DASS_Detector

• Description: Original Training Repository of the Paper: "Domain-Adaptive Self-Supervised Pre-training for Face & Body Detection in Drawings"

• Disclaimer: The model structure and the codes are highly adopted from the YOLOX model.

• Note: If you only need inference / evaluation modules, then please visit the inference repository of this study.

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Requirements

• CUDA >= 10.2
• PyTorch >= 1.8.2
• Chainer >= 7.8.1
• ChainerCV >= 0.13.1
• OpenCV-Python >= 4.5.5
• Matplotlib >= 3.3.4
• xmltodict

Abstract

Drawings are powerful means of pictorial abstraction and communication. Understanding diverse forms of drawings including digital arts, cartoons, and comics has been a major problem of interest for the computer vision and computer graphics communities. Although there are large amounts of digitized drawings from comic books and cartoons, they contain vast stylistic variations, which necessitate expensive manual labeling for training domain-specific recognizers. In this work, we show how self-supervised learning, based on a teacher-student network with a modified student network update design, can be used to build face and body detectors. Our setup allows exploiting large amounts of unlabeled data from the target domain when labels are provided for only a small subset of it. We further demonstrate that style transfer can be incorporated into our learning pipeline to bootstrap detectors using vast amount of out-of-domain labeled images from natural images (i.e., images from the real world). Our combined architecture yields detectors with state-of-the-art (SOTA) and near-SOTA performance using minimal annotation effort.

Pre-trained Weights

You can find all the pre-trained model weights from here. Please note that:

• if the model name includes xs, then the depth and width parameters should be set as depth, width = 0.33, 0.375. If it includes xl, then depth, width = 1.33, 1.25.
• for the stage-2 weights (i.e., self-supervised, teacher-student), load the model with the teacher_model key in the weight dictionary. Otherwise use model key.

Model Architecture

Overall Pipeline

Model Architecture

Self-Supervised Design

Results

The results shared below are calculated by averaging 5 separate training run outcomes for the XS sized models. For XL sized, results of a single run is given. The best-performing models among these runs are given as the pre-trained weights. Please refer to the original paper for the complete set of results and ablation studies.

Face Results

Models	iCartoonFace	Manga109	DCM772
XS Stage-1	42.50	54.74	69.93
XS Stage-2	49.19	69.25	82.45
XS Stage-3 w/ Single Datasets	87.75	87.86	75.87
XS Stage-3 w/ Mix of Datasets	83.15	86.45	78.40
XL Stage-3 w/ Single Datasets	90.01	87.88	77.40
XL Stage-3 w/ Mix of Datasets	87.77	87.08	85.77
ACFD	90.94	-	-
Ogawa et al.	-	76.20	-
Nguyen et al.	-	-	74.94

Body Results

Models	Manga109	DCM772	Comic2k	Watercolor2k	Clipart1k
XS Stage-1	42.72	65.46	56.80	67.36	55.65
XS Stage-2	69.41	77.83	67.38	71.60	64.12
XS Stage-3 w/ Single Datasets	87.06	84.89	71.66	89.17	77.97
XS Stage-3 w/ Mix of Datasets	86.54	83.52	75.60	82.68	75.96
XL Stage-3 w/ Single Datasets	87.98	86.14	73.65	89.81	83.59
XL Stage-3 w/ Mix of Datasets	87.50	87.24	76.00	84.75	79.63
Ogawa et al.	79.60	-	-	-	-
Nguyen et al.	-	76.76	-	-	-
Inoue et al.	-	-	70.10	77.30	76.20

Training Instructions

Stage-1: Style-transferred Pre-training

Required Datasets:

• WIDER FACE
• COCO - Detection 2020

Required Style-Transferring Models:

• CartoonGAN: Hayao, Hosoda, Paprika, and Shinkai styles
• CycleGAN: Cezanne, Monet, Van Gogh styles
• GANILLA: AS, KH, and Miyazaki styles
• White Box Cartonization: Default style

Steps for Dataset Preparation:

1. Process the complete WIDER FACE and COCO training images with these 11 styles from 4 cartoonization studies. The generated image outputs must be at the same shape with the original input images and must be named with the same name and extension.
2. Instead of the default folder design of these 2 datasets, change that design into the formats below:

# For COCO:

> COCO
    > train2017
        > cartoongan_hayao
            > <images>
        > ...
            > <images>
        >cyclegan_cezanne
            > <images>
        > ...
            > <images>
        > ganilla_AS
            > <images>
        > ...
            > <images>
        > whitebox
            > <images>
        > original # this is where the original COCO images are placed
            > <images>

# For WIDER FACE:

> WIDERFACE
    > WIDER_train
        > images
            > cartoongan_hayao
                > <image_folders>
            > ...
                > <image_folders>
            >cyclegan_cezanne
                > <image_folders>
            > ...
                > <image_folders>
            > ganilla_AS
                > <image_folders>
            > ...
                > <image_folders>
            > whitebox
                > <image_folders>
            > original # this is where the original WIDERFACE images are placed
                > <image_folders>

3. Filter out the COCO images that do not include any person or animal in it. We shared the reading ids and image names of these filtered outputs in here.
4. Filter out the WIDER FACE images that have people that have face side lengths smaller than 1/60 of the minimum side length of the image. Filtered file paths can be found here.
5. From the Base Experiment file, set the correct paths for wf_train_imgs, wf_test_imgs, wf_train_labels, wf_test_labels keys in the self.face_data_dir and coco key in the self.body_data_dir.

