Instructions to use ProbeX/Model-J__ResNet__model_idx_0652 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_0652 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_0652") 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_0652") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0652", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 547827af7e21bd7300698e5697b306212552bd82284b0768c3bfb61f89ab7157
- Size of remote file:
- 5.37 kB
- SHA256:
- 353819f2893cb0b8696d02e517137e622cd0fdf3063ce9cc43e1357a397090dc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.