Instructions to use PekingU/rtdetr_v2_r34vd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PekingU/rtdetr_v2_r34vd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="PekingU/rtdetr_v2_r34vd")# Load model directly from transformers import AutoTokenizer, RtDetrV2ForObjectDetection tokenizer = AutoTokenizer.from_pretrained("PekingU/rtdetr_v2_r34vd") model = RtDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r34vd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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score, label = score.item(), label_id.item()
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box = [round(i, 2) for i in box.tolist()]
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print(f"{model.config.id2label[label]}: {score:.2f} {box}")
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cat: 0.97 [341.14, 25.11, 639.98, 372.89]
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cat: 0.96 [12.78, 56.35, 317.67, 471.34]
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remote: 0.95 [39.96, 73.12, 175.65, 117.44]
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score, label = score.item(), label_id.item()
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box = [round(i, 2) for i in box.tolist()]
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print(f"{model.config.id2label[label]}: {score:.2f} {box}")
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```
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```
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cat: 0.97 [341.14, 25.11, 639.98, 372.89]
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cat: 0.96 [12.78, 56.35, 317.67, 471.34]
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remote: 0.95 [39.96, 73.12, 175.65, 117.44]
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