diff --git a/Retinaface_model_v2/Resnet50_Final.pth b/Retinaface_model_v2/Resnet50_Final.pth
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+++ b/Retinaface_model_v2/Resnet50_Final.pth
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+version https://git-lfs.github.com/spec/v1
+oid sha256:6d1de9c2944f2ccddca5f5e010ea5ae64a39845a86311af6fdf30841b0a5a16d
+size 109497761
diff --git a/Retinaface_model_v2/mobilenet0.25_Final.pth b/Retinaface_model_v2/mobilenet0.25_Final.pth
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+version https://git-lfs.github.com/spec/v1
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+size 1789735
diff --git a/Retinaface_model_v2/mobilenetV1X0.25_pretrain.tar b/Retinaface_model_v2/mobilenetV1X0.25_pretrain.tar
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+version https://git-lfs.github.com/spec/v1
+oid sha256:75c37f8d438b7c6aed2e6b310fd0540c40dabf0dd820770bd7082668dfd7c1c1
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diff --git a/convert_to_onnx.py b/convert_to_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..c96b5860a026fc695b772a1d41b3100b7f9bb1c0
--- /dev/null
+++ b/convert_to_onnx.py
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+from __future__ import print_function
+import os
+import argparse
+import torch
+import torch.backends.cudnn as cudnn
+import numpy as np
+from data import cfg_mnet, cfg_re50
+from layers.functions.prior_box import PriorBox
+from utils.nms.py_cpu_nms import py_cpu_nms
+import cv2
+from models.retinaface import RetinaFace
+from utils.box_utils import decode, decode_landm
+from utils.timer import Timer
+
+
+parser = argparse.ArgumentParser(description='Test')
+parser.add_argument('-m', '--trained_model', default='./weights/mobilenet0.25_Final.pth',
+                    type=str, help='Trained state_dict file path to open')
+parser.add_argument('--network', default='mobile0.25', help='Backbone network mobile0.25 or resnet50')
+parser.add_argument('--long_side', default=640, help='when origin_size is false, long_side is scaled size(320 or 640 for long side)')
+parser.add_argument('--cpu', action="store_true", default=True, help='Use cpu inference')
+
+args = parser.parse_args()
+
+
+def check_keys(model, pretrained_state_dict):
+    ckpt_keys = set(pretrained_state_dict.keys())
+    model_keys = set(model.state_dict().keys())
+    used_pretrained_keys = model_keys & ckpt_keys
+    unused_pretrained_keys = ckpt_keys - model_keys
+    missing_keys = model_keys - ckpt_keys
+    print('Missing keys:{}'.format(len(missing_keys)))
+    print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
+    print('Used keys:{}'.format(len(used_pretrained_keys)))
+    assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
+    return True
+
+
+def remove_prefix(state_dict, prefix):
+    ''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
+    print('remove prefix \'{}\''.format(prefix))
+    f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
+    return {f(key): value for key, value in state_dict.items()}
+
+
+def load_model(model, pretrained_path, load_to_cpu):
+    print('Loading pretrained model from {}'.format(pretrained_path))
+    if load_to_cpu:
+        pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage)
+    else:
+        device = torch.cuda.current_device()
+        pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device))
+    if "state_dict" in pretrained_dict.keys():
+        pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.')
+    else:
+        pretrained_dict = remove_prefix(pretrained_dict, 'module.')
+    check_keys(model, pretrained_dict)
+    model.load_state_dict(pretrained_dict, strict=False)
+    return model
+
+
+if __name__ == '__main__':
+    torch.set_grad_enabled(False)
+    cfg = None
+    if args.network == "mobile0.25":
+        cfg = cfg_mnet
+    elif args.network == "resnet50":
+        cfg = cfg_re50
+    # net and model
+    net = RetinaFace(cfg=cfg, phase = 'test')
+    net = load_model(net, args.trained_model, args.cpu)
+    net.eval()
+    print('Finished loading model!')
+    print(net)
+    device = torch.device("cpu" if args.cpu else "cuda")
+    net = net.to(device)
+
+    # ------------------------ export -----------------------------
+    output_onnx = 'FaceDetector.onnx'
+    print("==> Exporting model to ONNX format at '{}'".format(output_onnx))
+    input_names = ["input0"]
+    output_names = ["output0"]
+    inputs = torch.randn(1, 3, args.long_side, args.long_side).to(device)
+
+    torch_out = torch.onnx._export(net, inputs, output_onnx, export_params=True, verbose=False,
+                                   input_names=input_names, output_names=output_names)
+
+
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diff --git a/data/__init__.py b/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..01a1f00579e0ce4ff4320aec21fd41c388c5ae18
--- /dev/null
+++ b/data/__init__.py
@@ -0,0 +1,3 @@
+from .wider_face import WiderFaceDetection, detection_collate
+from .data_augment import *
+from .config import *
diff --git a/data/__pycache__/__init__.cpython-38.pyc b/data/__pycache__/__init__.cpython-38.pyc
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diff --git a/data/__pycache__/wider_face.cpython-38.pyc b/data/__pycache__/wider_face.cpython-38.pyc
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diff --git a/data/config.py b/data/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..c553f9a5235e8e908ebc4854ba41c860b5bf6762
--- /dev/null
+++ b/data/config.py
@@ -0,0 +1,42 @@
+# config.py
+
+cfg_mnet = {
+    'name': 'mobilenet0.25',
+    'min_sizes': [[16, 32], [64, 128], [256, 512]],
+    'steps': [8, 16, 32],
+    'variance': [0.1, 0.2],
+    'clip': False,
+    'loc_weight': 2.0,
+    'gpu_train': True,
+    'batch_size': 32,
+    'ngpu': 1,
+    'epoch': 250,
+    'decay1': 190,
+    'decay2': 220,
+    'image_size': 640,
+    'pretrain': True,
+    'return_layers': {'stage1': 1, 'stage2': 2, 'stage3': 3},
+    'in_channel': 32,
+    'out_channel': 64
+}
+
+cfg_re50 = {
+    'name': 'Resnet50',
+    'min_sizes': [[16, 32], [64, 128], [256, 512]],
+    'steps': [8, 16, 32],
+    'variance': [0.1, 0.2],
+    'clip': False,
+    'loc_weight': 2.0,
+    'gpu_train': True,
+    'batch_size': 24,
+    'ngpu': 4,
+    'epoch': 100,
+    'decay1': 70,
+    'decay2': 90,
+    'image_size': 840,
+    'pretrain': True,
+    'return_layers': {'layer2': 1, 'layer3': 2, 'layer4': 3},
+    'in_channel': 256,
+    'out_channel': 256
+}
+
diff --git a/data/data_augment.py b/data/data_augment.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5628e760ca4126a9e958b7d5f2cd89edca319ac
--- /dev/null
+++ b/data/data_augment.py
@@ -0,0 +1,237 @@
+import cv2
+import numpy as np
+import random
+from utils.box_utils import matrix_iof
+
+
+def _crop(image, boxes, labels, landm, img_dim):
+    height, width, _ = image.shape
+    pad_image_flag = True
+
+    for _ in range(250):
+        """
+        if random.uniform(0, 1) <= 0.2:
+            scale = 1.0
+        else:
+            scale = random.uniform(0.3, 1.0)
+        """
+        PRE_SCALES = [0.3, 0.45, 0.6, 0.8, 1.0]
+        scale = random.choice(PRE_SCALES)
+        short_side = min(width, height)
+        w = int(scale * short_side)
+        h = w
+
+        if width == w:
+            l = 0
+        else:
+            l = random.randrange(width - w)
+        if height == h:
+            t = 0
+        else:
+            t = random.randrange(height - h)
+        roi = np.array((l, t, l + w, t + h))
+
+        value = matrix_iof(boxes, roi[np.newaxis])
+        flag = (value >= 1)
+        if not flag.any():
+            continue
+
+        centers = (boxes[:, :2] + boxes[:, 2:]) / 2
+        mask_a = np.logical_and(roi[:2] < centers, centers < roi[2:]).all(axis=1)
+        boxes_t = boxes[mask_a].copy()
+        labels_t = labels[mask_a].copy()
+        landms_t = landm[mask_a].copy()
+        landms_t = landms_t.reshape([-1, 5, 2])
+
+        if boxes_t.shape[0] == 0:
+            continue
+
+        image_t = image[roi[1]:roi[3], roi[0]:roi[2]]
+
+        boxes_t[:, :2] = np.maximum(boxes_t[:, :2], roi[:2])
+        boxes_t[:, :2] -= roi[:2]
+        boxes_t[:, 2:] = np.minimum(boxes_t[:, 2:], roi[2:])
+        boxes_t[:, 2:] -= roi[:2]
+
+        # landm
+        landms_t[:, :, :2] = landms_t[:, :, :2] - roi[:2]
+        landms_t[:, :, :2] = np.maximum(landms_t[:, :, :2], np.array([0, 0]))
+        landms_t[:, :, :2] = np.minimum(landms_t[:, :, :2], roi[2:] - roi[:2])
+        landms_t = landms_t.reshape([-1, 10])
+
+
+	# make sure that the cropped image contains at least one face > 16 pixel at training image scale
+        b_w_t = (boxes_t[:, 2] - boxes_t[:, 0] + 1) / w * img_dim
+        b_h_t = (boxes_t[:, 3] - boxes_t[:, 1] + 1) / h * img_dim
+        mask_b = np.minimum(b_w_t, b_h_t) > 0.0
+        boxes_t = boxes_t[mask_b]
+        labels_t = labels_t[mask_b]
+        landms_t = landms_t[mask_b]
+
+        if boxes_t.shape[0] == 0:
+            continue
+
+        pad_image_flag = False
+
+        return image_t, boxes_t, labels_t, landms_t, pad_image_flag
+    return image, boxes, labels, landm, pad_image_flag
+
+
+def _distort(image):
+
+    def _convert(image, alpha=1, beta=0):
+        tmp = image.astype(float) * alpha + beta
+        tmp[tmp < 0] = 0
+        tmp[tmp > 255] = 255
+        image[:] = tmp
+
+    image = image.copy()
+
+    if random.randrange(2):
+
+        #brightness distortion
+        if random.randrange(2):
+            _convert(image, beta=random.uniform(-32, 32))
+
+        #contrast distortion
+        if random.randrange(2):
+            _convert(image, alpha=random.uniform(0.5, 1.5))
+
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+
+        #saturation distortion
+        if random.randrange(2):
+            _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5))
+
+        #hue distortion
+        if random.randrange(2):
+            tmp = image[:, :, 0].astype(int) + random.randint(-18, 18)
+            tmp %= 180
+            image[:, :, 0] = tmp
+
+        image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)
+
+    else:
+
+        #brightness distortion
+        if random.randrange(2):
+            _convert(image, beta=random.uniform(-32, 32))
+
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+
+        #saturation distortion
+        if random.randrange(2):
+            _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5))
+
+        #hue distortion
+        if random.randrange(2):
+            tmp = image[:, :, 0].astype(int) + random.randint(-18, 18)
+            tmp %= 180
+            image[:, :, 0] = tmp
+
+        image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)
+
+        #contrast distortion
+        if random.randrange(2):
+            _convert(image, alpha=random.uniform(0.5, 1.5))
+
+    return image
+
+
+def _expand(image, boxes, fill, p):
+    if random.randrange(2):
+        return image, boxes
+
+    height, width, depth = image.shape
+
+    scale = random.uniform(1, p)
+    w = int(scale * width)
+    h = int(scale * height)
+
+    left = random.randint(0, w - width)
+    top = random.randint(0, h - height)
+
+    boxes_t = boxes.copy()
+    boxes_t[:, :2] += (left, top)
+    boxes_t[:, 2:] += (left, top)
+    expand_image = np.empty(
+        (h, w, depth),
+        dtype=image.dtype)
+    expand_image[:, :] = fill
+    expand_image[top:top + height, left:left + width] = image
+    image = expand_image
+
+    return image, boxes_t
+
+
+def _mirror(image, boxes, landms):
+    _, width, _ = image.shape
+    if random.randrange(2):
+        image = image[:, ::-1]
+        boxes = boxes.copy()
+        boxes[:, 0::2] = width - boxes[:, 2::-2]
+
+        # landm
+        landms = landms.copy()
+        landms = landms.reshape([-1, 5, 2])