Steps for Training:

1. Set augmentation-specific hyper-parameters and epoch count from Styled Experiment file or leave them as default.

2. Run the following command:

> python3 train_single_gpu.py -b <BATCH_SIZE> --fp16 -o -expn <EXPERIMENT_NAME> -ms <EITHER_xs_OR_xl> --head-mode <0_FOR_BOTH_1_FOR_FACE_2_FOR_BODY_ONLY> -cfg allstyled

3. You can check different configuration options from Styled Experiment file.

Stage-2: Self-supervised Teacher-student Pre-training

Required Datasets:

• COMICS Pages
• iCartoonFace
• Manga 109
• Comic 2k / Watercolor 2k / Clipart 1k

Steps for Dataset Preparation:

1. Download all the datasets and leave the file format as it is already.
2. From the Base Experiment file, set the correct paths for icf_train_imgs, icf_test_imgs, icf_train_labels, icf_test_labels, m109, golden_pages, comic, watercolor, clipart keys in the self.face_data_dir and self.body_data_dir.

Steps for Training:

1. Set augmentation-specific and other hyper-parameters from Unsupervised Experiment file or leave them as default.

2. Run the following command:

> python3 train_single_gpu.py -b <BATCH_SIZE> --fp16 -o -expn <EXPERIMENT_NAME> -ms <EITHER_xs_OR_xl> --head-mode <0_FOR_BOTH_1_FOR_FACE_2_FOR_BODY_ONLY> -cfg uns_stu50_50 -c <STYLE_TRANSFERRED_WEIGHT_PTH_PATH>

3. You can check different configuration options from Unsupervised Experiment file.

Stage-3: Fine-tuning with Annotated Drawing Data

Required Datasets:

• DCM 772
• iCartoonFace
• Manga 109
• Comic 2k / Watercolor 2k / Clipart 1k

Steps for Dataset Preparation:

1. Download all the datasets and leave the file format as it is already.

2. To increase the sizes of the datasets, we divided Manga 109 and DCM 772 pages to individual panels. We only selected the panels having at least one character. So, process these two datasets for separating the panel images and save the modified annotations.

3. For DCM 772, you can find the panel creator script here.

4. For Manga 109, we do not share any script but we share the folder structure:

##############################
# Folder Structure:
##############################

> manga109_frames
    > imgs
        > <SERIES_NAME>_<PAGE_NUMBER_WITH_3_DIGITS>_<FRAME_NUMBER>.jpg
        > ...
    > annots.json
    
##############################
# Structure of annots.json:
##############################

{
    "<SERIES_NAME>_<PAGE_NUMBER_WITH_3_DIGITS>_<FRAME_NUMBER>.jpg": {
        "face": [[xmin, ymin, xmax, ymax], ...],
        "body": [[xmin, ymin, xmax, ymax], ...],
    },
    ...
}

5. From the Base Experiment file, set the correct paths for icf_train_imgs, icf_test_imgs, icf_train_labels, icf_test_labels, m109_frames_imgs, m109_frames_labels, dcm772_frames_imgs, dcm772_frames_labels, dcm772_frames_partition, comic, watercolor, clipart keys in the self.face_data_dir and self.body_data_dir.

Steps for Mixed Data Training:

1. Set augmentation-specific and other hyper-parameters from Comic Experiment file or leave them as default.

2. Run the following command:

> python3 train_single_gpu.py -b <BATCH_SIZE> --fp16 -o -expn <EXPERIMENT_NAME> -ms <EITHER_xs_OR_xl> --head-mode <0_FOR_BOTH_1_FOR_FACE_2_FOR_BODY_ONLY> -cfg comic -c <SELF_SUPERVISED_WEIGHT_PTH_PATH>

Steps for Single Dataset Training:

1. Set augmentation-specific and other hyper-parameters from Single Dataset Experiment file or leave them as default.

2. Run the following command:

> python3 train_single_gpu.py -b <BATCH_SIZE> --fp16 -o -expn <EXPERIMENT_NAME> -ms <EITHER_xs_OR_xl> --head-mode <0_FOR_BOTH_1_FOR_FACE_2_FOR_BODY_ONLY> -cfg <icartoonface / manga109 / dcm772 / comic2k> -c <SELF_SUPERVISED_WEIGHT_PTH_PATH>

Evaluation Instructions

Required Datasets:

In addition to the datasets in the Stage-3: Fine-tuning with Annotated Drawing Data subsection, you can download eBDtheque and set the correct file path in Base Experiment file.

Steps for Evaluation:

If you retrieved the final weights from your training, please use the inference repository to evaluate your models.

Visual Examples from "XS Stage-3 Fine-Tuned w/ Mix of Datasets"