Instructions to use ProbeX/Model-J__ResNet__model_idx_0331 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_0331 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_0331") 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_0331") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0331") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f68f63186b0b783f3a50a28c3e099d41108796b1d84a61d6ce841e2be88d3b91
- Size of remote file:
- 5.37 kB
- SHA256:
- a293ec600a16787f2ec60ca2f0c5017c068897fb23b4a0bd5910f42fbe75ef4c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.