Instructions to use ProbeX/Model-J__ResNet__model_idx_0606 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_0606 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_0606") 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_0606") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0606") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 893a1893099c14509a5a090f7f66fd546b657986788021e3cd1617b2256ad5b5
- Size of remote file:
- 5.37 kB
- SHA256:
- 0381ef676381cd3b2cb907181608709cddf04b8f25b228592ec3917f258fa31b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.