Object Detection
Transformers
Safetensors
detr
computer-vision
text-detection
historical-documents
Eval Results (legacy)
Instructions to use harness-race/opencode-r2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harness-race/opencode-r2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="harness-race/opencode-r2")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("harness-race/opencode-r2") model = AutoModelForObjectDetection.from_pretrained("harness-race/opencode-r2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1fa3ed08b6414baa3bc9166bfe8f2969d911758d9b74b1b5ae3228f5c0bc21a0
- Size of remote file:
- 167 MB
- SHA256:
- 8c8eda6d400c953831d35f5c2c1d95fbc10bf5933150bcf16923c072f2571a16
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