Instructions to use ProbeX/Model-J__ResNet__model_idx_0810 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_0810 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_0810") 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_0810") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0810") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4a465e029175772aa0960d7e52b8a46e0b92e6fca742569885fefb06dfca727
- Size of remote file:
- 5.37 kB
- SHA256:
- f0b95f47b942043c197f661d66339b39733d594ecfecc152fc0890e41cf1c7b6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.