Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
PaddlePaddle
PaddleDetection
提交
744cb882
P
PaddleDetection
项目概览
PaddlePaddle
/
PaddleDetection
大约 1 年 前同步成功
通知
695
Star
11112
Fork
2696
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
184
列表
看板
标记
里程碑
合并请求
40
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
PaddleDetection
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
184
Issue
184
列表
看板
标记
里程碑
合并请求
40
合并请求
40
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
未验证
提交
744cb882
编写于
11月 01, 2019
作者:
K
Kaipeng Deng
提交者：
GitHub
11月 01, 2019
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
model zoo YOLOv3 add paper mAP (#14)
上级
a69ca0ec
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
42 addition
and
32 deletion
+42
-32
docs/MODEL_ZOO.md
docs/MODEL_ZOO.md
+22
-17
docs/MODEL_ZOO_cn.md
docs/MODEL_ZOO_cn.md
+20
-15
未找到文件。
docs/MODEL_ZOO.md
浏览文件 @
744cb882
...
@@ -95,24 +95,26 @@ The backbone models pretrained on ImageNet are available. All backbone models ar
...
@@ -95,24 +95,26 @@ The backbone models pretrained on ImageNet are available. All backbone models ar
-
Group Normalization reference from
[
Group Normalization
](
https://arxiv.org/abs/1803.08494
)
.
-
Group Normalization reference from
[
Group Normalization
](
https://arxiv.org/abs/1803.08494
)
.
-
Detailed configuration file in
[
configs/gn
](
https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/PaddleDetection/configs/gn
)
-
Detailed configuration file in
[
configs/gn
](
https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/PaddleDetection/configs/gn
)
### Y
olo
v3
### Y
OLO
v3
| Backbone | Pretrain dataset | Size | deformable Conv | Image/gpu | Lr schd | Inf time (fps) | Box AP | Download |
| Backbone | Pretrain dataset | Size | deformable Conv | Image/gpu | Lr schd | Inf time (fps) | Box AP | Download |
| :----------- | :--------: | :-----: | :-----: |:------------: |:----: | :-------: | :----: | :-------: |
| :----------- | :--------: | :-----: | :-----: |:------------: |:----: | :-------: | :----: | :-------: |
| DarkNet53 | ImageNet | 608 | False | 8 | 270e | 45.571 | 38.9 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar
)
|
| DarkNet53 (paper) | ImageNet | 608 | False | 8 | 270e | - | 33.0 | - |
| DarkNet53 | ImageNet | 416 | False | 8 | 270e | - | 37.5 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar
)
|
| DarkNet53 (paper) | ImageNet | 416 | False | 8 | 270e | - | 31.0 | - |
| DarkNet53 | ImageNet | 320 | False | 8 | 270e | - | 34.8 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar
)
|
| DarkNet53 (paper) | ImageNet | 320 | False | 8 | 270e | - | 28.2 | - |
| MobileNet-V1 | ImageNet | 608 | False | 8 | 270e | 78.302 | 29.3 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar
)
|
| DarkNet53 | ImageNet | 608 | False | 8 | 270e | 45.571 | 38.9 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar
)
|
| MobileNet-V1 | ImageNet | 416 | False | 8 | 270e | - | 29.3 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar
)
|
| DarkNet53 | ImageNet | 416 | False | 8 | 270e | - | 37.5 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar
)
|
| MobileNet-V1 | ImageNet | 320 | False | 8 | 270e | - | 27.1 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar
)
|
| DarkNet53 | ImageNet | 320 | False | 8 | 270e | - | 34.8 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar
)
|
| ResNet34 | ImageNet | 608 | False | 8 | 270e | 63.356 | 36.2 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar
)
|
| MobileNet-V1 | ImageNet | 608 | False | 8 | 270e | 78.302 | 29.3 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar
)
|
| ResNet34 | ImageNet | 416 | False | 8 | 270e | - | 34.3 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar
)
|
| MobileNet-V1 | ImageNet | 416 | False | 8 | 270e | - | 29.3 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar
)
|
| ResNet34 | ImageNet | 320 | False | 8 | 270e | - | 31.4 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar
)
|
| MobileNet-V1 | ImageNet | 320 | False | 8 | 270e | - | 27.1 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar
)
|
| ResNet50_vd | ImageNet | 608 | True | 8 | 270e | - | 39.1 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn.tar
)
|
| ResNet34 | ImageNet | 608 | False | 8 | 270e | 63.356 | 36.2 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar
)
|
| ResNet50_vd | Object365 | 608 | True | 8 | 270e | - | 41.4 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_pretrained_coco.tar
)
|
| ResNet34 | ImageNet | 416 | False | 8 | 270e | - | 34.3 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar
)
|
| ResNet34 | ImageNet | 320 | False | 8 | 270e | - | 31.4 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar
)
|
| ResNet50_vd | ImageNet | 608 | True | 8 | 270e | - | 39.1 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn.tar
)
|
### Yolo v3 on Pascal VOC
| ResNet50_vd | Object365 | 608 | True | 8 | 270e | - | 41.4 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_pretrained_coco.tar
)
|
### YOLO v3 on Pascal VOC
| Backbone | Size | Image/gpu | Lr schd | Inf time (fps) | Box AP | Download |
| Backbone | Size | Image/gpu | Lr schd | Inf time (fps) | Box AP | Download |
| :----------- | :--: | :-------: | :-----: | :------------: | :----: | :----------------------------------------------------------: |
| :----------- | :--: | :-------: | :-----: | :------------: | :----: | :----------------------------------------------------------: |
...
