697 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_0697 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_0697") 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_0697") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0697", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b0cb00d21e390699cecf784569959e44e140fd4ccd2cb95cfe96b1cc7c237a7
- Size of remote file:
- 171 MB
- SHA256:
- 20050a17c9ec639f2ea8f54b050262dd99d5873264166de192f83cf75ee6c23d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.