Instructions to use ProbeX/Model-J__ResNet__model_idx_0510 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_0510 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_0510") 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_0510") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0510", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4379d813aa494adde34dda51987a664d2b503637b84db705ad9a546c26b77065
- Size of remote file:
- 5.37 kB
- SHA256:
- 60dd26ce473cb0b380c5496f8608124908f4367dbab8226c5729ba17e31e60fc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.