+        landms[:, :, 0] = width - landms[:, :, 0]
+        tmp = landms[:, 1, :].copy()
+        landms[:, 1, :] = landms[:, 0, :]
+        landms[:, 0, :] = tmp
+        tmp1 = landms[:, 4, :].copy()
+        landms[:, 4, :] = landms[:, 3, :]
+        landms[:, 3, :] = tmp1
+        landms = landms.reshape([-1, 10])
+
+    return image, boxes, landms
+
+
+def _pad_to_square(image, rgb_mean, pad_image_flag):
+    if not pad_image_flag:
+        return image
+    height, width, _ = image.shape
+    long_side = max(width, height)
+    image_t = np.empty((long_side, long_side, 3), dtype=image.dtype)
+    image_t[:, :] = rgb_mean
+    image_t[0:0 + height, 0:0 + width] = image
+    return image_t
+
+
+def _resize_subtract_mean(image, insize, rgb_mean):
+    interp_methods = [cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_NEAREST, cv2.INTER_LANCZOS4]
+    interp_method = interp_methods[random.randrange(5)]
+    image = cv2.resize(image, (insize, insize), interpolation=interp_method)
+    image = image.astype(np.float32)
+    image -= rgb_mean
+    return image.transpose(2, 0, 1)
+
+
+class preproc(object):
+
+    def __init__(self, img_dim, rgb_means):
+        self.img_dim = img_dim
+        self.rgb_means = rgb_means
+
+    def __call__(self, image, targets):
+        assert targets.shape[0] > 0, "this image does not have gt"
+
+        boxes = targets[:, :4].copy()
+        labels = targets[:, -1].copy()
+        landm = targets[:, 4:-1].copy()
+
+        image_t, boxes_t, labels_t, landm_t, pad_image_flag = _crop(image, boxes, labels, landm, self.img_dim)
+        image_t = _distort(image_t)
+        image_t = _pad_to_square(image_t,self.rgb_means, pad_image_flag)
+        image_t, boxes_t, landm_t = _mirror(image_t, boxes_t, landm_t)
+        height, width, _ = image_t.shape
+        image_t = _resize_subtract_mean(image_t, self.img_dim, self.rgb_means)
+        boxes_t[:, 0::2] /= width
+        boxes_t[:, 1::2] /= height
+
+        landm_t[:, 0::2] /= width
+        landm_t[:, 1::2] /= height
+
+        labels_t = np.expand_dims(labels_t, 1)
+        targets_t = np.hstack((boxes_t, landm_t, labels_t))
+
+        return image_t, targets_t
diff --git a/data/wider_face.py b/data/wider_face.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6d43a580588c737346a727fdd8e31aae832dc36
--- /dev/null
+++ b/data/wider_face.py
@@ -0,0 +1,101 @@
+import os
+import os.path
+import sys
+import torch
+import torch.utils.data as data
+import cv2
+import numpy as np
+
+class WiderFaceDetection(data.Dataset):
+    def __init__(self, txt_path, preproc=None):
+        self.preproc = preproc
+        self.imgs_path = []
+        self.words = []
+        f = open(txt_path,'r')
+        lines = f.readlines()
+        isFirst = True
+        labels = []
+        for line in lines:
+            line = line.rstrip()
+            if line.startswith('#'):
+                if isFirst is True:
+                    isFirst = False
+                else:
+                    labels_copy = labels.copy()
+                    self.words.append(labels_copy)
+                    labels.clear()
+                path = line[2:]
+                path = txt_path.replace('label.txt','images/') + path
+                self.imgs_path.append(path)
+            else:
+                line = line.split(' ')
+                label = [float(x) for x in line]
+                labels.append(label)
+
+        self.words.append(labels)
+
+    def __len__(self):
+        return len(self.imgs_path)
+
+    def __getitem__(self, index):
+        img = cv2.imread(self.imgs_path[index])
+        height, width, _ = img.shape
+
+        labels = self.words[index]
+        annotations = np.zeros((0, 15))
+        if len(labels) == 0:
+            return annotations
+        for idx, label in enumerate(labels):
+            annotation = np.zeros((1, 15))
+            # bbox
+            annotation[0, 0] = label[0]  # x1
+            annotation[0, 1] = label[1]  # y1
+            annotation[0, 2] = label[0] + label[2]  # x2
+            annotation[0, 3] = label[1] + label[3]  # y2
+
+            # landmarks
+            annotation[0, 4] = label[4]    # l0_x
+            annotation[0, 5] = label[5]    # l0_y
+            annotation[0, 6] = label[7]    # l1_x
+            annotation[0, 7] = label[8]    # l1_y
+            annotation[0, 8] = label[10]   # l2_x
+            annotation[0, 9] = label[11]   # l2_y
+            annotation[0, 10] = label[13]  # l3_x
+            annotation[0, 11] = label[14]  # l3_y
+            annotation[0, 12] = label[16]  # l4_x
+            annotation[0, 13] = label[17]  # l4_y
+            if (annotation[0, 4]<0):
+                annotation[0, 14] = -1
+            else:
+                annotation[0, 14] = 1
+
+            annotations = np.append(annotations, annotation, axis=0)
+        target = np.array(annotations)
+        if self.preproc is not None:
+            img, target = self.preproc(img, target)
+
+        return torch.from_numpy(img), target
+
+def detection_collate(batch):
+    """Custom collate fn for dealing with batches of images that have a different
+    number of associated object annotations (bounding boxes).
+
+    Arguments:
+        batch: (tuple) A tuple of tensor images and lists of annotations
+
+    Return:
+        A tuple containing:
+            1) (tensor) batch of images stacked on their 0 dim
+            2) (list of tensors) annotations for a given image are stacked on 0 dim
+    """
+    targets = []
+    imgs = []
+    for _, sample in enumerate(batch):
+        for _, tup in enumerate(sample):
+            if torch.is_tensor(tup):
+                imgs.append(tup)
+            elif isinstance(tup, type(np.empty(0))):
+                annos = torch.from_numpy(tup).float()
+                targets.append(annos)
+
+    return (torch.stack(imgs, 0), targets)
diff --git a/dataset/widerface/widerface/train/images/0--Parade/0_Parade_Parade_0_102.jpg b/dataset/widerface/widerface/train/images/0--Parade/0_Parade_Parade_0_102.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b7ed25c973589a9569e3a62ea706ed3e6d9588de
--- /dev/null
+++ b/dataset/widerface/widerface/train/images/0--Parade/0_Parade_Parade_0_102.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5640c641d09628a3ec70801cf4847818f8f305ce258d8acf5bde6422a12659e4
+size 185291
diff --git a/dataset/widerface/widerface/train/images/0--Parade/0_Parade_Parade_0_12.jpg b/dataset/widerface/widerface/train/images/0--Parade/0_Parade_Parade_0_12.jpg
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+++ b/dataset/widerface/widerface/train/images/0--Parade/0_Parade_Parade_0_12.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/dataset/widerface/widerface/train/timer.py b/dataset/widerface/widerface/train/timer.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4b3b8098a5ad41f8d18d42b6b2fedb694aa5508
--- /dev/null
+++ b/dataset/widerface/widerface/train/timer.py
@@ -0,0 +1,40 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+import time
+
+
+class Timer(object):
+    """A simple timer."""
+    def __init__(self):
+        self.total_time = 0.
+        self.calls = 0
+        self.start_time = 0.
+        self.diff = 0.
+        self.average_time = 0.
+
+    def tic(self):
+        # using time.time instead of time.clock because time time.clock
+        # does not normalize for multithreading
+        self.start_time = time.time()
+
+    def toc(self, average=True):
+        self.diff = time.time() - self.start_time
+        self.total_time += self.diff
+        self.calls += 1
+        self.average_time = self.total_time / self.calls
+        if average:
+            return self.average_time
+        else:
+            return self.diff
+
+    def clear(self):
+        self.total_time = 0.
+        self.calls = 0
+        self.start_time = 0.
+        self.diff = 0.
+        self.average_time = 0.
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diff --git a/detect.py b/detect.py
new file mode 100644
index 0000000000000000000000000000000000000000..89a4f9a385848817f80717c4f080ce1789d8b278
--- /dev/null
+++ b/detect.py
@@ -0,0 +1,170 @@
+from __future__ import print_function
+import os
+import argparse
+import torch
+import torch.backends.cudnn as cudnn
+import numpy as np
+from data import cfg_mnet, cfg_re50
+from layers.functions.prior_box import PriorBox
+from utils.nms.py_cpu_nms import py_cpu_nms
+import cv2
+from models.retinaface import RetinaFace
+from utils.box_utils import decode, decode_landm
+import time
+
+parser = argparse.ArgumentParser(description='Retinaface')
+
+parser.add_argument('-m', '--trained_model', default='Retinaface_model_v2/mobilenet0.25_Final.pth',
+                    type=str, help='Trained state_dict file path to open')
+parser.add_argument('--network', default='mobile0.25', help='Backbone network mobile0.25 or resnet50')
+parser.add_argument('--cpu', action="store_true", default=True, help='Use cpu inference')
+parser.add_argument('--confidence_threshold', default=0.02, type=float, help='confidence_threshold')
+parser.add_argument('--top_k', default=5000, type=int, help='top_k')
+parser.add_argument('--nms_threshold', default=0.4, type=float, help='nms_threshold')
+parser.add_argument('--keep_top_k', default=750, type=int, help='keep_top_k')
+parser.add_argument('-s', '--save_image', action="store_true", default=True, help='show detection results')
+parser.add_argument('--vis_thres', default=0.6, type=float, help='visualization_threshold')
+args = parser.parse_args()
+
+
+def check_keys(model, pretrained_state_dict):
+    ckpt_keys = set(pretrained_state_dict.keys())
+    model_keys = set(model.state_dict().keys())
+    used_pretrained_keys = model_keys & ckpt_keys
+    unused_pretrained_keys = ckpt_keys - model_keys
+    missing_keys = model_keys - ckpt_keys
+    print('Missing keys:{}'.format(len(missing_keys)))
+    print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
+    print('Used keys:{}'.format(len(used_pretrained_keys)))
+    assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
+    return True
+
+
+def remove_prefix(state_dict, prefix):
+    ''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
+    print('remove prefix \'{}\''.format(prefix))
+    f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
+    return {f(key): value for key, value in state_dict.items()}
+
+
+def load_model(model, pretrained_path, load_to_cpu):
+    print('Loading pretrained model from {}'.format(pretrained_path))
+    if load_to_cpu:
+        pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage)
+    else:
+        device = torch.cuda.current_device()
+        pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device))
+    if "state_dict" in pretrained_dict.keys():
+        pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.')
+    else:
+        pretrained_dict = remove_prefix(pretrained_dict, 'module.')
+    check_keys(model, pretrained_dict)
+    model.load_state_dict(pretrained_dict, strict=False)
+    return model
+
+
+if __name__ == '__main__':
+    torch.set_grad_enabled(False)
+    cfg = None
+    if args.network == "mobile0.25":
+        cfg = cfg_mnet
+    elif args.network == "resnet50":
+        cfg = cfg_re50
+
+    net = RetinaFace(cfg=cfg, phase='test')
+    net = load_model(net, args.trained_model, args.cpu)
+    net.eval()
+    print('Finished loading model!')
+    cudnn.benchmark = True
+    device = torch.device("cpu" if args.cpu else "cuda")
+    net = net.to(device)
+
+    resize = 1
+
+    cap = cv2.VideoCapture(0)
+
+    if not cap.isOpened():
+        print("Không thể mở camera")
+        exit()
+
+    while True:
+        ret, frame = cap.read()  # Đọc khung hình từ camera
+        if not ret:
+            print("Không thể nhận khung hình. Đang thoát...")
