Instructions to use ProbeX/Model-J__ResNet__model_idx_0773 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_0773 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_0773") 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_0773") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0773", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 78d4f4fe1b46693b6444ff3b4005bf0569c302875ef5ba45a187c016b2ba52b2
- Size of remote file:
- 171 MB
- SHA256:
- 9bfc51ec25980e58336b1e2db1295e3fcac0f3d0d6779153ba3a218513d4dc86
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.