Instructions to use hf-internal-testing/tiny-random-GroundingDinoForObjectDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-GroundingDinoForObjectDetection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-object-detection", model="hf-internal-testing/tiny-random-GroundingDinoForObjectDetection")# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-GroundingDinoForObjectDetection") model = AutoModelForZeroShotObjectDetection.from_pretrained("hf-internal-testing/tiny-random-GroundingDinoForObjectDetection") - Notebooks
- Google Colab
- Kaggle
File size: 496 Bytes
2c8a762 9416be2 2c8a762 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"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": "GroundingDinoImageProcessor",
"image_std": [
0.229,
0.224,
0.225
],
"pad_size": null,
"processor_class": "GroundingDinoProcessor",
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"width": 800,
"height": 800
}
}
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