+            break
+
+        img_raw = frame.copy()
+
+        img = np.float32(frame)
+        im_height, im_width, _ = img.shape
+        scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
+        img -= (104, 117, 123)
+        img = img.transpose(2, 0, 1)
+        img = torch.from_numpy(img).unsqueeze(0)
+        img = img.to(device)
+        scale = scale.to(device)
+
+        tic = time.time()
+        loc, conf, landms = net(img)  # forward pass
+        print('net forward time: {:.4f}'.format(time.time() - tic))
+
+        priorbox = PriorBox(cfg, image_size=(im_height, im_width))
+        priors = priorbox.forward()
+        priors = priors.to(device)
+        prior_data = priors.data
+        boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance'])
+        boxes = boxes * scale / resize
+        boxes = boxes.cpu().numpy()
+        scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
+        landms = decode_landm(landms.data.squeeze(0), prior_data, cfg['variance'])
+        scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
+                               img.shape[3], img.shape[2], img.shape[3], img.shape[2],
+                               img.shape[3], img.shape[2]])
+        scale1 = scale1.to(device)
+        landms = landms * scale1 / resize
+        landms = landms.cpu().numpy()
+
+        inds = np.where(scores > args.confidence_threshold)[0]
+        boxes = boxes[inds]
+        landms = landms[inds]
+        scores = scores[inds]
+
+        order = scores.argsort()[::-1][:args.top_k]
+        boxes = boxes[order]
+        landms = landms[order]
+        scores = scores[order]
+
+        dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
+        keep = py_cpu_nms(dets, args.nms_threshold)
+        dets = dets[keep, :]
+        landms = landms[keep]
+
+        dets = dets[:args.keep_top_k, :]
+        landms = landms[:args.keep_top_k, :]
+
+        dets = np.concatenate((dets, landms), axis=1)
+
+        for b in dets:
+            if b[4] < args.vis_thres:
+                continue
+            text = "{:.4f}".format(b[4])
+            b = list(map(int, b))
+            cv2.rectangle(img_raw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
+            cx = b[0]
+            cy = b[1] + 12
+            cv2.putText(img_raw, text, (cx, cy),
+                        cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))
+
+            cv2.circle(img_raw, (b[5], b[6]), 1, (0, 0, 255), 4)
+            cv2.circle(img_raw, (b[7], b[8]), 1, (0, 255, 255), 4)
+            cv2.circle(img_raw, (b[9], b[10]), 1, (255, 0, 255), 4)
+            cv2.circle(img_raw, (b[11], b[12]), 1, (0, 255, 0), 4)
+            cv2.circle(img_raw, (b[13], b[14]), 1, (255, 0, 0), 4)
+
+        cv2.imshow('RetinaFace Detection', img_raw)
+
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
diff --git a/layers/__init__.py b/layers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..59dfcbd639b692434275a446f1081315497fa99e
--- /dev/null
+++ b/layers/__init__.py
@@ -0,0 +1,2 @@
+from .functions import *
+from .modules import *
diff --git a/layers/__pycache__/__init__.cpython-38.pyc b/layers/__pycache__/__init__.cpython-38.pyc
new file mode 100644
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diff --git a/layers/functions/__pycache__/prior_box.cpython-38.pyc b/layers/functions/__pycache__/prior_box.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..806185d6b3a0838ae2373e6f551fff442d46654a
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diff --git a/layers/functions/prior_box.py b/layers/functions/prior_box.py
new file mode 100644
index 0000000000000000000000000000000000000000..60626b791a31debedea9cd06a505b8233aa4946c
--- /dev/null
+++ b/layers/functions/prior_box.py
@@ -0,0 +1,34 @@
+import torch
+from itertools import product as product
+import numpy as np
+from math import ceil
+
+
+class PriorBox(object):
+    def __init__(self, cfg, image_size=None, phase='train'):
+        super(PriorBox, self).__init__()
+        self.min_sizes = cfg['min_sizes']
+        self.steps = cfg['steps']
+        self.clip = cfg['clip']
+        self.image_size = image_size
+        self.feature_maps = [[ceil(self.image_size[0]/step), ceil(self.image_size[1]/step)] for step in self.steps]
+        self.name = "s"
+
+    def forward(self):
+        anchors = []
+        for k, f in enumerate(self.feature_maps):
+            min_sizes = self.min_sizes[k]
+            for i, j in product(range(f[0]), range(f[1])):
+                for min_size in min_sizes:
+                    s_kx = min_size / self.image_size[1]
+                    s_ky = min_size / self.image_size[0]
+                    dense_cx = [x * self.steps[k] / self.image_size[1] for x in [j + 0.5]]
+                    dense_cy = [y * self.steps[k] / self.image_size[0] for y in [i + 0.5]]
+                    for cy, cx in product(dense_cy, dense_cx):
+                        anchors += [cx, cy, s_kx, s_ky]
+
+        # back to torch land
+        output = torch.Tensor(anchors).view(-1, 4)
+        if self.clip:
+            output.clamp_(max=1, min=0)
+        return output
diff --git a/layers/modules/__init__.py b/layers/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..028f5306fb0fa0ca29fe51782841da6fc1658dc7
--- /dev/null
+++ b/layers/modules/__init__.py
@@ -0,0 +1,3 @@
+from .multibox_loss import MultiBoxLoss
+
+__all__ = ['MultiBoxLoss']
diff --git a/layers/modules/__pycache__/__init__.cpython-38.pyc b/layers/modules/__pycache__/__init__.cpython-38.pyc
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diff --git a/layers/modules/__pycache__/multibox_loss.cpython-38.pyc b/layers/modules/__pycache__/multibox_loss.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c7247a1d42ac344d6ab469037d83de0e95b3930c
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diff --git a/layers/modules/multibox_loss.py b/layers/modules/multibox_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..8bdd0c2f80ac30f5769f5362bbf104093a2e46ba
--- /dev/null
+++ b/layers/modules/multibox_loss.py
@@ -0,0 +1,125 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+from utils.box_utils import match, log_sum_exp
+from data import cfg_mnet
+GPU = cfg_mnet['gpu_train']
+
+class MultiBoxLoss(nn.Module):
+    """SSD Weighted Loss Function
+    Compute Targets:
+        1) Produce Confidence Target Indices by matching  ground truth boxes
+           with (default) 'priorboxes' that have jaccard index > threshold parameter
+           (default threshold: 0.5).
+        2) Produce localization target by 'encoding' variance into offsets of ground
+           truth boxes and their matched  'priorboxes'.
+        3) Hard negative mining to filter the excessive number of negative examples
+           that comes with using a large number of default bounding boxes.
+           (default negative:positive ratio 3:1)
+    Objective Loss:
+        L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N
+        Where, Lconf is the CrossEntropy Loss and Lloc is the SmoothL1 Loss
+        weighted by α which is set to 1 by cross val.
+        Args:
+            c: class confidences,
+            l: predicted boxes,
+            g: ground truth boxes
+            N: number of matched default boxes
+        See: https://arxiv.org/pdf/1512.02325.pdf for more details.
+    """
+
+    def __init__(self, num_classes, overlap_thresh, prior_for_matching, bkg_label, neg_mining, neg_pos, neg_overlap, encode_target):
+        super(MultiBoxLoss, self).__init__()
+        self.num_classes = num_classes
+        self.threshold = overlap_thresh
+        self.background_label = bkg_label
+        self.encode_target = encode_target
+        self.use_prior_for_matching = prior_for_matching
+        self.do_neg_mining = neg_mining
+        self.negpos_ratio = neg_pos
+        self.neg_overlap = neg_overlap
+        self.variance = [0.1, 0.2]
+
+    def forward(self, predictions, priors, targets):
+        """Multibox Loss
+        Args:
+            predictions (tuple): A tuple containing loc preds, conf preds,
+            and prior boxes from SSD net.
+                conf shape: torch.size(batch_size,num_priors,num_classes)
+                loc shape: torch.size(batch_size,num_priors,4)
+                priors shape: torch.size(num_priors,4)
+
+            ground_truth (tensor): Ground truth boxes and labels for a batch,
+                shape: [batch_size,num_objs,5] (last idx is the label).
+        """
+
+        loc_data, conf_data, landm_data = predictions
+        priors = priors
+        num = loc_data.size(0)
+        num_priors = (priors.size(0))
+
+        # match priors (default boxes) and ground truth boxes
+        loc_t = torch.Tensor(num, num_priors, 4)
+        landm_t = torch.Tensor(num, num_priors, 10)
+        conf_t = torch.LongTensor(num, num_priors)
+        for idx in range(num):
+            truths = targets[idx][:, :4].data
+            labels = targets[idx][:, -1].data
+            landms = targets[idx][:, 4:14].data
+            defaults = priors.data
+            match(self.threshold, truths, defaults, self.variance, labels, landms, loc_t, conf_t, landm_t, idx)
+        if GPU:
+            loc_t = loc_t.cuda()
+            conf_t = conf_t.cuda()
+            landm_t = landm_t.cuda()
+
+        zeros = torch.tensor(0).cuda()
+        # landm Loss (Smooth L1)
+        # Shape: [batch,num_priors,10]
+        pos1 = conf_t > zeros
+        num_pos_landm = pos1.long().sum(1, keepdim=True)
+        N1 = max(num_pos_landm.data.sum().float(), 1)
+        pos_idx1 = pos1.unsqueeze(pos1.dim()).expand_as(landm_data)
+        landm_p = landm_data[pos_idx1].view(-1, 10)
+        landm_t = landm_t[pos_idx1].view(-1, 10)
+        loss_landm = F.smooth_l1_loss(landm_p, landm_t, reduction='sum')
+
+
+        pos = conf_t != zeros
+        conf_t[pos] = 1
+
+        # Localization Loss (Smooth L1)
+        # Shape: [batch,num_priors,4]
+        pos_idx = pos.unsqueeze(pos.dim()).expand_as(loc_data)
+        loc_p = loc_data[pos_idx].view(-1, 4)
+        loc_t = loc_t[pos_idx].view(-1, 4)
+        loss_l = F.smooth_l1_loss(loc_p, loc_t, reduction='sum')
+
+        # Compute max conf across batch for hard negative mining
+        batch_conf = conf_data.view(-1, self.num_classes)
+        loss_c = log_sum_exp(batch_conf) - batch_conf.gather(1, conf_t.view(-1, 1))
+
+        # Hard Negative Mining
+        loss_c[pos.view(-1, 1)] = 0 # filter out pos boxes for now
+        loss_c = loss_c.view(num, -1)
+        _, loss_idx = loss_c.sort(1, descending=True)
+        _, idx_rank = loss_idx.sort(1)
+        num_pos = pos.long().sum(1, keepdim=True)
+        num_neg = torch.clamp(self.negpos_ratio*num_pos, max=pos.size(1)-1)
+        neg = idx_rank < num_neg.expand_as(idx_rank)
+
+        # Confidence Loss Including Positive and Negative Examples
+        pos_idx = pos.unsqueeze(2).expand_as(conf_data)
+        neg_idx = neg.unsqueeze(2).expand_as(conf_data)
+        conf_p = conf_data[(pos_idx+neg_idx).gt(0)].view(-1,self.num_classes)
+        targets_weighted = conf_t[(pos+neg).gt(0)]
+        loss_c = F.cross_entropy(conf_p, targets_weighted, reduction='sum')
+
+        # Sum of losses: L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N
+        N = max(num_pos.data.sum().float(), 1)
+        loss_l /= N
+        loss_c /= N
+        loss_landm /= N1
+
+        return loss_l, loss_c, loss_landm
diff --git a/models/__init__.py b/models/__init__.py
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diff --git a/models/__pycache__/net.cpython-38.pyc b/models/__pycache__/net.cpython-38.pyc
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diff --git a/models/__pycache__/retinaface.cpython-38.pyc b/models/__pycache__/retinaface.cpython-38.pyc
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diff --git a/models/net.py b/models/net.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6ac0d45c77bee1c7a79f03e51a71e6d22870b7c
--- /dev/null
+++ b/models/net.py
@@ -0,0 +1,137 @@
+import time
+import torch
+import torch.nn as nn
+import torchvision.models._utils as _utils
+import torchvision.models as models
+import torch.nn.functional as F
+from torch.autograd import Variable
+
+def conv_bn(inp, oup, stride = 1, leaky = 0):
+    return nn.Sequential(
+        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
+        nn.BatchNorm2d(oup),
+        nn.LeakyReLU(negative_slope=leaky, inplace=True)
+    )
+
+def conv_bn_no_relu(inp, oup, stride):
+    return nn.Sequential(
+        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
+        nn.BatchNorm2d(oup),
+    )
+
+def conv_bn1X1(inp, oup, stride, leaky=0):
+    return nn.Sequential(
+        nn.Conv2d(inp, oup, 1, stride, padding=0, bias=False),
+        nn.BatchNorm2d(oup),
+        nn.LeakyReLU(negative_slope=leaky, inplace=True)
+    )
+
+def conv_dw(inp, oup, stride, leaky=0.1):
+    return nn.Sequential(
+        nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
+        nn.BatchNorm2d(inp),
+        nn.LeakyReLU(negative_slope= leaky,inplace=True),
+
+        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
+        nn.BatchNorm2d(oup),
