Instructions to use ProbeX/Model-J__ResNet__model_idx_0218 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_0218 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_0218") 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_0218") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0218") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4730bd077e196e4b653981a5f85d4c94af85a3c4659551651232d3fd113c9933
- Size of remote file:
- 5.37 kB
- SHA256:
- 6ffd2ecb4d934ad1275c7b1377309a8e77258edd23aeaafbf4b6bda51b31ecd7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.