🔥[08/07/2024]🚀 the detection code of PODA was release in the detection branch

PODA: Prompt-driven Zero-shot Domain Adaptation

Mohammad Fahes¹, Tuan-Hung Vu^1,2, Andrei Bursuc^1,2, Patrick Pérez^1,2, Raoul de Charette^1
1 Inria, Paris, France.

² valeo.ai, Paris, France.

TL; DR: PØDA (or PODA) is a simple feature augmentation method for zero-shot domain adaptation guided by a single textual description of the target domain.

Project page: https://astra-vision.github.io/PODA/
Paper: https://arxiv.org/abs/2212.03241

Citation

@InProceedings{fahes2023poda,
  title={P{\O}DA: Prompt-driven Zero-shot Domain Adaptation},
  author={Fahes, Mohammad and Vu, Tuan-Hung and Bursuc, Andrei and P{\'e}rez, Patrick and de Charette, Raoul},
  booktitle={ICCV},
  year={2023}
}

Overview

Overview of PØDA

Method

We propose Prompt-driven Instance Normalization (PIN) to augment feature styles based on "feature/target domain description" similarity

Teaser

Test on unseen youtube video of night driving:
Training dataset: Cityscapes
Prompt: "driving at night"

Table of Content

• News
• Installation
  • Dependencies
  • Datasets
  • Source models
• Running PODA
  • Source training
  • Feature optimization
  • Model adaptation
  • Evaluation
• Inference & Visualization
• Qualitative Results
• PODA for Object Detection
• License
• Acknowledgement

News

• 29/11/2023: Check out our recent work A Simple Recipe for Language-guided Domain Generalized Segmentation.
• 19/08/2023: Camera-ready version is on arxiv.
• 14/07/2023: PODA is accepted at ICCV 2023.

Installation

Dependencies

First create a new conda environment with the required packages:

conda env create --file environment.yml

Then activate environment using:

conda activate poda_env

Datasets

• CITYSCAPES: Follow the instructions in Cityscapes to download the images and semantic segmentation ground-truths. Please follow the dataset directory structure:

  <CITYSCAPES_DIR>/             % Cityscapes dataset root
  ├── leftImg8bit/              % input image (leftImg8bit_trainvaltest.zip)
  └── gtFine/                   % semantic segmentation labels (gtFine_trainvaltest.zip)

• ACDC: Download ACDC images and ground truths from ACDC. Please follow the dataset directory structure:

  <ACDC_DIR>/                   % ACDC dataset root
  ├── rbg_anon/                 % input image (rgb_anon_trainvaltest.zip)
  └── gt/                       % semantic segmentation labels (gt_trainval.zip)

• GTA5: Download GTA5 images and ground truths from GTA5. Please follow the dataset directory structure:

  <GTA5_DIR>/                   % GTA5 dataset root
  ├── images/                   % input image 
  └── labels/                   % semantic segmentation labels

Source models

The source models are available here.

Running PODA

Source training

python3 main.py \
  --dataset <source_dataset> \
  --data_root <path_to_source_dataset> \
  --data_aug \
  --lr 0.1 \
  --crop_size 768 \
  --batch_size 2 \
  --freeze_BB \
  --ckpts_path saved_ckpts

Feature optimization

python3 PIN_aug.py \
--dataset <source_dataset> \
--data_root <path_to_source_dataset> \
--total_it 100 \
--resize_feat \
--domain_desc <target_domain_description>  \
--save_dir <directory_for_saved_statistics>

Model adaptation

python3 main.py \
--dataset <source_dataset> \
--data_root <path_to_source_dataset> \
--ckpt <path_to_source_checkpoint> \
--batch_size 8 \
--lr 0.01 \
--ckpts_path adapted \
--freeze_BB \
--train_aug \
--total_itrs 2000 \ 
--path_mu_sig <path_to_augmented_statistics>

Evaluation

python3 main.py \
--dataset <dataset_name> \
--data_root <dataset_path> \
--ckpt <path_to_tested_model> \
--test_only \
--val_batch_size 1 \
--ACDC_sub <ACDC_subset_if_tested_on_ACDC>   

Inference & Visualization

To test any model on any image and visualize the output, please add the images to predict_test directory and run:

python3 predict.py \
--ckpt <ckpt_path> \
--save_val_results_to <directory_for_saved_output_images>

Qualitative Results

PØDA for uncommon driving situations

PODA for Object Detection

Our feature augmentation is task-agnostic, as it operates on the feature extractor's level. We show some results of PØDA for object detection. The metric is mAP%

❗❗❗ [08/07/2024] For the Night-Clear and Day-Foggy results, we corrected the evaluation bug from the original paper where the test split was mistakenly used instead of the train split for testing.

License

PØDA is released under the Apache 2.0 license.

Acknowledgement

The code heavily borrows from this implementation of DeepLabv3+, and uses code from CLIP

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