Instructions to use ellabettison/Logo-Detection-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ellabettison/Logo-Detection-finetune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="ellabettison/Logo-Detection-finetune")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("ellabettison/Logo-Detection-finetune") model = AutoModelForObjectDetection.from_pretrained("ellabettison/Logo-Detection-finetune", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7bae7ec9404f0a14f6c6c26b11b6b5e507f5f55c831ac0d1c47ca4b784aa9b1f
- Size of remote file:
- 5.37 kB
- SHA256:
- 037592febc032598189be09b205224cb47b0d739b243dbd99d28a2599e10872b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.