Instructions to use frgfm/darknet19 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frgfm/darknet19 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="frgfm/darknet19") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("frgfm/darknet19", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9120cf155871f82c718e8d3cba4a0124d05d430d466d451e12ad5b444eece8ea
- Size of remote file:
- 79.4 MB
- SHA256:
- b02d469e0841608223cf8d027ca25006fca8406c58b9535d89f38084f537502b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.