+        nn.LeakyReLU(negative_slope= leaky,inplace=True),
+    )
+
+class SSH(nn.Module):
+    def __init__(self, in_channel, out_channel):
+        super(SSH, self).__init__()
+        assert out_channel % 4 == 0
+        leaky = 0
+        if (out_channel <= 64):
+            leaky = 0.1
+        self.conv3X3 = conv_bn_no_relu(in_channel, out_channel//2, stride=1)
+
+        self.conv5X5_1 = conv_bn(in_channel, out_channel//4, stride=1, leaky = leaky)
+        self.conv5X5_2 = conv_bn_no_relu(out_channel//4, out_channel//4, stride=1)
+
+        self.conv7X7_2 = conv_bn(out_channel//4, out_channel//4, stride=1, leaky = leaky)
+        self.conv7x7_3 = conv_bn_no_relu(out_channel//4, out_channel//4, stride=1)
+
+    def forward(self, input):
+        conv3X3 = self.conv3X3(input)
+
+        conv5X5_1 = self.conv5X5_1(input)
+        conv5X5 = self.conv5X5_2(conv5X5_1)
+
+        conv7X7_2 = self.conv7X7_2(conv5X5_1)
+        conv7X7 = self.conv7x7_3(conv7X7_2)
+
+        out = torch.cat([conv3X3, conv5X5, conv7X7], dim=1)
+        out = F.relu(out)
+        return out
+
+class FPN(nn.Module):
+    def __init__(self,in_channels_list,out_channels):
+        super(FPN,self).__init__()
+        leaky = 0
+        if (out_channels <= 64):
+            leaky = 0.1
+        self.output1 = conv_bn1X1(in_channels_list[0], out_channels, stride = 1, leaky = leaky)
+        self.output2 = conv_bn1X1(in_channels_list[1], out_channels, stride = 1, leaky = leaky)
+        self.output3 = conv_bn1X1(in_channels_list[2], out_channels, stride = 1, leaky = leaky)
+
+        self.merge1 = conv_bn(out_channels, out_channels, leaky = leaky)
+        self.merge2 = conv_bn(out_channels, out_channels, leaky = leaky)
+
+    def forward(self, input):
+        # names = list(input.keys())
+        input = list(input.values())
+
+        output1 = self.output1(input[0])
+        output2 = self.output2(input[1])
+        output3 = self.output3(input[2])
+
+        up3 = F.interpolate(output3, size=[output2.size(2), output2.size(3)], mode="nearest")
+        output2 = output2 + up3
+        output2 = self.merge2(output2)
+
+        up2 = F.interpolate(output2, size=[output1.size(2), output1.size(3)], mode="nearest")
+        output1 = output1 + up2
+        output1 = self.merge1(output1)
+
+        out = [output1, output2, output3]
+        return out
+
+
+
+class MobileNetV1(nn.Module):
+    def __init__(self):
+        super(MobileNetV1, self).__init__()
+        self.stage1 = nn.Sequential(
+            conv_bn(3, 8, 2, leaky = 0.1),    # 3
+            conv_dw(8, 16, 1),   # 7
+            conv_dw(16, 32, 2),  # 11
+            conv_dw(32, 32, 1),  # 19
+            conv_dw(32, 64, 2),  # 27
+            conv_dw(64, 64, 1),  # 43
+        )
+        self.stage2 = nn.Sequential(
+            conv_dw(64, 128, 2),  # 43 + 16 = 59
+            conv_dw(128, 128, 1), # 59 + 32 = 91
+            conv_dw(128, 128, 1), # 91 + 32 = 123
+            conv_dw(128, 128, 1), # 123 + 32 = 155
+            conv_dw(128, 128, 1), # 155 + 32 = 187
+            conv_dw(128, 128, 1), # 187 + 32 = 219
+        )
+        self.stage3 = nn.Sequential(
+            conv_dw(128, 256, 2), # 219 +3 2 = 241
+            conv_dw(256, 256, 1), # 241 + 64 = 301
+        )
+        self.avg = nn.AdaptiveAvgPool2d((1,1))
+        self.fc = nn.Linear(256, 1000)
+
+    def forward(self, x):
+        x = self.stage1(x)
+        x = self.stage2(x)
+        x = self.stage3(x)
+        x = self.avg(x)
+        # x = self.model(x)
+        x = x.view(-1, 256)
+        x = self.fc(x)
+        return x
+
diff --git a/models/retinaface.py b/models/retinaface.py
new file mode 100644
index 0000000000000000000000000000000000000000..69b16c3502711122b2de0144788aca008794c345
--- /dev/null
+++ b/models/retinaface.py
@@ -0,0 +1,127 @@
+import torch
+import torch.nn as nn
+import torchvision.models.detection.backbone_utils as backbone_utils
+import torchvision.models._utils as _utils
+import torch.nn.functional as F
+from collections import OrderedDict
+
+from models.net import MobileNetV1 as MobileNetV1
+from models.net import FPN as FPN
+from models.net import SSH as SSH
+
+
+
+class ClassHead(nn.Module):
+    def __init__(self,inchannels=512,num_anchors=3):
+        super(ClassHead,self).__init__()
+        self.num_anchors = num_anchors
+        self.conv1x1 = nn.Conv2d(inchannels,self.num_anchors*2,kernel_size=(1,1),stride=1,padding=0)
+
+    def forward(self,x):
+        out = self.conv1x1(x)
+        out = out.permute(0,2,3,1).contiguous()
+        
+        return out.view(out.shape[0], -1, 2)
+
+class BboxHead(nn.Module):
+    def __init__(self,inchannels=512,num_anchors=3):
+        super(BboxHead,self).__init__()
+        self.conv1x1 = nn.Conv2d(inchannels,num_anchors*4,kernel_size=(1,1),stride=1,padding=0)
+
+    def forward(self,x):
+        out = self.conv1x1(x)
+        out = out.permute(0,2,3,1).contiguous()
+
+        return out.view(out.shape[0], -1, 4)
+
+class LandmarkHead(nn.Module):
+    def __init__(self,inchannels=512,num_anchors=3):
+        super(LandmarkHead,self).__init__()
+        self.conv1x1 = nn.Conv2d(inchannels,num_anchors*10,kernel_size=(1,1),stride=1,padding=0)
+
+    def forward(self,x):
+        out = self.conv1x1(x)
+        out = out.permute(0,2,3,1).contiguous()
+
+        return out.view(out.shape[0], -1, 10)
+
+class RetinaFace(nn.Module):
+    def __init__(self, cfg = None, phase = 'train'):
+        """
+        :param cfg:  Network related settings.
+        :param phase: train or test.
+        """
+        super(RetinaFace,self).__init__()
+        self.phase = phase
+        backbone = None
+        if cfg['name'] == 'mobilenet0.25':
+            backbone = MobileNetV1()
+            if cfg['pretrain']:
+                checkpoint = torch.load("./Retinaface_model_v2/mobilenetV1X0.25_pretrain.tar", map_location=torch.device('cpu'))
+                from collections import OrderedDict
+                new_state_dict = OrderedDict()
+                for k, v in checkpoint['state_dict'].items():
+                    name = k[7:]  # remove module.
+                    new_state_dict[name] = v
+                # load params
+                backbone.load_state_dict(new_state_dict)
+        elif cfg['name'] == 'Resnet50':
+            import torchvision.models as models
+            backbone = models.resnet50(pretrained=cfg['pretrain'])
+
+        self.body = _utils.IntermediateLayerGetter(backbone, cfg['return_layers'])
+        in_channels_stage2 = cfg['in_channel']
+        in_channels_list = [
+            in_channels_stage2 * 2,
+            in_channels_stage2 * 4,
+            in_channels_stage2 * 8,
+        ]
+        out_channels = cfg['out_channel']
+        self.fpn = FPN(in_channels_list,out_channels)
+        self.ssh1 = SSH(out_channels, out_channels)
+        self.ssh2 = SSH(out_channels, out_channels)
+        self.ssh3 = SSH(out_channels, out_channels)
+
+        self.ClassHead = self._make_class_head(fpn_num=3, inchannels=cfg['out_channel'])
+        self.BboxHead = self._make_bbox_head(fpn_num=3, inchannels=cfg['out_channel'])
+        self.LandmarkHead = self._make_landmark_head(fpn_num=3, inchannels=cfg['out_channel'])
+
+    def _make_class_head(self,fpn_num=3,inchannels=64,anchor_num=2):
+        classhead = nn.ModuleList()
+        for i in range(fpn_num):
+            classhead.append(ClassHead(inchannels,anchor_num))
+        return classhead
+    
+    def _make_bbox_head(self,fpn_num=3,inchannels=64,anchor_num=2):
+        bboxhead = nn.ModuleList()
+        for i in range(fpn_num):
+            bboxhead.append(BboxHead(inchannels,anchor_num))
+        return bboxhead
+
+    def _make_landmark_head(self,fpn_num=3,inchannels=64,anchor_num=2):
+        landmarkhead = nn.ModuleList()
+        for i in range(fpn_num):
+            landmarkhead.append(LandmarkHead(inchannels,anchor_num))
+        return landmarkhead
+
+    def forward(self,inputs):
+        out = self.body(inputs)
+
+        # FPN
+        fpn = self.fpn(out)
+
+        # SSH
+        feature1 = self.ssh1(fpn[0])
+        feature2 = self.ssh2(fpn[1])
+        feature3 = self.ssh3(fpn[2])
+        features = [feature1, feature2, feature3]
+
+        bbox_regressions = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=1)
+        classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)],dim=1)
+        ldm_regressions = torch.cat([self.LandmarkHead[i](feature) for i, feature in enumerate(features)], dim=1)
+
+        if self.phase == 'train':
+            output = (bbox_regressions, classifications, ldm_regressions)
+        else:
+            output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions)
+        return output
\ No newline at end of file
diff --git a/requirements_cpu.txt b/requirements_cpu.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b9faa93d159dd9aebcad3aaafc88709209b14ee0
--- /dev/null
+++ b/requirements_cpu.txt
@@ -0,0 +1,6 @@
+# Python 3.8
+torch==1.10.1+cpu
+torchvision==0.11.2+cpu
+torchaudio==0.10.1
+opencv-python==4.10.0.84
+-f https://download.pytorch.org/whl/cpu/torch_stable.html
diff --git a/requirements_gpu b/requirements_gpu
new file mode 100644
index 0000000000000000000000000000000000000000..cb3f2cff54962bb0a4f5b78adc1aa1288c837a2a
--- /dev/null
+++ b/requirements_gpu
@@ -0,0 +1,6 @@
+# Python 3.8
+torch==1.10.1+cu11
+torchvision==0.11.2+cu111
+torchaudio==0.10.1
+opencv-python==4.10.0.84
+-f https://download.pytorch.org/whl/cu111/torch_stable.html
diff --git a/test_fddb.py b/test_fddb.py
new file mode 100644
index 0000000000000000000000000000000000000000..889b1aada348172173a013d4b1248c4531ad4162
--- /dev/null
+++ b/test_fddb.py
@@ -0,0 +1,207 @@
+from __future__ import print_function
+import os
+import argparse
+import torch
+import torch.backends.cudnn as cudnn
+import numpy as np
+from data import cfg_mnet, cfg_re50
+from layers.functions.prior_box import PriorBox
+from utils.nms.py_cpu_nms import py_cpu_nms
+import cv2
+from models.retinaface import RetinaFace
+from utils.box_utils import decode, decode_landm
+from utils.timer import Timer
+
+parser = argparse.ArgumentParser(description='Retinaface')
+
+parser.add_argument('-m', '--trained_model', default='./weights/mobilenet0.25_Final.pth',
+                    type=str, help='Trained state_dict file path to open')
+parser.add_argument('--network', default='mobile0.25', help='Backbone network mobile0.25 or resnet50')
+parser.add_argument('--save_folder', default='eval/', type=str, help='Dir to save results')
+parser.add_argument('--cpu', action="store_true", default=False, help='Use cpu inference')
+parser.add_argument('--dataset', default='FDDB', type=str, choices=['FDDB'], help='dataset')
+parser.add_argument('--confidence_threshold', default=0.02, type=float, help='confidence_threshold')
+parser.add_argument('--top_k', default=5000, type=int, help='top_k')
+parser.add_argument('--nms_threshold', default=0.4, type=float, help='nms_threshold')
+parser.add_argument('--keep_top_k', default=750, type=int, help='keep_top_k')
+parser.add_argument('-s', '--save_image', action="store_true", default=False, help='show detection results')
+parser.add_argument('--vis_thres', default=0.5, type=float, help='visualization_threshold')
+args = parser.parse_args()
+
+
+def check_keys(model, pretrained_state_dict):
+    ckpt_keys = set(pretrained_state_dict.keys())
+    model_keys = set(model.state_dict().keys())
+    used_pretrained_keys = model_keys & ckpt_keys
+    unused_pretrained_keys = ckpt_keys - model_keys
+    missing_keys = model_keys - ckpt_keys
+    print('Missing keys:{}'.format(len(missing_keys)))
+    print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
+    print('Used keys:{}'.format(len(used_pretrained_keys)))
+    assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
+    return True
+
+
+def remove_prefix(state_dict, prefix):
+    ''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
+    print('remove prefix \'{}\''.format(prefix))
+    f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
+    return {f(key): value for key, value in state_dict.items()}
+
+
+def load_model(model, pretrained_path, load_to_cpu):
+    print('Loading pretrained model from {}'.format(pretrained_path))
+    if load_to_cpu:
+        pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage)
+    else:
+        device = torch.cuda.current_device()
+        pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device))
+    if "state_dict" in pretrained_dict.keys():
+        pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.')
+    else:
+        pretrained_dict = remove_prefix(pretrained_dict, 'module.')
+    check_keys(model, pretrained_dict)
+    model.load_state_dict(pretrained_dict, strict=False)
+    return model
+
+
+if __name__ == '__main__':
+    torch.set_grad_enabled(False)
+    cfg = None
+    if args.network == "mobile0.25":
+        cfg = cfg_mnet
+    elif args.network == "resnet50":
+        cfg = cfg_re50
+    # net and model
+    net = RetinaFace(cfg=cfg, phase = 'test')
+    net = load_model(net, args.trained_model, args.cpu)
+    net.eval()
+    print('Finished loading model!')
