Instructions to use ProbeX/Model-J__ResNet__model_idx_0791 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_0791 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_0791") 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_0791") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0791", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d504f1e30a516c188cdc2225f8711a957d3feb1edaab481536781931acd77a7d
- Size of remote file:
- 5.37 kB
- SHA256:
- 16d7b924a149758e169a1fc28ed63bc1e4aba47fbd59d179a82953ee4253352a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.