diff --git a/configs/wider_face/README.md b/configs/wider_face/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..f7392007a1ce6379aee4c5e4544111f8207fe823
--- /dev/null
+++ b/configs/wider_face/README.md
@@ -0,0 +1,32 @@
+## WIDER Face Dataset
+
+To use the WIDER Face dataset you need to download it
+and extract to the `data/WIDERFace` folder. Annotation in the VOC format
+can be found in this [repo](https://github.com/sovrasov/wider-face-pascal-voc-annotations.git).
+You should move the annotation files from `WIDER_train_annotations` and `WIDER_val_annotations` folders
+to the `Annotation` folders inside the corresponding directories `WIDER_train` and `WIDER_val`.
+Also annotation lists `val.txt` and `train.txt` should be copied to `data/WIDERFace` from `WIDER_train_annotations` and `WIDER_val_annotations`.
+The directory should be like this:
+
+```
+mmdetection
+├── mmdet
+├── tools
+├── configs
+├── data
+│ ├── WIDERFace
+│ │ ├── WIDER_train
+│ | │ ├──0--Parade
+│ | │ ├── ...
+│ | │ ├── Annotations
+│ │ ├── WIDER_val
+│ | │ ├──0--Parade
+│ | │ ├── ...
+│ | │ ├── Annotations
+│ │ ├── val.txt
+│ │ ├── train.txt
+
+```
+
+After that you can train the SSD300 on WIDER by launching training with the `ssd300_wider_face.py` config or
+create your own config based on the presented one.
diff --git a/configs/wider_face/ssd300_wider_face.py b/configs/wider_face/ssd300_wider_face.py
new file mode 100644
index 0000000000000000000000000000000000000000..53cafc1ef2ea269e3a98208de3e6fefc03a94837
--- /dev/null
+++ b/configs/wider_face/ssd300_wider_face.py
@@ -0,0 +1,135 @@
+# model settings
+input_size = 300
+model = dict(
+ type='SingleStageDetector',
+ pretrained='open-mmlab://vgg16_caffe',
+ backbone=dict(
+ type='SSDVGG',
+ input_size=input_size,
+ depth=16,
+ with_last_pool=False,
+ ceil_mode=True,
+ out_indices=(3, 4),
+ out_feature_indices=(22, 34),
+ l2_norm_scale=20),
+ neck=None,
+ bbox_head=dict(
+ type='SSDHead',
+ input_size=input_size,
+ in_channels=(512, 1024, 512, 256, 256, 256),
+ num_classes=2,
+ anchor_strides=(8, 16, 32, 64, 100, 300),
+ basesize_ratio_range=(0.15, 0.9),
+ anchor_ratios=([2], [2, 3], [2, 3], [2, 3], [2], [2]),
+ target_means=(.0, .0, .0, .0),
+ target_stds=(0.1, 0.1, 0.2, 0.2)))
+cudnn_benchmark = True
+train_cfg = dict(
+ assigner=dict(
+ type='MaxIoUAssigner',
+ pos_iou_thr=0.5,
+ neg_iou_thr=0.5,
+ min_pos_iou=0.,
+ ignore_iof_thr=-1,
+ gt_max_assign_all=False),
+ smoothl1_beta=1.,
+ allowed_border=-1,
+ pos_weight=-1,
+ neg_pos_ratio=3,
+ debug=False)
+test_cfg = dict(
+ nms=dict(type='nms', iou_thr=0.45),
+ min_bbox_size=0,
+ score_thr=0.02,
+ max_per_img=200)
+# model training and testing settings
+# dataset settings
+dataset_type = 'WIDERFaceDataset'
+data_root = 'data/WIDERFace/'
+img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
+data = dict(
+ imgs_per_gpu=60,
+ workers_per_gpu=2,
+ train=dict(
+ type='RepeatDataset',
+ times=2,
+ dataset=dict(
+ type=dataset_type,
+ ann_file=[
+ data_root + 'train.txt',
+ ],
+ img_prefix=[data_root + 'WIDER_train/'],
+ img_scale=(300, 300),
+ min_size=17, # throw away very small faces to improve training,
+ # because 300x300 is too low resolution to detect them
+ img_norm_cfg=img_norm_cfg,
+ size_divisor=None,
+ flip_ratio=0.5,
+ with_mask=False,
+ with_crowd=False,
+ with_label=True,
+ test_mode=False,
+ extra_aug=dict(
+ photo_metric_distortion=dict(
+ brightness_delta=32,
+ contrast_range=(0.5, 1.5),
+ saturation_range=(0.5, 1.5),
+ hue_delta=18),
+ expand=dict(
+ mean=img_norm_cfg['mean'],
+ to_rgb=img_norm_cfg['to_rgb'],
+ ratio_range=(1, 4)),
+ random_crop=dict(
+ min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3)),
+ resize_keep_ratio=False)),
+ val=dict(
+ type=dataset_type,
+ ann_file=data_root + '/val.txt',
+ img_prefix=data_root + 'WIDER_val/',
+ img_scale=(300, 300),
+ img_norm_cfg=img_norm_cfg,
+ size_divisor=None,
+ flip_ratio=0,
+ with_mask=False,
+ with_label=False,
+ test_mode=True,
+ resize_keep_ratio=False),
+ test=dict(
+ type=dataset_type,
+ ann_file=data_root + '/val.txt',
+ img_prefix=data_root + 'WIDER_val/',
+ img_scale=(300, 300),
+ img_norm_cfg=img_norm_cfg,
+ size_divisor=None,
+ flip_ratio=0,
+ with_mask=False,
+ with_label=False,
+ test_mode=True,
+ resize_keep_ratio=False))
+# optimizer
+optimizer = dict(type='SGD', lr=1e-3, momentum=0.9, weight_decay=5e-4)
+optimizer_config = dict()