+    print(net)
+    cudnn.benchmark = True
+    device = torch.device("cpu" if args.cpu else "cuda")
+    net = net.to(device)
+
+
+    # save file
+    if not os.path.exists(args.save_folder):
+        os.makedirs(args.save_folder)
+    fw = open(os.path.join(args.save_folder, args.dataset + '_dets.txt'), 'w')
+
+    # testing dataset
+    testset_folder = os.path.join('data', args.dataset, 'images/')
+    testset_list = os.path.join('data', args.dataset, 'img_list.txt')
+    with open(testset_list, 'r') as fr:
+        test_dataset = fr.read().split()
+    num_images = len(test_dataset)
+
+    # testing scale
+    resize = 1
+
+    _t = {'forward_pass': Timer(), 'misc': Timer()}
+
+    # testing begin
+    for i, img_name in enumerate(test_dataset):
+        image_path = testset_folder + img_name + '.jpg'
+        img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR)
+
+        img = np.float32(img_raw)
+        if resize != 1:
+            img = cv2.resize(img, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)
+        im_height, im_width, _ = img.shape
+        scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
+        img -= (104, 117, 123)
+        img = img.transpose(2, 0, 1)
+        img = torch.from_numpy(img).unsqueeze(0)
+        img = img.to(device)
+        scale = scale.to(device)
+
+        _t['forward_pass'].tic()
+        loc, conf, landms = net(img)  # forward pass
+        _t['forward_pass'].toc()
+        _t['misc'].tic()
+        priorbox = PriorBox(cfg, image_size=(im_height, im_width))
+        priors = priorbox.forward()
+        priors = priors.to(device)
+        prior_data = priors.data
+        boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance'])
+        boxes = boxes * scale / resize
+        boxes = boxes.cpu().numpy()
+        scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
+        landms = decode_landm(landms.data.squeeze(0), prior_data, cfg['variance'])
+        scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
+                               img.shape[3], img.shape[2], img.shape[3], img.shape[2],
+                               img.shape[3], img.shape[2]])
+        scale1 = scale1.to(device)
+        landms = landms * scale1 / resize
+        landms = landms.cpu().numpy()
+
+        # ignore low scores
+        inds = np.where(scores > args.confidence_threshold)[0]
+        boxes = boxes[inds]
+        landms = landms[inds]
+        scores = scores[inds]
+
+        # keep top-K before NMS
+        # order = scores.argsort()[::-1][:args.top_k]
+        order = scores.argsort()[::-1]
+        boxes = boxes[order]
+        landms = landms[order]
+        scores = scores[order]
+
+        # do NMS
+        dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
+        keep = py_cpu_nms(dets, args.nms_threshold)
+
+        dets = dets[keep, :]
+        landms = landms[keep]
+
+        # keep top-K faster NMS
+        # dets = dets[:args.keep_top_k, :]
+        # landms = landms[:args.keep_top_k, :]
+
+        dets = np.concatenate((dets, landms), axis=1)
+        _t['misc'].toc()
+
+        # save dets
+        if args.dataset == "FDDB":
+            fw.write('{:s}\n'.format(img_name))
+            fw.write('{:.1f}\n'.format(dets.shape[0]))
+            for k in range(dets.shape[0]):
+                xmin = dets[k, 0]
+                ymin = dets[k, 1]
+                xmax = dets[k, 2]
+                ymax = dets[k, 3]
+                score = dets[k, 4]
+                w = xmax - xmin + 1
+                h = ymax - ymin + 1
+                # fw.write('{:.3f} {:.3f} {:.3f} {:.3f} {:.10f}\n'.format(xmin, ymin, w, h, score))
+                fw.write('{:d} {:d} {:d} {:d} {:.10f}\n'.format(int(xmin), int(ymin), int(w), int(h), score))
+        print('im_detect: {:d}/{:d} forward_pass_time: {:.4f}s misc: {:.4f}s'.format(i + 1, num_images, _t['forward_pass'].average_time, _t['misc'].average_time))
+
+        # show image
+        if args.save_image:
+            for b in dets:
+                if b[4] < args.vis_thres:
+                    continue
+                text = "{:.4f}".format(b[4])
+                b = list(map(int, b))
+                cv2.rectangle(img_raw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
+                cx = b[0]
+                cy = b[1] + 12
+                cv2.putText(img_raw, text, (cx, cy),
+                            cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))
+
+                # landms
+                cv2.circle(img_raw, (b[5], b[6]), 1, (0, 0, 255), 4)
+                cv2.circle(img_raw, (b[7], b[8]), 1, (0, 255, 255), 4)
+                cv2.circle(img_raw, (b[9], b[10]), 1, (255, 0, 255), 4)
+                cv2.circle(img_raw, (b[11], b[12]), 1, (0, 255, 0), 4)
+                cv2.circle(img_raw, (b[13], b[14]), 1, (255, 0, 0), 4)
+            # save image
+            if not os.path.exists("./results/"):
+                os.makedirs("./results/")
+            name = "./results/" + str(i) + ".jpg"
+            cv2.imwrite(name, img_raw)
+
+    fw.close()
diff --git a/test_widerface.py b/test_widerface.py
new file mode 100644
index 0000000000000000000000000000000000000000..475c4e7af455e05b1eef605a492b9ce44a3bd788
--- /dev/null
+++ b/test_widerface.py
@@ -0,0 +1,219 @@
+from __future__ import print_function
+import os
+import argparse
+import torch
+import torch.backends.cudnn as cudnn
+import numpy as np
+from data import cfg_mnet, cfg_re50
+from layers.functions.prior_box import PriorBox
+from utils.nms.py_cpu_nms import py_cpu_nms
+import cv2
+from models.retinaface import RetinaFace
+from utils.box_utils import decode, decode_landm
+from utils.timer import Timer
+
+
+parser = argparse.ArgumentParser(description='Retinaface')
+parser.add_argument('-m', '--trained_model', default='./weights/Resnet50_Final.pth',
+                    type=str, help='Trained state_dict file path to open')
+parser.add_argument('--network', default='resnet50', help='Backbone network mobile0.25 or resnet50')
+parser.add_argument('--origin_size', default=True, type=str, help='Whether use origin image size to evaluate')
+parser.add_argument('--save_folder', default='./widerface_evaluate/widerface_txt/', type=str, help='Dir to save txt results')
+parser.add_argument('--cpu', action="store_true", default=False, help='Use cpu inference')
+parser.add_argument('--dataset_folder', default='./data/widerface/val/images/', type=str, help='dataset path')
+parser.add_argument('--confidence_threshold', default=0.02, type=float, help='confidence_threshold')
+parser.add_argument('--top_k', default=5000, type=int, help='top_k')
+parser.add_argument('--nms_threshold', default=0.4, type=float, help='nms_threshold')
+parser.add_argument('--keep_top_k', default=750, type=int, help='keep_top_k')
+parser.add_argument('-s', '--save_image', action="store_true", default=False, help='show detection results')
+parser.add_argument('--vis_thres', default=0.5, type=float, help='visualization_threshold')
+args = parser.parse_args()
+
+
+def check_keys(model, pretrained_state_dict):
+    ckpt_keys = set(pretrained_state_dict.keys())
+    model_keys = set(model.state_dict().keys())
+    used_pretrained_keys = model_keys & ckpt_keys
+    unused_pretrained_keys = ckpt_keys - model_keys
+    missing_keys = model_keys - ckpt_keys
+    print('Missing keys:{}'.format(len(missing_keys)))
+    print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
+    print('Used keys:{}'.format(len(used_pretrained_keys)))
+    assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
+    return True
+
+
+def remove_prefix(state_dict, prefix):
+    ''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
+    print('remove prefix \'{}\''.format(prefix))
+    f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
+    return {f(key): value for key, value in state_dict.items()}
+
+
+def load_model(model, pretrained_path, load_to_cpu):
+    print('Loading pretrained model from {}'.format(pretrained_path))
+    if load_to_cpu:
+        pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage)
+    else:
+        device = torch.cuda.current_device()
+        pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device))
+    if "state_dict" in pretrained_dict.keys():
+        pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.')
+    else:
+        pretrained_dict = remove_prefix(pretrained_dict, 'module.')
+    check_keys(model, pretrained_dict)
+    model.load_state_dict(pretrained_dict, strict=False)
+    return model
+
+
+if __name__ == '__main__':
+    torch.set_grad_enabled(False)
+
+    cfg = None
+    if args.network == "mobile0.25":
+        cfg = cfg_mnet
+    elif args.network == "resnet50":
+        cfg = cfg_re50
+    # net and model
+    net = RetinaFace(cfg=cfg, phase = 'test')
+    net = load_model(net, args.trained_model, args.cpu)
+    net.eval()
+    print('Finished loading model!')
+    print(net)
+    cudnn.benchmark = True
+    device = torch.device("cpu" if args.cpu else "cuda")
+    net = net.to(device)
+
+    # testing dataset
+    testset_folder = args.dataset_folder
+    testset_list = args.dataset_folder[:-7] + "wider_val.txt"
+
+    with open(testset_list, 'r') as fr:
+        test_dataset = fr.read().split()
+    num_images = len(test_dataset)
+
+    _t = {'forward_pass': Timer(), 'misc': Timer()}
+
+    # testing begin
+    for i, img_name in enumerate(test_dataset):
+        image_path = testset_folder + img_name
+        img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR)
+        img = np.float32(img_raw)
+
+        # testing scale
+        target_size = 1600
+        max_size = 2150
+        im_shape = img.shape
+        im_size_min = np.min(im_shape[0:2])
+        im_size_max = np.max(im_shape[0:2])
+        resize = float(target_size) / float(im_size_min)
+        # prevent bigger axis from being more than max_size:
+        if np.round(resize * im_size_max) > max_size:
+            resize = float(max_size) / float(im_size_max)
+        if args.origin_size:
+            resize = 1
+
+        if resize != 1:
+            img = cv2.resize(img, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)
+        im_height, im_width, _ = img.shape
+        scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
+        img -= (104, 117, 123)
+        img = img.transpose(2, 0, 1)
+        img = torch.from_numpy(img).unsqueeze(0)
+        img = img.to(device)
+        scale = scale.to(device)
+
+        _t['forward_pass'].tic()
+        loc, conf, landms = net(img)  # forward pass
+        _t['forward_pass'].toc()
+        _t['misc'].tic()
+        priorbox = PriorBox(cfg, image_size=(im_height, im_width))
+        priors = priorbox.forward()
+        priors = priors.to(device)
+        prior_data = priors.data
+        boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance'])
+        boxes = boxes * scale / resize
+        boxes = boxes.cpu().numpy()
+        scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
+        landms = decode_landm(landms.data.squeeze(0), prior_data, cfg['variance'])
+        scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
+                               img.shape[3], img.shape[2], img.shape[3], img.shape[2],
+                               img.shape[3], img.shape[2]])
+        scale1 = scale1.to(device)
+        landms = landms * scale1 / resize
+        landms = landms.cpu().numpy()
+
+        # ignore low scores
+        inds = np.where(scores > args.confidence_threshold)[0]
+        boxes = boxes[inds]
+        landms = landms[inds]
+        scores = scores[inds]
+
+        # keep top-K before NMS
+        order = scores.argsort()[::-1]
+        # order = scores.argsort()[::-1][:args.top_k]
+        boxes = boxes[order]
+        landms = landms[order]
+        scores = scores[order]
+
+        # do NMS
+        dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
+        keep = py_cpu_nms(dets, args.nms_threshold)
+        # keep = nms(dets, args.nms_threshold,force_cpu=args.cpu)
+        dets = dets[keep, :]
+        landms = landms[keep]
+
+        # keep top-K faster NMS
+        # dets = dets[:args.keep_top_k, :]
+        # landms = landms[:args.keep_top_k, :]
+
+        dets = np.concatenate((dets, landms), axis=1)
+        _t['misc'].toc()
+
+        # --------------------------------------------------------------------
+        save_name = args.save_folder + img_name[:-4] + ".txt"
+        dirname = os.path.dirname(save_name)
+        if not os.path.isdir(dirname):
+            os.makedirs(dirname)
+        with open(save_name, "w") as fd:
+            bboxs = dets
+            file_name = os.path.basename(save_name)[:-4] + "\n"
+            bboxs_num = str(len(bboxs)) + "\n"
+            fd.write(file_name)
+            fd.write(bboxs_num)
+            for box in bboxs:
+                x = int(box[0])
+                y = int(box[1])
+                w = int(box[2]) - int(box[0])
+                h = int(box[3]) - int(box[1])
+                confidence = str(box[4])
+                line = str(x) + " " + str(y) + " " + str(w) + " " + str(h) + " " + confidence + " \n"
+                fd.write(line)
+
+        print('im_detect: {:d}/{:d} forward_pass_time: {:.4f}s misc: {:.4f}s'.format(i + 1, num_images, _t['forward_pass'].average_time, _t['misc'].average_time))
+
+        # save image
+        if args.save_image:
+            for b in dets:
+                if b[4] < args.vis_thres:
+                    continue
+                text = "{:.4f}".format(b[4])
+                b = list(map(int, b))
+                cv2.rectangle(img_raw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
+                cx = b[0]
+                cy = b[1] + 12
+                cv2.putText(img_raw, text, (cx, cy),
+                            cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))
+
+                # landms
+                cv2.circle(img_raw, (b[5], b[6]), 1, (0, 0, 255), 4)
+                cv2.circle(img_raw, (b[7], b[8]), 1, (0, 255, 255), 4)
+                cv2.circle(img_raw, (b[9], b[10]), 1, (255, 0, 255), 4)
+                cv2.circle(img_raw, (b[11], b[12]), 1, (0, 255, 0), 4)
+                cv2.circle(img_raw, (b[13], b[14]), 1, (255, 0, 0), 4)
+            # save image
+            if not os.path.exists("./results/"):
+                os.makedirs("./results/")
+            name = "./results/" + str(i) + ".jpg"
+            cv2.imwrite(name, img_raw)
+
diff --git a/train.py b/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6cbf473b707c784051f134a5c524b96ca35983c
--- /dev/null
+++ b/train.py
@@ -0,0 +1,160 @@
+from __future__ import print_function
+import os
+import torch
+import torch.optim as optim
+import torch.backends.cudnn as cudnn
+import argparse
+import torch.utils.data as data
+from data import WiderFaceDetection, detection_collate, preproc, cfg_mnet, cfg_re50
+from layers.modules import MultiBoxLoss
+from layers.functions.prior_box import PriorBox
+import time
+import datetime
+import math
+from models.retinaface import RetinaFace
+
+parser = argparse.ArgumentParser(description='Retinaface Training')
+parser.add_argument('--training_dataset', default='./dataset/widerface/widerface/train/label.txt', help='Training dataset directory')
+parser.add_argument('--network', default='mobile0.25', help='Backbone network mobile0.25 or resnet50')
+parser.add_argument('--num_workers', default=4, type=int, help='Number of workers used in dataloading')
+parser.add_argument('--lr', '--learning-rate', default=1e-3, type=float, help='initial learning rate')
+parser.add_argument('--momentum', default=0.9, type=float, help='momentum')
+parser.add_argument('--resume_net', default=None, help='resume net for retraining')
+parser.add_argument('--resume_epoch', default=0, type=int, help='resume iter for retraining')
+parser.add_argument('--weight_decay', default=5e-4, type=float, help='Weight decay for SGD')
+parser.add_argument('--gamma', default=0.1, type=float, help='Gamma update for SGD')
+parser.add_argument('--save_folder', default='./weights/', help='Location to save checkpoint models')
+
+args = parser.parse_args()
+
+if not os.path.exists(args.save_folder):
+    os.mkdir(args.save_folder)
+cfg = None
+if args.network == "mobile0.25":
+    cfg = cfg_mnet
+elif args.network == "resnet50":
+    cfg = cfg_re50
+
+rgb_mean = (104, 117, 123) # bgr order
+num_classes = 2
+img_dim = cfg['image_size']
+num_gpu = cfg['ngpu']
+batch_size = cfg['batch_size']
+max_epoch = cfg['epoch']
+gpu_train = cfg['gpu_train']
+
+num_workers = args.num_workers
+momentum = args.momentum
+weight_decay = args.weight_decay
+initial_lr = args.lr
+gamma = args.gamma
+training_dataset = args.training_dataset
+save_folder = args.save_folder
+
+net = RetinaFace(cfg=cfg)
+print("Printing net...")