@@ -126,8 +128,11 @@ The backbone models pretrained on ImageNet are available. All backbone models ar
...
@@ -126,8 +128,11 @@ The backbone models pretrained on ImageNet are available. All backbone models ar
| ResNet34 | 416 | 8 | 270e | - | 81.9 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34_voc.tar
)
|
| ResNet34 | 416 | 8 | 270e | - | 81.9 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34_voc.tar
)
|
| ResNet34 | 320 | 8 | 270e | - | 80.1 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34_voc.tar
)
|
| ResNet34 | 320 | 8 | 270e | - | 80.1 |
[
model
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34_voc.tar
)
|
**Notes:**
Yolo v3 is trained in 8 GPU with total batch size as 64 and trained 270 epoches. Yolo v3 training data augmentations: mixup,
#### Notes:
randomly color distortion, randomly cropping, randomly expansion, randomly interpolation method, randomly flippling. Yolo v3 used randomly
-
YOLOv3-DarkNet53 performance in paper
[
YOLOv3
](
https://arxiv.org/abs/1804.02767
)
is also provided above, our implements
improved performance mainly by using L1 loss in bounding box width and height regression, image mixup and label smooth.
-
YOLO v3 is trained in 8 GPU with total batch size as 64 and trained 270 epoches. YOLO v3 training data augmentations: mixup,
randomly color distortion, randomly cropping, randomly expansion, randomly interpolation method, randomly flippling. YOLO v3 used randomly
reshaped minibatch in training, inferences can be performed on different image sizes with the same model weights, and we provided evaluation
reshaped minibatch in training, inferences can be performed on different image sizes with the same model weights, and we provided evaluation
results of image size 608/416/320 above. Deformable conv is added on stage 5 of backbone.
results of image size 608/416/320 above. Deformable conv is added on stage 5 of backbone.
...
...
docs/MODEL_ZOO_cn.md
浏览文件 @
744cb882
...
@@ -92,23 +92,26 @@ Paddle提供基于ImageNet的骨架网络预训练模型。所有预训练模型
...
@@ -92,23 +92,26 @@ Paddle提供基于ImageNet的骨架网络预训练模型。所有预训练模型
-
Group Normalization参考论文
[
Group Normalization
](
https://arxiv.org/abs/1803.08494
)
.
-
Group Normalization参考论文
[
Group Normalization
](
https://arxiv.org/abs/1803.08494
)
.
-
详细的配置文件在
[
configs/gn
](
https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/PaddleDetection/configs/gn
)
-
详细的配置文件在
[
configs/gn
](
https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/PaddleDetection/configs/gn
)
### Y
olo
v3
### Y
OLO
v3
| 骨架网络 | 预训练数据集 | 输入尺寸 | 加入deformable卷积 | 每张GPU图片个数 | 学习率策略 |推理时间(fps)| Box AP | 下载 |
| 骨架网络 | 预训练数据集 | 输入尺寸 | 加入deformable卷积 | 每张GPU图片个数 | 学习率策略 |推理时间(fps)| Box AP | 下载 |
| :----------- | :--: | :-----: | :-----: |:------------: |:----: | :-------: | :----: | :-------: |
| :----------- | :--: | :-----: | :-----: |:------------: |:----: | :-------: | :----: | :-------: |
| DarkNet53 | ImageNet | 608 | 否 | 8 | 270e | 45.571 | 38.9 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar
)
|
| DarkNet53 (paper) | ImageNet | 608 | 否 | 8 | 270e | - | 33.0 | - |
| DarkNet53 | ImageNet | 416 | 否 | 8 | 270e | - | 37.5 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar
)
|
| DarkNet53 (paper) | ImageNet | 416 | 否 | 8 | 270e | - | 31.0 | - |
| DarkNet53 | ImageNet | 320 | 否 | 8 | 270e | - | 34.8 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar
)
|
| DarkNet53 (paper) | ImageNet | 320 | 否 | 8 | 270e | - | 28.2 | - |
| MobileNet-V1 | ImageNet | 608 | 否 | 8 | 270e | 78.302 | 29.3 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar
)
|
| DarkNet53 | ImageNet | 608 | 否 | 8 | 270e | 45.571 | 38.9 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar
