Instructions to use Omnifact/conditional-detr-resnet-101-dc5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Omnifact/conditional-detr-resnet-101-dc5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Omnifact/conditional-detr-resnet-101-dc5")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Omnifact/conditional-detr-resnet-101-dc5") model = AutoModelForObjectDetection.from_pretrained("Omnifact/conditional-detr-resnet-101-dc5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dee0962e766a8454ac36b239f1cd6a55ef72a2283fda949b8b59a89b9c4d404b
- Size of remote file:
- 251 MB
- SHA256:
- 559a624d3f4719d72c3517ea553216bbbed7ac4cb4eb62789de7a2a46afaaead
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