Instructions to use ProbeX/Model-J__ResNet__model_idx_0016 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_0016 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_0016") 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_0016") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0016") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a32e37e2525a7c7c070bdb05ee6edfc0030f744bcaa0ff509da8c05c0927122
- Size of remote file:
- 5.37 kB
- SHA256:
- 0cec3452a0d8b4f6579cf38fe4311a123742f3b148fd8a9ac4081539cd3afadb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.