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PyraBiNet

PyraBiNet is an innovative hybrid model optimized for lightweight semantic segmentation tasks. This model ingeniously merges the merits of Convolutional Neural Networks (CNNs) and Transformers.

Data Preparation

  1. Download and extract ImageNet train and val images from http://image-net.org/. The directory structure is the standard layout for the datasets. ImageFolder and the training and validation data are expected to be in the train/ folder and val folder respectively: /path/to/imagenet/ train/

    class1/

    img1.jpeg

    class2/

    img2.jpeg

    /path/to/imagenet/ val/

    class1/

    img3.jpeg

    class/2

    img4.jpeg

  2. Download the DOS dataset, organized in the following format:

├── data

│ ├── coco_trash

│ │ ├── images

│ │ │ ├── train

│ │ │ │ ├── xxx{img_suffix}

│ │ │ │ ├── yyy{img_suffix}

│ │ │ │ ├── zzz{img_suffix}

│ │ │ ├── val

│ │ ├── annotations

│ │ │ ├── train

│ │ │ │ ├── xxx{seg_map_suffix}

│ │ │ │ ├── yyy{seg_map_suffix}

│ │ │ │ ├── zzz{seg_map_suffix}

│ │ │ ├── val

Download

The DOS dataset can be downloaded from: DOS DATASET

Installation

Our project has been tested on torch=1.7.0 torchvision=0.8.1 timm=0.3.2

Pre-Training

Train 300epochs on the ImageNet dataset with 8 GPUs:

sh dist_train.sh configs/sem_fpn/Pyrabinet/fpn_pyrabinet_ade20k_80k.py 8 --data-path /path/to/imagenet

Training

Use 4 gpus to train pyrabinet+Semantic FPN on the ADE20K dataset, run:

dist_train.sh configs/sem_fpn/Pyrabinet/fpn_pyrabinet_ade20k_80k.py 4

Evaluation

dist_test.sh configs/sem_fpn/Pyrabinet/fpn_pyrabinet_ade20k_80k.py /path/to/checkpoint_file 4 --out results.pkl --eval mIoU

Calculating FLOPS & Params

python flops.py