+print(net)
+
+if args.resume_net is not None:
+    print('Loading resume network...')
+    state_dict = torch.load(args.resume_net)
+    # create new OrderedDict that does not contain `module.`
+    from collections import OrderedDict
+    new_state_dict = OrderedDict()
+    for k, v in state_dict.items():
+        head = k[:7]
+        if head == 'module.':
+            name = k[7:] # remove `module.`
+        else:
+            name = k
+        new_state_dict[name] = v
+    net.load_state_dict(new_state_dict)
+
+if num_gpu > 1 and gpu_train:
+    net = torch.nn.DataParallel(net).cuda()
+else:
+    net = net.cuda()
+
+cudnn.benchmark = True
+
+
+optimizer = optim.SGD(net.parameters(), lr=initial_lr, momentum=momentum, weight_decay=weight_decay)
+criterion = MultiBoxLoss(num_classes, 0.35, True, 0, True, 7, 0.35, False)
+
+priorbox = PriorBox(cfg, image_size=(img_dim, img_dim))
+with torch.no_grad():
+    priors = priorbox.forward()
+    priors = priors.cuda()
+
+def train():
+    net.train()
+    epoch = 0 + args.resume_epoch
+    print('Loading Dataset...')
+
+    dataset = WiderFaceDetection( training_dataset,preproc(img_dim, rgb_mean))
+
+    epoch_size = math.ceil(len(dataset) / batch_size)
+    max_iter = max_epoch * epoch_size
+
+    stepvalues = (cfg['decay1'] * epoch_size, cfg['decay2'] * epoch_size)
+    step_index = 0
+
+    if args.resume_epoch > 0:
+        start_iter = args.resume_epoch * epoch_size
+    else:
+        start_iter = 0
+
+    for iteration in range(start_iter, max_iter):
+        if iteration % epoch_size == 0:
+            # create batch iterator
+            batch_iterator = iter(data.DataLoader(dataset, batch_size, shuffle=True, num_workers=num_workers, collate_fn=detection_collate))
+            if (epoch % 10 == 0 and epoch > 0) or (epoch % 5 == 0 and epoch > cfg['decay1']):
+                torch.save(net.state_dict(), save_folder + cfg['name']+ '_epoch_' + str(epoch) + '.pth')
+            epoch += 1
+
+        load_t0 = time.time()
+        if iteration in stepvalues:
+            step_index += 1
+        lr = adjust_learning_rate(optimizer, gamma, epoch, step_index, iteration, epoch_size)
+
+        # load train data
+        images, targets = next(batch_iterator)
+        images = images.cuda()
+        targets = [anno.cuda() for anno in targets]
+
+        # forward
+        out = net(images)
+
+        # backprop
+        optimizer.zero_grad()
+        loss_l, loss_c, loss_landm = criterion(out, priors, targets)
+        loss = cfg['loc_weight'] * loss_l + loss_c + loss_landm
+        loss.backward()
+        optimizer.step()
+        load_t1 = time.time()
+        batch_time = load_t1 - load_t0
+        eta = int(batch_time * (max_iter - iteration))
+        print('Epoch:{}/{} || Epochiter: {}/{} || Iter: {}/{} || Loc: {:.4f} Cla: {:.4f} Landm: {:.4f} || LR: {:.8f} || Batchtime: {:.4f} s || ETA: {}'
+              .format(epoch, max_epoch, (iteration % epoch_size) + 1,
+              epoch_size, iteration + 1, max_iter, loss_l.item(), loss_c.item(), loss_landm.item(), lr, batch_time, str(datetime.timedelta(seconds=eta))))
+
+    torch.save(net.state_dict(), save_folder + cfg['name'] + '_Final.pth')
+    # torch.save(net.state_dict(), save_folder + 'Final_Retinaface.pth')
+
+
+def adjust_learning_rate(optimizer, gamma, epoch, step_index, iteration, epoch_size):
+    """Sets the learning rate
+    # Adapted from PyTorch Imagenet example:
+    # https://github.com/pytorch/examples/blob/master/imagenet/main.py
+    """
+    warmup_epoch = -1
+    if epoch <= warmup_epoch:
+        lr = 1e-6 + (initial_lr-1e-6) * iteration / (epoch_size * warmup_epoch)
+    else:
+        lr = initial_lr * (gamma ** (step_index))
+    for param_group in optimizer.param_groups:
+        param_group['lr'] = lr
+    return lr
+
+if __name__ == '__main__':
+    train()
diff --git a/utils/__init__.py b/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/utils/__pycache__/__init__.cpython-38.pyc b/utils/__pycache__/__init__.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cc2a16ff2015fd8bd43782fd55fa64975325d94a
Binary files /dev/null and b/utils/__pycache__/__init__.cpython-38.pyc differ
diff --git a/utils/__pycache__/box_utils.cpython-38.pyc b/utils/__pycache__/box_utils.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7891f46a8ea0881e81bd57a137cff702fb58073d
Binary files /dev/null and b/utils/__pycache__/box_utils.cpython-38.pyc differ
diff --git a/utils/box_utils.py b/utils/box_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ad336dd1b66339987f0d26745c804b92e299cf1
--- /dev/null
+++ b/utils/box_utils.py
@@ -0,0 +1,330 @@
+import torch
+import numpy as np
+
+
+def point_form(boxes):
+    """ Convert prior_boxes to (xmin, ymin, xmax, ymax)
+    representation for comparison to point form ground truth data.
+    Args:
+        boxes: (tensor) center-size default boxes from priorbox layers.
+    Return:
+        boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
+    """
+    return torch.cat((boxes[:, :2] - boxes[:, 2:]/2,     # xmin, ymin
+                     boxes[:, :2] + boxes[:, 2:]/2), 1)  # xmax, ymax
+
+
+def center_size(boxes):
+    """ Convert prior_boxes to (cx, cy, w, h)
+    representation for comparison to center-size form ground truth data.
+    Args:
+        boxes: (tensor) point_form boxes
+    Return:
+        boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
+    """
+    return torch.cat((boxes[:, 2:] + boxes[:, :2])/2,  # cx, cy
+                     boxes[:, 2:] - boxes[:, :2], 1)  # w, h
+
+
+def intersect(box_a, box_b):
+    """ We resize both tensors to [A,B,2] without new malloc:
+    [A,2] -> [A,1,2] -> [A,B,2]
+    [B,2] -> [1,B,2] -> [A,B,2]
+    Then we compute the area of intersect between box_a and box_b.
+    Args:
+      box_a: (tensor) bounding boxes, Shape: [A,4].
+      box_b: (tensor) bounding boxes, Shape: [B,4].
+    Return:
+      (tensor) intersection area, Shape: [A,B].
+    """
+    A = box_a.size(0)
+    B = box_b.size(0)
+    max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2),
+                       box_b[:, 2:].unsqueeze(0).expand(A, B, 2))
+    min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2),
+                       box_b[:, :2].unsqueeze(0).expand(A, B, 2))
+    inter = torch.clamp((max_xy - min_xy), min=0)
+    return inter[:, :, 0] * inter[:, :, 1]
+
+
+def jaccard(box_a, box_b):
+    """Compute the jaccard overlap of two sets of boxes.  The jaccard overlap
+    is simply the intersection over union of two boxes.  Here we operate on
+    ground truth boxes and default boxes.
+    E.g.:
+        A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B)
+    Args:
+        box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4]
+        box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4]
+    Return:
+        jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)]
+    """
+    inter = intersect(box_a, box_b)
+    area_a = ((box_a[:, 2]-box_a[:, 0]) *
+              (box_a[:, 3]-box_a[:, 1])).unsqueeze(1).expand_as(inter)  # [A,B]
+    area_b = ((box_b[:, 2]-box_b[:, 0]) *
+              (box_b[:, 3]-box_b[:, 1])).unsqueeze(0).expand_as(inter)  # [A,B]
+    union = area_a + area_b - inter
+    return inter / union  # [A,B]
+
+
+def matrix_iou(a, b):
+    """
+    return iou of a and b, numpy version for data augenmentation
+    """
+    lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])
+    rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
+
+    area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)
+    area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
+    area_b = np.prod(b[:, 2:] - b[:, :2], axis=1)
+    return area_i / (area_a[:, np.newaxis] + area_b - area_i)
+
+
+def matrix_iof(a, b):
+    """
+    return iof of a and b, numpy version for data augenmentation
+    """
+    lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])
+    rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
+
+    area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)
+    area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
+    return area_i / np.maximum(area_a[:, np.newaxis], 1)
+
+
+def match(threshold, truths, priors, variances, labels, landms, loc_t, conf_t, landm_t, idx):
+    """Match each prior box with the ground truth box of the highest jaccard
+    overlap, encode the bounding boxes, then return the matched indices
+    corresponding to both confidence and location preds.
+    Args:
+        threshold: (float) The overlap threshold used when mathing boxes.
+        truths: (tensor) Ground truth boxes, Shape: [num_obj, 4].
+        priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4].
+        variances: (tensor) Variances corresponding to each prior coord,
+            Shape: [num_priors, 4].
+        labels: (tensor) All the class labels for the image, Shape: [num_obj].
+        landms: (tensor) Ground truth landms, Shape [num_obj, 10].
+        loc_t: (tensor) Tensor to be filled w/ endcoded location targets.
+        conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds.
+        landm_t: (tensor) Tensor to be filled w/ endcoded landm targets.
+        idx: (int) current batch index
+    Return:
+        The matched indices corresponding to 1)location 2)confidence 3)landm preds.
