Modify README and add gen_data.py

datasets/labels/val_wider_face.json

+{
+    "info": {
+        "version": "1.0",
+        "year": 2020,
+        "contributor": "Mr.yang",
+        "date_created": "2018/08/21",
+        "github": "https://github.com/bleakie"
+    },
+    "licenses": [
+        {
+            "url": "http://creativecommons.org/licenses/by-nc-sa/2.0/",
+            "name": "Attribution-NonCommercial-ShareAlike License",
+            "id": 1
+        }
+    ],
+    "images": [],
+    "annotations": [],
+    "categories": [
+        {
+            "supercategory": "face",
+            "id": 1,
+            "name": "face",
+            "keypoints": [
+                "left_eye",
+                "right_eye",
+                "nose",
+                "left_mouth",
+                "right_mouth"
+            ]
+        }
+    ]
+}
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model.properties

+# 模型唯一标识
+modelCode = 440
+# 模型名称
+modelName=centerface_pytorch
+# 模型描述
+modelDescription=CenterFace是一种人脸检测算法,采用了轻量级网络mobileNetV2作为主干网络,结合特征金字塔网络(FPN)实现anchor free的人脸检测。
+# 应用场景
+appScenario=推理,训练,教育,交通,公安,医疗
+# 框架类型
+frameType=PyTorch

requirements.txt

+onnx==1.14.0
+Torch==1.13.1
+TorchVision==0.14.1
+numpy>=1.18.1
+Pillow>=7.2.0
+pycocotools>=2.0.2
+opencv-python==4.1.2.30
+scipy
+progress
+Cython
+tqdm
+IPython
+numba==0.58.1

src/lib/datasets/dataset/centerface_hp.py

@@ -31,17 +31,16 @@ class FACEHP(data.Dataset):
 
         self.acc_idxs = [1, 2, 3, 4]
         self.data_dir = opt.data_dir
-        self.img_dir = os.path.join(
-            self.data_dir, 'WIDER_train/images')  # 训练图片所在地址
+        self.img_dir = os.path.join(self.data_dir, 'images/train')  # 训练图片所在地址
         _ann_name = {'train': 'train', 'val': 'val'}
         if split == 'val':
             self.annot_path = os.path.join(
-                self.data_dir, 'annotations',
-                '{}_wider_face.json').format(_ann_name[split])
+                self.data_dir, 'labels',
+                '{}_face.json').format(_ann_name[split])
         else:
             self.annot_path = os.path.join(
-                self.data_dir, 'annotations',
-                '{}_wider_face.json').format(_ann_name[split])
+                self.data_dir, 'labels',
+                '{}_face.json').format(_ann_name[split])
         self.max_objs = 32
         self._data_rng = np.random.RandomState(123)
         self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571],

src/lib/opts_pose.py

@@ -24,7 +24,7 @@ class opts(object):
                                       '2: show the network output features'
                                       '3: use matplot to display'  # useful when lunching training with ipython notebook
                                       '4: save all visualizations to disk')
-        self.parser.add_argument('--demo', default='/path/WIDER_train/images/0--Parade/0_Parade_marchingband_1_80.jpg',
+        self.parser.add_argument('--demo', default='../datassets/images/train/0--Parade/0_Parade_marchingband_1_512.jpg',
                                  help='path to image/ image folders/ video. '
                                       'or "webcam"')
         self.parser.add_argument(
@@ -92,7 +92,7 @@ class opts(object):
         # train
         self.parser.add_argument('--lr', type=float, default=1.25e-4,
                                  help='learning rate for batch size 32.')
-        self.parser.add_argument('--lr_step', type=str, default='30,80',
+        self.parser.add_argument('--lr_step', type=str, default='90,120',
                                  help='drop learning rate by 10.')
         self.parser.add_argument('--num_epochs', type=int, default=140,
                                  help='total training epochs.')

src/test_wider_face.py

@@ -114,8 +114,8 @@ if __name__ == '__main__':
     debug = 0
     model_path = '../models/model_best.pth'  # or your model path
     # 单图测试
-    # test_img(model_path, debug)
+    test_img(model_path, debug)
     # 视频测试
     # test_vedio(model_path, debug)
     # WIDER_val 数据集测试
-    test_wider_Face(model_path, debug)
+    # test_wider_Face(model_path, debug)

src/train.sh

 #!/usr/bin/env bash
 
-python main.py --input_h 800 --input_w 800 --batch_size 16 --lr 6.25e-5 --load_model /path/of/model_best.pth --data_dir /your/WIDER_train/dir/path
+python main.py --input_h 514 --input_w 514 --batch_size 8 --lr 5e-4 --data_dir ../datasets/images/train/
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src/train_multi.sh

 #!/usr/bin/env bash
 
-python main.py --input_h 800 --input_w 800 --batch_size 16 --lr 6.25e-5 --gpus 0,1 --load_model /path/of/model_best.pth --data_dir /your/WIDER_train/dir/path
+python main.py --input_h 514 --input_w 514 --batch_size 8 --lr 5e-4 --data_dir ../datasets/images/train/ --gpus 0,1
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