Instructions to use ProbeX/Model-J__ResNet__model_idx_0782 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_0782 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_0782") 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_0782") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0782") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e1b6846d435e7aa4eb52f4e41356b917fa51a03dc99d10fc21a6609d74e6b26
- Size of remote file:
- 5.37 kB
- SHA256:
- 8a5bc981910b03506329a237d532fa14482d64a8569e8ab57b3c5cce4246e8ed
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.