Instructions to use ProbeX/Model-J__ResNet__model_idx_0876 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_0876 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_0876") 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_0876") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0876", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a45890385ea14f96ff335a8b5cd6fecd1d43900e4bec9f17beb13fc9d8c2ac2
- Size of remote file:
- 171 MB
- SHA256:
- f029b7b518089c8ffac7cce58ba0ba3c0cf99cef0a9b81b27d9612911778b11f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.