This is a very short implementation of Faster RCNN using PyTorch.
- Ubuntu 18.04
- Python==3.7.1
- torch==1.3.1
- torchvision==0.4.2
- tqdm==4.38.0
- numpy==1.17.4
- cupy-cuda101==6.5.0
- pycocotools==2.0
- Pillow==6.2.1
- six==1.13.0
You should build the cython code in model/utils/nms/:
cd model/utils/nms/
python build.py build_ext --inplaceYou should prepare COCO dataset following the instructions in COCO website. You should also change the corresponding paths in the code.
You should locate at the root of this project and excute:
python train.pyThe trained models will be saved in checkpoints/.
Due to lack of GPU resources, the model is left untrained.
This code is expected to get a mAP of around 30 on COCO testset.
This work is mainly based on chenyuntc's simple-faster-rcnn-pytorch. Note that codes in model/utils/ are directly taken from simple-faster-rcnn-pytorch/model/utils.
The original paper where Faster RCNN comes from is Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.