Instructions to use ProbeX/Model-J__ResNet__model_idx_0049 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_0049 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_0049") 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_0049") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0049", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ce60bd4fa653f60cb49db6d06619984f32680eff213346217eb128641abda620
- Size of remote file:
- 171 MB
- SHA256:
- f52cae813c851f9b3e1365c28b6c494e5d9a72cf376a38536b62151589dc1c2b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.