Instructions to use ProbeX/Model-J__ResNet__model_idx_0598 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_0598 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_0598") 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_0598") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0598") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e58259574c4ef7f44699805df70776c7274ef1b58e42998291090c183eeeca38
- Size of remote file:
- 5.37 kB
- SHA256:
- ebd32c0fd609d84a33798d662e170e5873e893000b58716864983fb1f36f7dd4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.