+    """
+    # jaccard index
+    overlaps = jaccard(
+        truths,
+        point_form(priors)
+    )
+    # (Bipartite Matching)
+    # [1,num_objects] best prior for each ground truth
+    best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)
+
+    # ignore hard gt
+    valid_gt_idx = best_prior_overlap[:, 0] >= 0.2
+    best_prior_idx_filter = best_prior_idx[valid_gt_idx, :]
+    if best_prior_idx_filter.shape[0] <= 0:
+        loc_t[idx] = 0
+        conf_t[idx] = 0
+        return
+
+    # [1,num_priors] best ground truth for each prior
+    best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True)
+    best_truth_idx.squeeze_(0)
+    best_truth_overlap.squeeze_(0)
+    best_prior_idx.squeeze_(1)
+    best_prior_idx_filter.squeeze_(1)
+    best_prior_overlap.squeeze_(1)
+    best_truth_overlap.index_fill_(0, best_prior_idx_filter, 2)  # ensure best prior
+    # TODO refactor: index  best_prior_idx with long tensor
+    # ensure every gt matches with its prior of max overlap
+    for j in range(best_prior_idx.size(0)):     # 判别此anchor是预测哪一个boxes
+        best_truth_idx[best_prior_idx[j]] = j
+    matches = truths[best_truth_idx]            # Shape: [num_priors,4] 此处为每一个anchor对应的bbox取出来
+    conf = labels[best_truth_idx]               # Shape: [num_priors]      此处为每一个anchor对应的label取出来
+    conf[best_truth_overlap < threshold] = 0    # label as background   overlap<0.35的全部作为负样本
+    loc = encode(matches, priors, variances)
+
+    matches_landm = landms[best_truth_idx]
+    landm = encode_landm(matches_landm, priors, variances)
+    loc_t[idx] = loc    # [num_priors,4] encoded offsets to learn
+    conf_t[idx] = conf  # [num_priors] top class label for each prior
+    landm_t[idx] = landm
+
+
+def encode(matched, priors, variances):
+    """Encode the variances from the priorbox layers into the ground truth boxes
+    we have matched (based on jaccard overlap) with the prior boxes.
+    Args:
+        matched: (tensor) Coords of ground truth for each prior in point-form
+            Shape: [num_priors, 4].
+        priors: (tensor) Prior boxes in center-offset form
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        encoded boxes (tensor), Shape: [num_priors, 4]
+    """
+
+    # dist b/t match center and prior's center
+    g_cxcy = (matched[:, :2] + matched[:, 2:])/2 - priors[:, :2]
+    # encode variance
+    g_cxcy /= (variances[0] * priors[:, 2:])
+    # match wh / prior wh
+    g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]
+    g_wh = torch.log(g_wh) / variances[1]
+    # return target for smooth_l1_loss
+    return torch.cat([g_cxcy, g_wh], 1)  # [num_priors,4]
+
+def encode_landm(matched, priors, variances):
+    """Encode the variances from the priorbox layers into the ground truth boxes
+    we have matched (based on jaccard overlap) with the prior boxes.
+    Args:
+        matched: (tensor) Coords of ground truth for each prior in point-form
+            Shape: [num_priors, 10].
+        priors: (tensor) Prior boxes in center-offset form
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        encoded landm (tensor), Shape: [num_priors, 10]
+    """
+
+    # dist b/t match center and prior's center
+    matched = torch.reshape(matched, (matched.size(0), 5, 2))
+    priors_cx = priors[:, 0].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+    priors_cy = priors[:, 1].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+    priors_w = priors[:, 2].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+    priors_h = priors[:, 3].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+    priors = torch.cat([priors_cx, priors_cy, priors_w, priors_h], dim=2)
+    g_cxcy = matched[:, :, :2] - priors[:, :, :2]
+    # encode variance
+    g_cxcy /= (variances[0] * priors[:, :, 2:])
+    # g_cxcy /= priors[:, :, 2:]
+    g_cxcy = g_cxcy.reshape(g_cxcy.size(0), -1)
+    # return target for smooth_l1_loss
+    return g_cxcy
+
+
+# Adapted from https://github.com/Hakuyume/chainer-ssd
+def decode(loc, priors, variances):
+    """Decode locations from predictions using priors to undo
+    the encoding we did for offset regression at train time.
+    Args:
+        loc (tensor): location predictions for loc layers,
+            Shape: [num_priors,4]
+        priors (tensor): Prior boxes in center-offset form.
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        decoded bounding box predictions
+    """
+
+    boxes = torch.cat((
+        priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
+        priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
+    boxes[:, :2] -= boxes[:, 2:] / 2
+    boxes[:, 2:] += boxes[:, :2]
+    return boxes
+
+def decode_landm(pre, priors, variances):
+    """Decode landm from predictions using priors to undo
+    the encoding we did for offset regression at train time.
+    Args:
+        pre (tensor): landm predictions for loc layers,
+            Shape: [num_priors,10]
+        priors (tensor): Prior boxes in center-offset form.
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        decoded landm predictions
+    """
+    landms = torch.cat((priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:],
+                        priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:],
+                        priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:],
+                        priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:],
+                        priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:],
+                        ), dim=1)
+    return landms
+
+
+def log_sum_exp(x):
+    """Utility function for computing log_sum_exp while determining
+    This will be used to determine unaveraged confidence loss across
+    all examples in a batch.
+    Args:
+        x (Variable(tensor)): conf_preds from conf layers
+    """
+    x_max = x.data.max()
+    return torch.log(torch.sum(torch.exp(x-x_max), 1, keepdim=True)) + x_max
+
+
+# Original author: Francisco Massa:
+# https://github.com/fmassa/object-detection.torch
+# Ported to PyTorch by Max deGroot (02/01/2017)
+def nms(boxes, scores, overlap=0.5, top_k=200):
+    """Apply non-maximum suppression at test time to avoid detecting too many
+    overlapping bounding boxes for a given object.
+    Args:
+        boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
+        scores: (tensor) The class predscores for the img, Shape:[num_priors].
+        overlap: (float) The overlap thresh for suppressing unnecessary boxes.
+        top_k: (int) The Maximum number of box preds to consider.
+    Return:
+        The indices of the kept boxes with respect to num_priors.
+    """
+
+    keep = torch.Tensor(scores.size(0)).fill_(0).long()
+    if boxes.numel() == 0:
+        return keep
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2]
+    y2 = boxes[:, 3]
+    area = torch.mul(x2 - x1, y2 - y1)
+    v, idx = scores.sort(0)  # sort in ascending order
+    # I = I[v >= 0.01]
+    idx = idx[-top_k:]  # indices of the top-k largest vals
+    xx1 = boxes.new()
+    yy1 = boxes.new()
+    xx2 = boxes.new()
+    yy2 = boxes.new()
+    w = boxes.new()
+    h = boxes.new()
+
+    # keep = torch.Tensor()
+    count = 0
+    while idx.numel() > 0:
+        i = idx[-1]  # index of current largest val
+        # keep.append(i)
+        keep[count] = i
+        count += 1
+        if idx.size(0) == 1:
+            break
+        idx = idx[:-1]  # remove kept element from view
+        # load bboxes of next highest vals
+        torch.index_select(x1, 0, idx, out=xx1)
+        torch.index_select(y1, 0, idx, out=yy1)
+        torch.index_select(x2, 0, idx, out=xx2)
+        torch.index_select(y2, 0, idx, out=yy2)
+        # store element-wise max with next highest score
+        xx1 = torch.clamp(xx1, min=x1[i])
+        yy1 = torch.clamp(yy1, min=y1[i])
+        xx2 = torch.clamp(xx2, max=x2[i])
+        yy2 = torch.clamp(yy2, max=y2[i])
+        w.resize_as_(xx2)
+        h.resize_as_(yy2)
+        w = xx2 - xx1
+        h = yy2 - yy1
+        # check sizes of xx1 and xx2.. after each iteration
+        w = torch.clamp(w, min=0.0)
+        h = torch.clamp(h, min=0.0)
+        inter = w*h
+        # IoU = i / (area(a) + area(b) - i)
+        rem_areas = torch.index_select(area, 0, idx)  # load remaining areas)
+        union = (rem_areas - inter) + area[i]
+        IoU = inter/union  # store result in iou
+        # keep only elements with an IoU <= overlap
+        idx = idx[IoU.le(overlap)]
+    return keep, count
+
+
diff --git a/utils/nms/__init__.py b/utils/nms/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/utils/nms/__pycache__/__init__.cpython-38.pyc b/utils/nms/__pycache__/__init__.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4257f1103b267e3f8663f991aa11710f7c26b05f
Binary files /dev/null and b/utils/nms/__pycache__/__init__.cpython-38.pyc differ
diff --git a/utils/nms/__pycache__/py_cpu_nms.cpython-38.pyc b/utils/nms/__pycache__/py_cpu_nms.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e6de018e4e206fb35ce875ad6f3eef3d4fd5aba2
Binary files /dev/null and b/utils/nms/__pycache__/py_cpu_nms.cpython-38.pyc differ
diff --git a/utils/nms/py_cpu_nms.py b/utils/nms/py_cpu_nms.py
new file mode 100644
index 0000000000000000000000000000000000000000..d55d936c30d46689f4361b4d779ba4e7872628b8
--- /dev/null
+++ b/utils/nms/py_cpu_nms.py
@@ -0,0 +1,38 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+import numpy as np
+
+def py_cpu_nms(dets, thresh):
+    """Pure Python NMS baseline."""
+    x1 = dets[:, 0]
+    y1 = dets[:, 1]
+    x2 = dets[:, 2]
+    y2 = dets[:, 3]
+    scores = dets[:, 4]
+
+    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+    order = scores.argsort()[::-1]
+
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+        xx1 = np.maximum(x1[i], x1[order[1:]])
+        yy1 = np.maximum(y1[i], y1[order[1:]])
+        xx2 = np.minimum(x2[i], x2[order[1:]])
+        yy2 = np.minimum(y2[i], y2[order[1:]])
+
+        w = np.maximum(0.0, xx2 - xx1 + 1)
+        h = np.maximum(0.0, yy2 - yy1 + 1)
+        inter = w * h
+        ovr = inter / (areas[i] + areas[order[1:]] - inter)
+
+        inds = np.where(ovr <= thresh)[0]
+        order = order[inds + 1]
+
+    return keep
diff --git a/utils/timer.py b/utils/timer.py
new file mode 100644
index 0000000000000000000000000000000000000000..18954fa6572fe979612fbf8926f23f0f79a78c8e
--- /dev/null
+++ b/utils/timer.py
@@ -0,0 +1,40 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+import time
+
+
+class Timer(object):
+    """A simple timer."""
+    def __init__(self):
+        self.total_time = 0.
+        self.calls = 0
+        self.start_time = 0.
+        self.diff = 0.
+        self.average_time = 0.
+
+    def tic(self):
+        # using time.time instead of time.clock because time time.clock
+        # does not normalize for multithreading
+        self.start_time = time.time()
+
+    def toc(self, average=True):
+        self.diff = time.time() - self.start_time
+        self.total_time += self.diff
+        self.calls += 1
+        self.average_time = self.total_time / self.calls
+        if average:
+            return self.average_time
+        else:
+            return self.diff
+
+    def clear(self):
+        self.total_time = 0.
+        self.calls = 0
+        self.start_time = 0.
+        self.diff = 0.
+        self.average_time = 0.
diff --git a/widerface_evaluate/README.md b/widerface_evaluate/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..79f7b4249887b5ff9c3778342b71dadbe9842216
--- /dev/null
+++ b/widerface_evaluate/README.md
@@ -0,0 +1,27 @@
+# WiderFace-Evaluation
+Python Evaluation Code for [Wider Face Dataset](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/)
+
+
+## Usage
+
+
+##### before evaluating ....
