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harness-race
/
pi-r2

Object Detection
Transformers
Safetensors
PyTorch
detr
vision
document-layout-analysis
Model card Files Files and versions
xet
Community

Instructions to use harness-race/pi-r2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use harness-race/pi-r2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("object-detection", model="harness-race/pi-r2")
    # Load model directly
    from transformers import AutoImageProcessor, AutoModelForObjectDetection
    
    processor = AutoImageProcessor.from_pretrained("harness-race/pi-r2")
    model = AutoModelForObjectDetection.from_pretrained("harness-race/pi-r2", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
pi-r2
172 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
davanstrien's picture
davanstrien HF Staff
validation sample predictions
4823c84 verified 1 day ago
  • figures
    validation sample predictions 1 day ago
  • .gitattributes
    1.69 kB
    validation sample predictions 1 day ago
  • README.md
    3.5 kB
    Train DETR-R50 on loc_beyond_words: final model + eval metrics 1 day ago
  • config.json
    1.65 kB
    Train DETR-R50 on loc_beyond_words: final model + eval metrics 1 day ago
  • model.safetensors
    167 MB
    xet
    Train DETR-R50 on loc_beyond_words: final model + eval metrics 1 day ago
  • preprocessor_config.json
    454 Bytes
    Train DETR-R50 on loc_beyond_words: final model + eval metrics 1 day ago
  • requirements.txt
    143 Bytes
    Train DETR-R50 on loc_beyond_words: final model + eval metrics 1 day ago
  • train_detr.py
    24.4 kB
    Train DETR-R50 on loc_beyond_words: final model + eval metrics 1 day ago
  • val_metrics.json
    486 Bytes
    Train DETR-R50 on loc_beyond_words: final model + eval metrics 1 day ago