diff --git a/configs/ssd/README.md b/configs/ssd/README.md
index 9340e7e812986bbcc427f27a485b2ca9f376e50d..1ebc458669cfbc60216440c412aa1df7ac62602c 100644
--- a/configs/ssd/README.md
+++ b/configs/ssd/README.md
@@ -6,8 +6,8 @@
 
 | 骨架网络        | 网络类型       | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP |                           下载                          | 配置文件 |
 | :-------------- | :------------- | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: |
-| VGG             | SSD            |    8    |   240e    |     ----     |  77.8  | [下载链接](https://paddledet.bj.bcebos.com/models/ssd_vgg16_300_240e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ssd/ssd_vgg16_300_240e_voc.yml) |
-| MobileNet v1    | SSD            |    32    |   120e    |     ----     |  73.8  | [下载链接](https://paddledet.bj.bcebos.com/models/ssd_mobilenet_v1_300_120e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ssd/ssd_mobilenet_v1_300_120e_voc.yml) |
+| VGG             | SSD            |    8    |   240e    |     ----     |  77.8  | [下载链接](https://paddledet.bj.bcebos.com/models/ssd_vgg16_300_240e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ssd/ssd_vgg16_300_240e_voc.yml) |
+| MobileNet v1    | SSD            |    32    |   120e    |     ----     |  73.8  | [下载链接](https://paddledet.bj.bcebos.com/models/ssd_mobilenet_v1_300_120e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ssd/ssd_mobilenet_v1_300_120e_voc.yml) |
 
 **注意：** SSD-VGG使用4GPU在总batch size为32下训练240个epoch。SSD-MobileNetv1使用2GPU在总batch size为64下训练120周期。
 
diff --git a/ppdet/modeling/architectures/ssd.py b/ppdet/modeling/architectures/ssd.py
index 49b01bde0d6e4d399f35b43660d608ea44b06644..136e34fd5259486f7e922d455707e9f4d5019a06 100644
--- a/ppdet/modeling/architectures/ssd.py
+++ b/ppdet/modeling/architectures/ssd.py
@@ -24,6 +24,15 @@ __all__ = ['SSD']
 
 @register
 class SSD(BaseArch):
+    """
+    Single Shot MultiBox Detector, see https://arxiv.org/abs/1512.02325
+
+    Args:
+        backbone (nn.Layer): backbone instance
+        ssd_head (nn.Layer): `SSDHead` instance
+        post_process (object): `BBoxPostProcess` instance
+    """
+
     __category__ = 'architecture'
     __inject__ = ['post_process']
 
diff --git a/ppdet/modeling/heads/ssd_head.py b/ppdet/modeling/heads/ssd_head.py
index a280c01432049c82396f301736cf197006fdbc80..8cbbe0a460441b5414fec5826f21699834c960c0 100644
--- a/ppdet/modeling/heads/ssd_head.py
+++ b/ppdet/modeling/heads/ssd_head.py
@@ -68,6 +68,20 @@ class SepConvLayer(nn.Layer):
 
 @register
 class SSDHead(nn.Layer):
+    """
+    SSDHead
+
+    Args:
+        num_classes (int): Number of classes
+        in_channels (list): Number of channels per input feature
+        anchor_generator (dict): Configuration of 'AnchorGeneratorSSD' instance
+        kernel_size (int): Conv kernel size
+        padding (int): Conv padding
+        use_sepconv (bool): Use SepConvLayer if true
+        conv_decay (float): Conv regularization coeff
+        loss (object): 'SSDLoss' instance
+    """
+
     __shared__ = ['num_classes']
     __inject__ = ['anchor_generator', 'loss']
 
diff --git a/ppdet/modeling/losses/ssd_loss.py b/ppdet/modeling/losses/ssd_loss.py
index 04ba75b64c4eee4b4460e2acefb130a4741be81d..961a8c4e8d899c76c4baa50b82f133756488a0b9 100644
--- a/ppdet/modeling/losses/ssd_loss.py
+++ b/ppdet/modeling/losses/ssd_loss.py
@@ -28,6 +28,21 @@ __all__ = ['SSDLoss']
 
 @register
 class SSDLoss(nn.Layer):
+    """
+    SSDLoss
+
+    Args:
+        match_type (str): The type of matching method, should be
+           'bipartite' or 'per_prediction'. None ('bipartite') by default.
+        overlap_threshold (float32, optional): If `match_type` is 'per_prediction',
+            this threshold is to determine the extra matching bboxes based
+            on the maximum distance, 0.5 by default.
+        neg_pos_ratio (float): The ratio of negative samples / positive samples.
+        neg_overlap (float): The overlap threshold of negative samples.
+        loc_loss_weight (float): The weight of loc_loss.
+        conf_loss_weight (float): The weight of conf_loss.
+    """
+
     def __init__(self,
                  match_type='per_prediction',
                  overlap_threshold=0.5,