+
+````
+python3 setup.py build_ext --inplace
+````
+
+##### evaluating
+
+**GroungTruth:** `wider_face_val.mat`, `wider_easy_val.mat`, `wider_medium_val.mat`,`wider_hard_val.mat`
+
+````
+python3 evaluation.py -p <your prediction dir> -g <groud truth dir>
+````
+
+## Bugs & Problems
+please issue
+
+## Acknowledgements
+
+some code borrowed from Sergey Karayev
diff --git a/widerface_evaluate/box_overlaps.pyx b/widerface_evaluate/box_overlaps.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..8c410371117339a4da95c3f3d215a584e0e19465
--- /dev/null
+++ b/widerface_evaluate/box_overlaps.pyx
@@ -0,0 +1,55 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Sergey Karayev
+# --------------------------------------------------------
+
+cimport cython
+import numpy as np
+cimport numpy as np
+
+DTYPE = np.float
+ctypedef np.float_t DTYPE_t
+
+def bbox_overlaps(
+        np.ndarray[DTYPE_t, ndim=2] boxes,
+        np.ndarray[DTYPE_t, ndim=2] query_boxes):
+    """
+    Parameters
+    ----------
+    boxes: (N, 4) ndarray of float
+    query_boxes: (K, 4) ndarray of float
+    Returns
+    -------
+    overlaps: (N, K) ndarray of overlap between boxes and query_boxes
+    """
+    cdef unsigned int N = boxes.shape[0]
+    cdef unsigned int K = query_boxes.shape[0]
+    cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE)
+    cdef DTYPE_t iw, ih, box_area
+    cdef DTYPE_t ua
+    cdef unsigned int k, n
+    for k in range(K):
+        box_area = (
+            (query_boxes[k, 2] - query_boxes[k, 0] + 1) *
+            (query_boxes[k, 3] - query_boxes[k, 1] + 1)
+        )
+        for n in range(N):
+            iw = (
+                min(boxes[n, 2], query_boxes[k, 2]) -
+                max(boxes[n, 0], query_boxes[k, 0]) + 1
+            )
+            if iw > 0:
+                ih = (
+                    min(boxes[n, 3], query_boxes[k, 3]) -
+                    max(boxes[n, 1], query_boxes[k, 1]) + 1
+                )
+                if ih > 0:
+                    ua = float(
+                        (boxes[n, 2] - boxes[n, 0] + 1) *
+                        (boxes[n, 3] - boxes[n, 1] + 1) +
+                        box_area - iw * ih
+                    )
+                    overlaps[n, k] = iw * ih / ua
+    return overlaps
\ No newline at end of file
diff --git a/widerface_evaluate/evaluation.py b/widerface_evaluate/evaluation.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c2adf14b3a967b7f9b8c191d6c4499fb096d4d4
--- /dev/null
+++ b/widerface_evaluate/evaluation.py
@@ -0,0 +1,303 @@
+"""
+WiderFace evaluation code
+author: wondervictor
+mail: tianhengcheng@gmail.com
+copyright@wondervictor
+"""
+
+import os
+import tqdm
+import pickle
+import argparse
+import numpy as np
+from scipy.io import loadmat
+from bbox import bbox_overlaps
+from IPython import embed
+
+
+def get_gt_boxes(gt_dir):
+    """ gt dir: (wider_face_val.mat, wider_easy_val.mat, wider_medium_val.mat, wider_hard_val.mat)"""
+
+    gt_mat = loadmat(os.path.join(gt_dir, 'wider_face_val.mat'))
+    hard_mat = loadmat(os.path.join(gt_dir, 'wider_hard_val.mat'))
+    medium_mat = loadmat(os.path.join(gt_dir, 'wider_medium_val.mat'))
+    easy_mat = loadmat(os.path.join(gt_dir, 'wider_easy_val.mat'))
+
+    facebox_list = gt_mat['face_bbx_list']
+    event_list = gt_mat['event_list']
+    file_list = gt_mat['file_list']
+
+    hard_gt_list = hard_mat['gt_list']
+    medium_gt_list = medium_mat['gt_list']
+    easy_gt_list = easy_mat['gt_list']
+
+    return facebox_list, event_list, file_list, hard_gt_list, medium_gt_list, easy_gt_list
+
+
+def get_gt_boxes_from_txt(gt_path, cache_dir):
+
+    cache_file = os.path.join(cache_dir, 'gt_cache.pkl')
+    if os.path.exists(cache_file):
+        f = open(cache_file, 'rb')
+        boxes = pickle.load(f)
+        f.close()
+        return boxes
+
+    f = open(gt_path, 'r')
+    state = 0
+    lines = f.readlines()
+    lines = list(map(lambda x: x.rstrip('\r\n'), lines))
+    boxes = {}
+    print(len(lines))
+    f.close()
+    current_boxes = []
+    current_name = None
+    for line in lines:
+        if state == 0 and '--' in line:
+            state = 1
+            current_name = line
+            continue
+        if state == 1:
+            state = 2
+            continue
+
+        if state == 2 and '--' in line:
+            state = 1
+            boxes[current_name] = np.array(current_boxes).astype('float32')
+            current_name = line
+            current_boxes = []
+            continue
+
+        if state == 2:
+            box = [float(x) for x in line.split(' ')[:4]]
+            current_boxes.append(box)
+            continue
+
+    f = open(cache_file, 'wb')
+    pickle.dump(boxes, f)
+    f.close()
+    return boxes
+
+
+def read_pred_file(filepath):
+
+    with open(filepath, 'r') as f:
+        lines = f.readlines()
+        img_file = lines[0].rstrip('\n\r')
+        lines = lines[2:]
+
+    # b = lines[0].rstrip('\r\n').split(' ')[:-1]
+    # c = float(b)
+    # a = map(lambda x: [[float(a[0]), float(a[1]), float(a[2]), float(a[3]), float(a[4])] for a in x.rstrip('\r\n').split(' ')], lines)
+    boxes = []
+    for line in lines:
+        line = line.rstrip('\r\n').split(' ')
+        if line[0] is '':
+            continue
+        # a = float(line[4])
+        boxes.append([float(line[0]), float(line[1]), float(line[2]), float(line[3]), float(line[4])])
+    boxes = np.array(boxes)
+    # boxes = np.array(list(map(lambda x: [float(a) for a in x.rstrip('\r\n').split(' ')], lines))).astype('float')
+    return img_file.split('/')[-1], boxes
+
+
+def get_preds(pred_dir):
+    events = os.listdir(pred_dir)
+    boxes = dict()
+    pbar = tqdm.tqdm(events)
+
+    for event in pbar:
+        pbar.set_description('Reading Predictions ')
+        event_dir = os.path.join(pred_dir, event)
+        event_images = os.listdir(event_dir)
+        current_event = dict()
+        for imgtxt in event_images:
+            imgname, _boxes = read_pred_file(os.path.join(event_dir, imgtxt))
+            current_event[imgname.rstrip('.jpg')] = _boxes
+        boxes[event] = current_event
+    return boxes
+
+
+def norm_score(pred):
+    """ norm score
+    pred {key: [[x1,y1,x2,y2,s]]}
+    """
+
+    max_score = 0
+    min_score = 1
+
+    for _, k in pred.items():
+        for _, v in k.items():
+            if len(v) == 0:
+                continue
+            _min = np.min(v[:, -1])
+            _max = np.max(v[:, -1])
+            max_score = max(_max, max_score)
+            min_score = min(_min, min_score)
+
+    diff = max_score - min_score
+    for _, k in pred.items():
+        for _, v in k.items():
+            if len(v) == 0:
+                continue
+            v[:, -1] = (v[:, -1] - min_score)/diff
+
+
+def image_eval(pred, gt, ignore, iou_thresh):
+    """ single image evaluation
+    pred: Nx5
+    gt: Nx4
+    ignore:
+    """
+
+    _pred = pred.copy()
+    _gt = gt.copy()
+    pred_recall = np.zeros(_pred.shape[0])
+    recall_list = np.zeros(_gt.shape[0])
+    proposal_list = np.ones(_pred.shape[0])
+
+    _pred[:, 2] = _pred[:, 2] + _pred[:, 0]
+    _pred[:, 3] = _pred[:, 3] + _pred[:, 1]
+    _gt[:, 2] = _gt[:, 2] + _gt[:, 0]
+    _gt[:, 3] = _gt[:, 3] + _gt[:, 1]
+
+    overlaps = bbox_overlaps(_pred[:, :4], _gt)
+
+    for h in range(_pred.shape[0]):
+
+        gt_overlap = overlaps[h]
+        max_overlap, max_idx = gt_overlap.max(), gt_overlap.argmax()
+        if max_overlap >= iou_thresh:
+            if ignore[max_idx] == 0:
+                recall_list[max_idx] = -1
+                proposal_list[h] = -1
+            elif recall_list[max_idx] == 0:
+                recall_list[max_idx] = 1
+
+        r_keep_index = np.where(recall_list == 1)[0]
+        pred_recall[h] = len(r_keep_index)
+    return pred_recall, proposal_list
+
+
+def img_pr_info(thresh_num, pred_info, proposal_list, pred_recall):
+    pr_info = np.zeros((thresh_num, 2)).astype('float')
+    for t in range(thresh_num):
+
+        thresh = 1 - (t+1)/thresh_num
+        r_index = np.where(pred_info[:, 4] >= thresh)[0]
+        if len(r_index) == 0:
+            pr_info[t, 0] = 0
+            pr_info[t, 1] = 0
+        else:
+            r_index = r_index[-1]
+            p_index = np.where(proposal_list[:r_index+1] == 1)[0]
+            pr_info[t, 0] = len(p_index)
+            pr_info[t, 1] = pred_recall[r_index]
+    return pr_info
+
+
+def dataset_pr_info(thresh_num, pr_curve, count_face):
+    _pr_curve = np.zeros((thresh_num, 2))
+    for i in range(thresh_num):
+        _pr_curve[i, 0] = pr_curve[i, 1] / pr_curve[i, 0]
+        _pr_curve[i, 1] = pr_curve[i, 1] / count_face
+    return _pr_curve
+
+
+def voc_ap(rec, prec):
+
+    # correct AP calculation
+    # first append sentinel values at the end
+    mrec = np.concatenate(([0.], rec, [1.]))
+    mpre = np.concatenate(([0.], prec, [0.]))
+
+    # compute the precision envelope
+    for i in range(mpre.size - 1, 0, -1):
+        mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
+
+    # to calculate area under PR curve, look for points
+    # where X axis (recall) changes value
+    i = np.where(mrec[1:] != mrec[:-1])[0]
+
+    # and sum (\Delta recall) * prec
+    ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
+    return ap
+
+
+def evaluation(pred, gt_path, iou_thresh=0.5):
+    pred = get_preds(pred)
+    norm_score(pred)
+    facebox_list, event_list, file_list, hard_gt_list, medium_gt_list, easy_gt_list = get_gt_boxes(gt_path)
+    event_num = len(event_list)
+    thresh_num = 1000
+    settings = ['easy', 'medium', 'hard']
+    setting_gts = [easy_gt_list, medium_gt_list, hard_gt_list]
+    aps = []
+    for setting_id in range(3):
+        # different setting
+        gt_list = setting_gts[setting_id]
+        count_face = 0
+        pr_curve = np.zeros((thresh_num, 2)).astype('float')
+        # [hard, medium, easy]
+        pbar = tqdm.tqdm(range(event_num))
+        for i in pbar:
+            pbar.set_description('Processing {}'.format(settings[setting_id]))
+            event_name = str(event_list[i][0][0])
+            img_list = file_list[i][0]
+            pred_list = pred[event_name]
+            sub_gt_list = gt_list[i][0]
+            # img_pr_info_list = np.zeros((len(img_list), thresh_num, 2))
+            gt_bbx_list = facebox_list[i][0]
+
+            for j in range(len(img_list)):
+                pred_info = pred_list[str(img_list[j][0][0])]
+
+                gt_boxes = gt_bbx_list[j][0].astype('float')
+                keep_index = sub_gt_list[j][0]
+                count_face += len(keep_index)
+
+                if len(gt_boxes) == 0 or len(pred_info) == 0:
+                    continue
+                ignore = np.zeros(gt_boxes.shape[0])
+                if len(keep_index) != 0:
+                    ignore[keep_index-1] = 1
+                pred_recall, proposal_list = image_eval(pred_info, gt_boxes, ignore, iou_thresh)
+
+                _img_pr_info = img_pr_info(thresh_num, pred_info, proposal_list, pred_recall)
+
+                pr_curve += _img_pr_info
+        pr_curve = dataset_pr_info(thresh_num, pr_curve, count_face)
+
+        propose = pr_curve[:, 0]
+        recall = pr_curve[:, 1]
+
+        ap = voc_ap(recall, propose)
+        aps.append(ap)
+
+    print("==================== Results ====================")
+    print("Easy   Val AP: {}".format(aps[0]))
+    print("Medium Val AP: {}".format(aps[1]))
+    print("Hard   Val AP: {}".format(aps[2]))
+    print("=================================================")
+
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-p', '--pred', default="./widerface_txt/")
+    parser.add_argument('-g', '--gt', default='./ground_truth/')
+
+    args = parser.parse_args()
+    evaluation(args.pred, args.gt)
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/widerface_evaluate/ground_truth/wider_easy_val.mat b/widerface_evaluate/ground_truth/wider_easy_val.mat
new file mode 100644
index 0000000000000000000000000000000000000000..5b78df7c006982ab1a2038149f1f5168b2237f07
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diff --git a/widerface_evaluate/ground_truth/wider_face_val.mat b/widerface_evaluate/ground_truth/wider_face_val.mat
new file mode 100644
index 0000000000000000000000000000000000000000..7ac47e55b72d04c5fa237321509b2d2319f8ded2
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diff --git a/widerface_evaluate/ground_truth/wider_hard_val.mat b/widerface_evaluate/ground_truth/wider_hard_val.mat
new file mode 100644
index 0000000000000000000000000000000000000000..061fcab0761d4fb6a0574756ae5c3d4591aecfc7
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diff --git a/widerface_evaluate/ground_truth/wider_medium_val.mat b/widerface_evaluate/ground_truth/wider_medium_val.mat
new file mode 100644
index 0000000000000000000000000000000000000000..9670c7a96d8af29c59d6b08cab7765b8bc427182
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diff --git a/widerface_evaluate/setup.py b/widerface_evaluate/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f63489c005bc437709b7756da6a8cda5cb3baa6
--- /dev/null
+++ b/widerface_evaluate/setup.py
@@ -0,0 +1,13 @@
+"""
+WiderFace evaluation code
+author: wondervictor
+mail: tianhengcheng@gmail.com
+copyright@wondervictor
+"""
+
+from distutils.core import setup, Extension
+from Cython.Build import cythonize
+import numpy
+
+package = Extension('bbox', ['box_overlaps.pyx'], include_dirs=[numpy.get_include()])
+setup(ext_modules=cythonize([package]))