Instructions to use ProbeX/Model-J__ResNet__model_idx_0493 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_0493 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_0493") 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_0493") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0493") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 750e669d5d4797fd10ea8b0bea3cbf317cb6964411277a402e3e2e52c6102ed4
- Size of remote file:
- 5.37 kB
- SHA256:
- 488ba386a64cfdf20ce35a65013a1b280a32232bd2756b745d1a4e558743e4df
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.