Instructions to use ProbeX/Model-J__ResNet__model_idx_0938 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_0938 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_0938") 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_0938") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0938", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c2b1632c41b7a97b42f7acf3de57911ee55d77a0fe3ee2294f56ade029176aee
- Size of remote file:
- 171 MB
- SHA256:
- 84b1bfc8608fc6d90b96191a2befa16ea1e09ae7374ef42d015356c1cfd35b0b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.