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Important
This is a strip down version of the original Tapir repository focused on inference.
- Removed the JAX dependencies and the training code.
- Make it easy to run in real-time even with a camera feed.
- Converted tensors from 5D to 4D (only use one frame)
- ⚠️⚠️⚠️ONNX Inference is very slow⚠️⚠️⚠️
Installation
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git clone https://github.com/ibaiGorordo/Tapir-Pytorch-Inference.git
cd Tapir-Pytorch-Inference
pip install -r requirements.txt
- Download model from: https://storage.googleapis.com/dm-tapnet/causal_bootstapir_checkpoint.pt
License
The License of the original model is Apache 2.0: License
ONNX Export
⚠️⚠️⚠️ONNX Inference is very slow⚠️⚠️⚠️
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python onnx_export.py
Arguments:
- –model: Path to the model weights
- –resolution: Input resolution (default: 640)
- –num_points: Number of points (default: 1000)
- –dynamic: Export with dynamic number of points (default: False)
- –num_iters: Number of iterations, use 0 for faster inference, 4 for better results (default: 4)
- –output_dir: Output directory (default: ./)
Examples
https://github.com/user-attachments/assets/457eeb57-9961-4022-9b15-55f1d9dc2260
Video inference:
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python example_video_tracking.py
Webcam inference:
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python example_webcam_tracking.py
References:
- TAPIR Repository: https://github.com/google-deepmind/tapnet/tree/main