Awesome

A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial Images

Accepted for publication at ICIAP 2021.

Introduction

MiB-CD is an Incremental Learning framework tailored for Semantic Segmentation in aerial images. The basic idea is to exploit the arbitrariness of rotations in top-down pictures to introduce a novel regularization that forces models to produce similar feature maps, regardless of the orientation.

Example outputs, from left to right:

RGB input, finetuning, Unbiased CE (MiB) + Contrastive Distillation, MiB, MiB + Contrastive Distillation, ground truth.

Quickstart

Just bring me to the interesting part

Absolutely! The interesting bit is during training, this is a good starting point. Comments and code should be enough to get you started.

How to

The entrypoint for the whole pipeline is the run.py script. The project is using click and pydantic to handle configuration objects with static typing to help with readability and coding inside the IDE. Of course, the order of commands to execute is: prepare, train and test. Specifically:

1. First, you'll need to download the Potsdam dataset from here. There should be some minor folder renaming involved (lowercase names), check saticl/preproc/isprs.py for more details about the folder structure.

2. Install the requirements with pip install -r requirements.txt. I skimmed useless dependencies from it, but never really had time to test it. If I missed some, please let me know, thanks!

3. Run python run.py prepare --src=<your data source> --dst=<your destination> to produce a preprocessed and tiled version of the dataset. The command offers other options, such as channels (RGB, RGBIR) or overlap (112 for instance is a good compromise for Potsdam). Use python run.py prepare --help for more info.

4. Run python run.py train overriding all the required parameters. You can find some examples in the scripts folder. As always, use python run.py train --help for more info. Each script automatically starts the ICL flow, one task at a time, in a loop. You can also manually launch a given task and step, provided that the previous ones are available. Example:

#!/usr/bin/env bash

CUDA=0
PORT=1234
NAME=rgb-mib-cd
DATA_ROOT="<PREPROCESSED FOLDER PATH>"
COMMENT="Retraining with RGB, rot. invariance on both new and old, factor 0.1, flip+rot90"

for STEP in {0..4}
do
    echo "===| Launching step ${STEP}... |==="

    CUDA_VISIBLE_DEVICES=$CUDA accelerate launch --config configs/single-gpu.json --main_process_port $PORT run.py train \
    --data-root $DATA_ROOT \
    --model.encoder=tresnet_m \
    --task.name 6s \
    --task.step $STEP \
    --task.filter-mode=split \
    --model.act=ident \
    --model.norm=iabn_sync \
    --trainer.batch-size=8 \
    --trainer.amp \
    --trainer.patience=25 \
    --optimizer.lr=1e-3 \
    --scheduler.target=cosine \
    --in-channels=3 \
    --aug.factor=0.1 \
    --aug.factor-icl=0.1 \
    --aug.fixed-angles \
    --name=$NAME \
    --comment $COMMENT
done