Awesome

Foreground Object Search Dataset FOSD

This is the official repository for the following paper:

Foreground Object Search by Distilling Composite Image Feature [arXiv]<br>

Bo Zhang, Jiacheng Sui, Li Niu<br> Accepted by ICCV 2023.

Our model has been integrated into our image composition toolbox libcom https://github.com/bcmi/libcom. Welcome to visit and try ＼(^▽^)／

Requirements

See requirements.txt for other dependencies.

Data Preparing

Download Open-Images-v6 trainset from Open Images V6 - Download and unzip them. We recommend that you use FiftyOne to download the Open-Images-v6 dataset. After the dataset is downloaded, the data structure of Open-Images-v6 dataset should be as follows.

Open-Images-v6
├── metadata
├── train
│   ├── data
│   │   ├── xxx.jpg
│   │   ├── xxx.jpg
│   │   ...
│   │
│   └── labels
│       └── masks
│       │   ├── 0
│       │       ├── xxx.png
│       │       ├── xxx.png
│       │       ...
│       │   ├── 1
│       │   ...
│       │
│       ├── segmentations.csv
│       ...

Download S-FOSD annotations, R-FOSD annotations and background images of R-FOSD from Baidu disk (code: 3wvf) and save them to the appropriate location under the data directory according to the data structure below.

Generate backgrounds and foregrounds.

python prepare_data/fetch_data.py --open_images_dir <path/to/open/images>

The data structure is like this:

data
├── metadata
│   ├── classes.csv
│   └── category_embeddings.pkl
├── test
│   ├── bg_set1
│   │   ├── xxx.jpg
│   │   ├── xxx.jpg
│   │   ...
│   │
│   ├── bg_set2
│   │   ├── xxx.jpg
│   │   ├── xxx.jpg
│   │   ...
│   │
│   ├── fg
│   │   ├── xxx.jpg
│   │   ├── xxx.jpg
│   │   ...
│   └── labels
│       └── masks
│       │   ├── 0
│       │       ├── xxx.png
│       │       ├── xxx.png
│       │       ...
│       │   ├── 1
│       │   ...
│       │
│       ├── test_set1.json
│       ├── test_set2.json
│       └── segmentations.csv
│
└── train
    ├── bg
    │   ├── xxx.jpg
    │   ├── xxx.jpg
    │   ...
    │
    ├── fg
    │   ├── xxx.jpg
    │   ├── xxx.jpg
    │   ...
    │
    └── labels
        └── masks
        │   ├── 0
        │       ├── xxx.png
        │       ├── xxx.png
        │       ...
        │   ├── 1
        │   ...
        │
        ├── train_sfosd.json
        ├── train_rfosd.json
        ├── category.json
        ├── number_per_category.csv
        └── segmentations.csv

Pretrained Model

We provide the checkpoint (Baidu disk code: 7793) for the evaluation on S-FOSD dataset and checkpoint (Baidu disk code: 6kme) for testing on R-FOSD dataset. By default, we assume that the pretrained model is downloaded and saved to the directory checkpoints.

Testing

Evaluation on S-FOSD Dataset

python evaluate/evaluate.py --testOnSet1

Evaluation on R-FOSD Dataset

python evaluate/evaluate.py --testOnSet2

The evaluation results will be stored to the directory eval_results.

If you want to save top 20 results on R-FOSD, add --saveTop20 parameter. The top 20 results on R-FOSD will be stored to the directory top20 by default.

If you want to save the model's prediction scores on R-FOSD, add --saveScores parameter. The model scores on R-FOSD will be stored to the directory model_scores by default.

Training

Please download the pretrained teacher models from Baidu disk (code: 40a5) and save the model to directory checkpoints/teacher.

To train a new sfosd model, you can simply run:

.train/train_sfosd.sh

Similarly, train a new rfosd model by:

.train/train_rfosd.sh

FOS Score

Our model can be used to evaluate the compatibility between foreground and background in terms of geometry and semantics.

To launch the demo, you can run:

python demo/demo_ui.py

Here are three steps you can take to get a compatibility score for the foreground and the background.

Upload a background image in the left box of the first row
Click the left-top point and the right-bottom point of the bounding box in the right box of the first row
Upload a foreground image in the left box of the second row, then click 'run' button.

Other Resources

License

Both background and foreground images of S-FOSD belong to Open-Images. The background images of R-FOSD are collected from Internet and are licensed under a Creative Commons Attribution 4.0 License.