Instructions to use ProbeX/Model-J__ResNet__model_idx_0435 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_0435 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_0435") 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_0435") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0435", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c8aecd813317f106b720a348e84064938ffd23aed1d00d64ce18696d21f0f78f
- Size of remote file:
- 5.37 kB
- SHA256:
- 569f3f25fc7d659ce60d2b8f5f5e08ea752b5eebfee2d6f1dbd4346e2e15d374
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.