Densely connected Atrous Spatial Pyramid Pooling (DenseASPP), which connects a set of atrous convolutional layers in a dense way. Such that it generates multi-scale features that not only cover a larger scale range, but also cover that scale range densely, without significantly increasing the model size.
pip3 install 'scipy' 'matplotlib' 'pycocotools' 'opencv-python' 'easydict' 'tqdm'
Go to visit COCO official website, then select the COCO dataset you want to download.
Take coco2017 dataset as an example, specify /path/to/coco2017 to your COCO path in later training process, the unzipped dataset path structure sholud look like:
coco2017
├── annotations
│ ├── instances_train2017.json
│ ├── instances_val2017.json
│ └── ...
├── train2017
│ ├── 000000000009.jpg
│ ├── 000000000025.jpg
│ └── ...
├── val2017
│ ├── 000000000139.jpg
│ ├── 000000000285.jpg
│ └── ...
├── train2017.txt
├── val2017.txt
└── ...bash train_denseaspp_r50_dist.sh --data-path /path/to/coco2017/ --dataset cocoRef: https://github.com/LikeLy-Journey/SegmenTron Ref: torchvision