)
|
| MobileNet-V1 | ImageNet | 416 | 否 | 8 | 270e | - | 29.3 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar
)
|
| DarkNet53 | ImageNet | 416 | 否 | 8 | 270e | - | 37.5 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar
)
|
| MobileNet-V1 | ImageNet | 320 | 否 | 8 | 270e | - | 27.1 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar
)
|
| DarkNet53 | ImageNet | 320 | 否 | 8 | 270e | - | 34.8 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar
)
|
| ResNet34 | ImageNet | 608 | 否 | 8 | 270e | 63.356 | 36.2 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar
)
|
| MobileNet-V1 | ImageNet | 608 | 否 | 8 | 270e | 78.302 | 29.3 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar
)
|
| ResNet34 | ImageNet | 416 | 否 | 8 | 270e | - | 34.3 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar
)
|
| MobileNet-V1 | ImageNet | 416 | 否 | 8 | 270e | - | 29.3 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar
)
|
| ResNet34 | ImageNet | 320 | 否 | 8 | 270e | - | 31.4 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar
)
|
| MobileNet-V1 | ImageNet | 320 | 否 | 8 | 270e | - | 27.1 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar
)
|
| ResNet50_vd | ImageNet | 608 | 是 | 8 | 270e | - | 39.1 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn.tar
)
|
| ResNet34 | ImageNet | 608 | 否 | 8 | 270e | 63.356 | 36.2 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar
)
|
| ResNet50_vd | Object365 | 608 | 是 | 8 | 270e | - | 41.4 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_pretrained_coco.tar
)
|
| ResNet34 | ImageNet | 416 | 否 | 8 | 270e | - | 34.3 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar
)
|
| ResNet34 | ImageNet | 320 | 否 | 8 | 270e | - | 31.4 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar
)
|
### Yolo v3 基于Pasacl VOC数据集
| ResNet50_vd | ImageNet | 608 | 是 | 8 | 270e | - | 39.1 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn.tar
)
|
| ResNet50_vd | Object365 | 608 | 是 | 8 | 270e | - | 41.4 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_pretrained_coco.tar
)
|
### YOLO v3 基于Pasacl VOC数据集
| 骨架网络 | 输入尺寸 | 每张GPU图片个数 | 学习率策略 |推理时间(fps)| Box AP | 下载 |
| 骨架网络 | 输入尺寸 | 每张GPU图片个数 | 学习率策略 |推理时间(fps)| Box AP | 下载 |
| :----------- | :--: | :-----: | :-----: |:------------: |:----: | :-------: |
| :----------- | :--: | :-----: | :-----: |:------------: |:----: | :-------: |
...
@@ -122,7 +125,9 @@ Paddle提供基于ImageNet的骨架网络预训练模型。所有预训练模型
...
@@ -122,7 +125,9 @@ Paddle提供基于ImageNet的骨架网络预训练模型。所有预训练模型
| ResNet34 | 416 | 8 | 270e | - | 81.9 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34_voc.tar
)
|
| ResNet34 | 416 | 8 | 270e | - | 81.9 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34_voc.tar
)
|
| ResNet34 | 320 | 8 | 270e | - | 80.1 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34_voc.tar
)
|
| ResNet34 | 320 | 8 | 270e | - | 80.1 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34_voc.tar
)
|
**注意事项:**
Yolo v3在8卡，总batch size为64下训练270轮。数据增强包括：mixup, 随机颜色失真，随机剪裁，随机扩张，随机插值法，随机翻转。Yolo v3在训练阶段对minibatch采用随机reshape，可以采用相同的模型测试不同尺寸图片，我们分别提供了尺寸为608/416/320大小的测试结果。deformable卷积作用在骨架网络5阶段。
#### 注意事项:
-
上表中也提供了原论文
[
YOLOv3
](
https://arxiv.org/abs/1804.02767
)
中YOLOv3-DarkNet53的精度，我们的实现版本主要从在bounding box的宽度和高度回归上使用了L1损失，图像mixup和label smooth等方法优化了其精度。
-
YOLO v3在8卡，总batch size为64下训练270轮。数据增强包括：mixup, 随机颜色失真，随机剪裁，随机扩张，随机插值法，随机翻转。YOLO v3在训练阶段对minibatch采用随机reshape，可以采用相同的模型测试不同尺寸图片，我们分别提供了尺寸为608/416/320大小的测试结果。deformable卷积作用在骨架网络5阶段。
### RetinaNet
### RetinaNet
...
...
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑！
取消
想要评论请
注册
或
登录
