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") - Notebooks
- Google Colab
- Kaggle
[Clean-up] Planned removal of the `max_size` argument
#2
by HichTala - opened
- Replace deprecated
"feature_extractor_type": "ConditionalDetrFeatureExtractor"with"image_processor_type": "ConditionalDetrImageProcessor" - Remove deprecated
max_sizeand replacesizewith aDict