Instructions to use ProbeX/Model-J__ResNet__model_idx_0344 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_0344 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_0344") 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_0344") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0344", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0ad7c46ad227b84c3c22bafc476dd90391b2e86f67fe30c0019eb1af08373529
- Size of remote file:
- 171 MB
- SHA256:
- 6116a50b82a84f22a45194b9d4b17eccabd15db4aa16aaed029717efb76fb3df
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.