Instructions to use machinelearningzuu/queue_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use machinelearningzuu/queue_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="machinelearningzuu/queue_detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("machinelearningzuu/queue_detection") model = AutoModelForObjectDetection.from_pretrained("machinelearningzuu/queue_detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from machinelearningzuu/queue_detection: direct link, hf CLI and curl.
- Browser
- Download file 525 Bytes
-
https://huggingface.co/machinelearningzuu/queue_detection/resolve/49da7599cb59b984e4f0394094d4ccfd170fae1a/preprocessor_config.json
- Command line
-
hf download hf://machinelearningzuu/queue_detection@49da7599cb59b984e4f0394094d4ccfd170fae1a/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/machinelearningzuu/queue_detection/resolve/49da7599cb59b984e4f0394094d4ccfd170fae1a/preprocessor_config.json
525 Bytes
| { | |
| "do_convert_annotations": true, | |
| "do_normalize": true, | |
| "do_pad": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "format": "coco_detection", | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_processor_type": "ConditionalDetrImageProcessor", | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "pad_size": { | |
| "height": 480, | |
| "width": 480 | |
| }, | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "max_height": 480, | |
| "max_width": 480 | |
| } | |
| } | |