+# learning policy
+lr_config = dict(
+ policy='step',
+ warmup='linear',
+ warmup_iters=1000,
+ warmup_ratio=1.0 / 3,
+ step=[16, 20])
+checkpoint_config = dict(interval=1)
+# yapf:disable
+log_config = dict(
+ interval=1,
+ hooks=[
+ dict(type='TextLoggerHook'),
+ dict(type='TensorboardLoggerHook')
+ ])
+# yapf:enable
+# runtime settings
+total_epochs = 24
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = './work_dirs/ssd300_wider'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]
diff --git a/mmdet/core/evaluation/class_names.py b/mmdet/core/evaluation/class_names.py
index a8d0a3b9b2c4acf1ade04cafc16f48fd0eb743d7..87fb2399b2b5a852a34da384f7d52e610a41e0f9 100644
--- a/mmdet/core/evaluation/class_names.py
+++ b/mmdet/core/evaluation/class_names.py
@@ -1,6 +1,10 @@
import mmcv
+def wider_face_classes():
+ return ['face']
+
+
def voc_classes():
return [
'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
@@ -82,7 +86,8 @@ dataset_aliases = {
'voc': ['voc', 'pascal_voc', 'voc07', 'voc12'],
'imagenet_det': ['det', 'imagenet_det', 'ilsvrc_det'],
'imagenet_vid': ['vid', 'imagenet_vid', 'ilsvrc_vid'],
- 'coco': ['coco', 'mscoco', 'ms_coco']
+ 'coco': ['coco', 'mscoco', 'ms_coco'],
+ 'wider_face': ['WIDERFaceDataset', 'wider_face', 'WDIERFace']
}
diff --git a/mmdet/datasets/__init__.py b/mmdet/datasets/__init__.py
index 572b0faa4ab11a0c0f0b72253c517e39299381c0..2c1612daff14ba8de444b45ec54a9f4d67f3be00 100644
--- a/mmdet/datasets/__init__.py
+++ b/mmdet/datasets/__init__.py
@@ -2,6 +2,7 @@ from .custom import CustomDataset
from .xml_style import XMLDataset
from .coco import CocoDataset
from .voc import VOCDataset
+from .wider_face import WIDERFaceDataset
from .loader import GroupSampler, DistributedGroupSampler, build_dataloader
from .utils import to_tensor, random_scale, show_ann, get_dataset
from .concat_dataset import ConcatDataset
@@ -12,5 +13,5 @@ __all__ = [
'CustomDataset', 'XMLDataset', 'CocoDataset', 'VOCDataset', 'GroupSampler',
'DistributedGroupSampler', 'build_dataloader', 'to_tensor', 'random_scale',
'show_ann', 'get_dataset', 'ConcatDataset', 'RepeatDataset',
- 'ExtraAugmentation'
+ 'ExtraAugmentation', 'WIDERFaceDataset'
]
diff --git a/mmdet/datasets/wider_face.py b/mmdet/datasets/wider_face.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad52a952704a2c0ca4ade0ad98f53bbfd637b0ba
--- /dev/null
+++ b/mmdet/datasets/wider_face.py
@@ -0,0 +1,37 @@
+import os.path as osp
+import xml.etree.ElementTree as ET
+
+import mmcv
+
+from .xml_style import XMLDataset
+
+
+class WIDERFaceDataset(XMLDataset):
+ """
+ Reader for the WIDER Face dataset in PASCAL VOC format.
+ Conversion scripts can be found in
+ https://github.com/sovrasov/wider-face-pascal-voc-annotations
+ """
+ CLASSES = ('face',)
+
+ def __init__(self, **kwargs):
+ super(WIDERFaceDataset, self).__init__(**kwargs)
+
+ def load_annotations(self, ann_file):
+ img_infos = []
+ img_ids = mmcv.list_from_file(ann_file)
+ for img_id in img_ids:
+ filename = '{}.jpg'.format(img_id)
+ xml_path = osp.join(self.img_prefix, 'Annotations',
+ '{}.xml'.format(img_id))
+ tree = ET.parse(xml_path)
+ root = tree.getroot()
+ size = root.find('size')
+ width = int(size.find('width').text)
+ height = int(size.find('height').text)
+ folder = root.find('folder').text
+ img_infos.append(
+ dict(id=img_id, filename=osp.join(folder, filename),
+ width=width, height=height))
+
+ return img_infos
diff --git a/mmdet/datasets/xml_style.py b/mmdet/datasets/xml_style.py
index 40e3374e6dc17be5c31b238acb47e93fceb56a56..e0c6ac1b6f42b62b93a73a21ddd6f4135b103910 100644
--- a/mmdet/datasets/xml_style.py
+++ b/mmdet/datasets/xml_style.py
@@ -9,9 +9,10 @@ from .custom import CustomDataset
class XMLDataset(CustomDataset):
- def __init__(self, **kwargs):
+ def __init__(self, min_size=None, **kwargs):
super(XMLDataset, self).__init__(**kwargs)
self.cat2label = {cat: i + 1 for i, cat in enumerate(self.CLASSES)}
+ self.min_size = min_size
def load_annotations(self, ann_file):
img_infos = []
@@ -50,7 +51,14 @@ class XMLDataset(CustomDataset):
int(bnd_box.find('xmax').text),
int(bnd_box.find('ymax').text)
]
- if difficult:
+ ignore = False
+ if self.min_size:
+ assert not self.test_mode
+ w = bbox[2] - bbox[0]
+ h = bbox[3] - bbox[1]
+ if w < self.min_size or h < self.min_size:
+ ignore = True
+ if difficult or ignore:
bboxes_ignore.append(bbox)
labels_ignore.append(label)
else: