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Python 2.7.+
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TensorFlow v1.+
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OpenCV
- Clone the repository
git clone https://github.com/HCMUS-Smart-Environment-Group/small-vehicle-detector- Update your -arch in setup script to match your GPU
cd tf-faster-rcnn/lib
# Change the GPU architecture (-arch) if necessary
vim setup.py| GPU model | Architecture |
|---|---|
| TitanX (Maxwell/Pascal) | sm_52 |
| GTX 960M | sm_50 |
| GTX 1080 (Ti) | sm_61 |
| Grid K520 (AWS g2.2xlarge) | sm_30 |
| Tesla K80 (AWS p2.xlarge) | sm_37 |
Note: You are welcome to contribute the settings on your end if you have made the code work properly on other GPUs. Also even if you are only using CPU tensorflow, GPU based code (for NMS) will be used by default, so please set USE_GPU_NMS False to get the correct output.
- Build the Cython modules
make clean
make
cd ..- Install the Python COCO API. The code requires the API to access COCO dataset.
cd data
git clone https://github.com/pdollar/coco.git
cd coco/PythonAPI
make
cd ../../..Request Access is required. Contact email [email protected]
Create mall-vehicle-detector/output/res101 folder
After extracting file, copy those files to small-vehicle-detector/output/res101
python tools/demo.py