Instructions to use AmineAllo/MT-lively-blaze-90 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AmineAllo/MT-lively-blaze-90 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="AmineAllo/MT-lively-blaze-90")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("AmineAllo/MT-lively-blaze-90") model = AutoModelForObjectDetection.from_pretrained("AmineAllo/MT-lively-blaze-90") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 70562c23403ea76290340e05360861efb7963c797c3508dd155a1e5690ab4194
- Size of remote file:
- 115 MB
- SHA256:
- 21acc4b6d5981960f0605920df6d60a83bf373afec3307f9478834f9b406b52e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.