DAOD

In this part, we give the tutorial about domain adaptive object detection (DAOD).

Dataset

Tasks	C2F	C2B	K2C	S2C
Source(Labeled)	Cityscapes	Cityscapes	KITTI	Sim10k
Target(Unlabeled)	Foggy-Cityscapes	BDD100k-Daytime	Cityscapes	Cityscapes

Usage

Training

1. Use labeled data to train a baseline (aka "source only" model)

Before training，please download the pretrained backbone (vgg) to pretrained_model/backbone.

# |---------------------|--------|---------|
# | xonsh train_gpu2.sh | config | dataset |
# |---------------------|--------|---------|
# there are three dataset for DAOD baseline: C2F, K2C, S2C
# Note that C2B share the same "source only" model with C2F
cd examples/train/xonsh
xonsh train_gpu2.sh ./configs/baseline/baseline_uda.py C2F

• In our implementation, we use 2-gpus to train.

• You can also run bash train_baseline_uda.sh in examples/train/bash

• After training, we organize the pretrained baseline to pretrained_model/baseline as follows:

  pretrained_model/
  	└── baseline/
          ├── C2F.pth
          ├── K2C.pth
          └── S2C.pth
  

2. Use labeled data + unlabeled data to train detector

## there are four adaptation tasks: C2F, C2B, K2C, S2C
## C2F and C2B share the same "source only" model
cd examples/train/xonsh
xonsh train_gpu8.sh ./configs/labelmatch/labelmatch_uda.py C2F

• In our implementation, we use 8-gpus to train.
• You can also run bash train_uda.sh in examples/train/bash

Evaluation

# change "data_name" and "checkpoint" in scripts to support different dataset and trained model
cd examples/eval
xonsh eval_uda.sh

Performance

• $\dagger$ is an ideal setting, using the label distribution from unlabeled data annotations.
• mAP: AP50

Normal-to-foggy weather adaptation

C2F	mAP	truck	car	rider	person	train	motor	bicycle	bus
source only	30.9	19.2	47.9	40.8	34.8	7.8	24.2	36.0	36.4
LabelMatch	52.4	42.0	62.2	55.4	45.3	55.1	43.5	51.5	64.1

Small-to-large scale dataset adaptation

C2B	mAP	truck	car	rider	person	train	motor	bicycle	bus
source only	28.7	18.3	50.0	33.3	35.8	/	18.4	27.6	17.0
LabelMatch	38.8	39.4	54.6	37.4	42.9	/	25.7	29.8	41.7
LabelMatch$^\dagger$	44.5	39.8	55.4	44.5	44.8	/	38.6	41.5	47.1

Cross-Camera adaptation & Synthetic-to-Real adaptation

K2C	AP	S2C	AP
source only	42.2	source only	36.5
LabelMatch	51.0	LabelMatch	52.7
LabelMatch$^{\dagger}$	52.2	LabelMatch$^{\dagger}$	53.8