Instructions to use ProbeX/Model-J__ResNet__model_idx_0409 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_0409 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_0409") 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_0409") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0409", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f21e42a9c461b55b9c9ca0b5a0fc2865714a3a6535372b6da1785c1b883458c4
- Size of remote file:
- 5.37 kB
- SHA256:
- 696795e573bade872c240a05d97d6398631dd562defc6887ccc300f719537b9b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.