Instructions to use ProbeX/Model-J__ResNet__model_idx_0886 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_0886 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_0886") 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_0886") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0886", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 22498f2227d2e249c6dca2985396a6e7fe7f2c8be6021a16c97840cc0aa8ad38
- Size of remote file:
- 5.37 kB
- SHA256:
- 4bd87cc5f18f0614d9c8ec822a276d6edd8aecea1c798d1e8b11a37e75ddd6fc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.