Instructions to use ProbeX/Model-J__ResNet__model_idx_0499 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_0499 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_0499") 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_0499") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0499", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 74e8a430cddee44fce166001f3e7663ab528b3e03d5411959439b1b2a1c1b077
- Size of remote file:
- 171 MB
- SHA256:
- 78905c229a668196bdd58a76c86c3cc047f2b38e7396d3822973f083cb806a21
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.