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PROB: Probabilistic Objectness for Open World Object Detection (CVPR 2023)

paper arXiv website video

Orr Zohar, Jackson Wang, Serena Yeung

📰 News

• [2024.01.05] ⏭️ Check out my new OWOD paper, where I attempt to integrate foundation models into the OWOD objective!
• [2023.06.18] 🤝 Presenting at CVPR - come check out our poster, and discuss the future of OWOD.
• [2023.02.27] 🚀 PROB was accepted to CVPR 2023!
• [2022.12.02] First published on arXiv.

Certainly! Here's a more concise version of your "Highlights" section:

🔥 Highlights

• Open World Object Detection (OWOD): A new computer vision task that extends traditional object detection to include both seen and unknown objects, aligning more with real-world scenarios.
• Challenges with Standard OD: Traditional methods inadequately classify unknown objects as background, failing in OWOD contexts.
• Novel Probabilistic Framework: Introduces a method for estimating objectness in embedded feature space, enhancing the identification of unknown objects.
• PROB: A Transformer-Based Detector: A new model that adapts existing OD models for OWOD, significantly improving unknown object detection.
• Superior Performance: PROB outperforms existing OWOD methods, doubling the recall for unknown objects and increasing known object detection mAP by 10%.

Overview

PROB adapts the Deformable DETR model by adding the proposed 'probabilistic objectness' head. In training, we alternate between distribution estimation (top right) and objectness likelihood maximization of matched ground-truth objects (top left). For inference, the objectness probability multiplies the classification probabilities. For more, see the manuscript.

📊 Results

🛠️ Requirements and Installation

Python Environment

We have trained and tested our models on Ubuntu 16.04, CUDA 11.1/11.3, GCC 5.4.0, Python 3.10.4

conda create --name prob python==3.10.4
conda activate prob
pip install -r requirements.txt
pip install torch==1.12.0+cu113 torchvision==0.13.0+cu113 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu113

Backbone features

Download the self-supervised backbone from here and add in models folder.

Compiling CUDA operators

cd ./models/ops
sh ./make.sh
# unit test (should see all checking is True)
python test.py

Dataset Preparation

The file structure:

PROB/
└── data/
    └── OWOD/
        ├── JPEGImages
        ├── Annotations
        └── ImageSets
            ├── OWDETR
            ├── TOWOD
            └── VOC2007

The splits are present inside data/OWOD/ImageSets/ folder.

1. Download the COCO Images and Annotations from coco dataset into the data/ directory.
2. Unzip train2017 and val2017 folder. The current directory structure should look like:

PROB/
└── data/
    └── coco/
        ├── annotations/
        ├── train2017/
        └── val2017/

4. Move all images from train2017/ and val2017/ to JPEGImages folder.
5. Use the code coco2voc.py for converting json annotations to xml files.
6. Download the PASCAL VOC 2007 & 2012 Images and Annotations from pascal dataset into the data/ directory.
7. untar the trainval 2007 and 2012 and test 2007 folders.
8. Move all the images to JPEGImages folder and annotations to Annotations folder.

Currently, we follow the VOC format for data loading and evaluation

🤖 Training

Training on single node

To train PROB on a single node with 4 GPUS, run

bash ./run.sh

**note: you may need to give permissions to the .sh files under the 'configs' and 'tools' directories by running chmod +x *.sh in each directory.

By editing the run.sh file, you can decide to run each one of the configurations defined in \configs:

1. EVAL_M_OWOD_BENCHMARK.sh - evaluation of tasks 1-4 on the MOWOD Benchmark.
2. EVAL_S_OWOD_BENCHMARK.sh - evaluation of tasks 1-4 on the SOWOD Benchmark.
3. M_OWOD_BENCHMARK.sh - training for tasks 1-4 on the MOWOD Benchmark.
4. M_OWOD_BENCHMARK_RANDOM_IL.sh - training for tasks 1-4 on the MOWOD Benchmark with random exemplar selection.
5. S_OWOD_BENCHMARK.sh - training for tasks 1-4 on the SOWOD Benchmark.

Training on slurm cluster

To train PROB on a slurm cluster having 2 nodes with 8 GPUS each (not tested), run

bash run_slurm.sh

**note: you may need to give permissions to the .sh files under the 'configs' and 'tools' directories by running chmod +x *.sh in each directory.

Hyperparameters for different systems

📈 Evaluation

For reproducing any of the aforementioned results, please download our weights and place them in the 'exps' directory. Run the run_eval.sh file to utilize multiple GPUs.

**note: you may need to give permissions to the .sh files under the 'configs' and 'tools' directories by running chmod +x *.sh in each directory.

PROB/
└── exps/
    ├── MOWODB/
    |   └── PROB/ (t1.ph - t4.ph)
    └── SOWODB/
        └── PROB/ (t1.ph - t4.ph)

Note: Please check the Deformable DETR repository for more training and evaluation details.

✏️ Citation

If you use PROB, please consider citing:

@InProceedings{Zohar_2023_CVPR,
    author    = {Zohar, Orr and Wang, Kuan-Chieh and Yeung, Serena},
    title     = {PROB: Probabilistic Objectness for Open World Object Detection},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {11444-11453}
}

📧 Contact

Should you have any questions, please contact :e-mail: orrzohar@stanford.edu

👍 Acknowledgements

PROB builds on previous works' code bases such as OW-DETR, Deformable DETR, Detreg, and OWOD. If you found PROB useful please consider citing these works as well.

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