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
- preprocessor_config.json +18 -16
preprocessor_config.json
CHANGED
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@@ -1,18 +1,20 @@
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{
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"
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}
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{
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"do_normalize": true,
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"do_resize": true,
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"image_processor_type": "ConditionalDetrImageProcessor",
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"format": "coco_detection",
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"image_mean": [
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0.485,
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0.456,
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0.406
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],
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"image_std": [
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0.229,
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0.224,
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0.225
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],
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"size": {
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"longest_edge": 1333,
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"shortest_edge": 800
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}
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}
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