Instructions to use ProbeX/Model-J__ResNet__model_idx_0597 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_0597 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_0597") 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_0597") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0597") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6191297c2ef6489a2430b6ac0fec71e7661f18c8bda9f3cf527cccb0ec8098ce
- Size of remote file:
- 5.37 kB
- SHA256:
- 43b886613a2c64820a56467c9020310fc316e7bc9d42f0e587c850605307df5c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.