diff --git a/configs/ppyolo/README.md b/configs/ppyolo/README.md
index 57b9b399bea40e3efd0543bf4b9f83535d17df1b..3d8f2af9ec805fda072d7d9a80f4babde21ddd8a 100644
--- a/configs/ppyolo/README.md
+++ b/configs/ppyolo/README.md
@@ -67,22 +67,26 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods:
### PP-YOLO for mobile
-| Model | GPU number | images/GPU | Model Size | input shape | Box APval | Kirin 990(FPS) | download | config |
-|:----------------------------:|:----------:|:----------:| :--------: | :----------:| :------------------: | :------------: | :------: | :-----: |
-| PP-YOLO_MobileNetV3_large | 4 | 32 | 18MB | 320 | 22.0 | 14.1 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_large.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_mobilenet_v3_large.yml) |
-| PP-YOLO_MobileNetV3_small | 4 | 32 | 11MB | 320 | 16.8 | 21.5 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_small.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_mobilenet_v3_small.yml) |
+| Model | GPU number | images/GPU | Model Size | input shape | Box APval | Box AP50val | Kirin 990 1xCore(FPS) | download | inference model download | config |
+|:----------------------------:|:----------:|:----------:| :--------: | :----------:| :------------------: | :--------------------: | :-------------------: | :------: | :----------------------: | :-----: |
+| PP-YOLO_MobileNetV3_large | 4 | 32 | 18MB | 320 | 23.2 | 42.6 | 15.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_large.pdparams) | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_large.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_mobilenet_v3_large.yml) |
+| PP-YOLO_MobileNetV3_small | 4 | 32 | 11MB | 320 | 17.2 | 33.8 | 28.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_small.pdparams) | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_small.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_mobilenet_v3_small.yml) |
**Notes:**
-- PP-YOLO_MobileNetV3 is trained on COCO train2017 datast and evaluated on val2017 dataset，Box APval is evaluation results of `mAP(IoU=0.5:0.95)`.
+- PP-YOLO_MobileNetV3 is trained on COCO train2017 datast and evaluated on val2017 dataset，Box APval is evaluation results of `mAP(IoU=0.5:0.95)`, Box APval is evaluation results of `mAP(IoU=0.5)`.
- PP-YOLO_MobileNetV3 used 4 GPUs for training and mini-batch size as 32 on each GPU, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](../../docs/FAQ.md).
- PP-YOLO_MobileNetV3 inference speed is tested on Kirin 990 with 1 thread.
### Slim PP-YOLO
-| Model | GPU number | images/GPU | Prune Ratio | Teacher Model | Model Size | input shape | Box APval | Kirin 990(FPS) | download | config |
-|:----------------------------:|:----------:|:----------:| :---------: | :-----------------------: | :--------: | :----------:| :------------------: | :------------: | :------: | :-----: |
-| PP-YOLO_MobileNetV3_small | 4 | 32 | 75% | PP-YOLO_MobileNetV3_large | 4.1MB | 320 | 14.4 | 21.5 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_small.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_mobilenet_v3_small.yml) |
+| Model | GPU number | images/GPU | Prune Ratio | Teacher Model | Model Size | input shape | Box APval | Kirin 990 1xCore(FPS) | download | inference model download | config |
+|:----------------------------:|:----------:|:----------:| :---------: | :-----------------------: | :--------: | :----------:| :------------------: | :-------------------: | :------: | :----------------------: | :-----: |
+| PP-YOLO_MobileNetV3_small | 4 | 32 | 75% | PP-YOLO_MobileNetV3_large | 4.2MB | 320 | 16.2 | 39.8 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_small_prune75_distillby_mobilenet_v3_large.pdparams) | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_small_prune75_distillby_mobilenet_v3_large.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_mobilenet_v3_small.yml) |
+
+- Slim PP-YOLO is trained by slim traing method from [Distill pruned model](../../slim/extentions/distill_pruned_model/README.md)，distill training pruned PP-YOLO_MobileNetV3_small model with PP-YOLO_MobileNetV3_large model as the teacher model
+- Pruning detectiom head of PP-YOLO model with ratio as 75%, while the arguments are `--pruned_params="yolo_block.0.2.conv.weights,yolo_block.0.tip.conv.weights,yolo_block.1.2.conv.weights,yolo_block.1.tip.conv.weights" --pruned_ratios="0.75,0.75,0.75,0.75"`
+- For Slim PP-YOLO training, evaluation, inference and model exporting, please see [Distill pruned model](../../slim/extentions/distill_pruned_model/README.md)
### PP-YOLO on Pascal VOC
diff --git a/configs/ppyolo/README_cn.md b/configs/ppyolo/README_cn.md
index e5152420cd81482353f16b546c3173f02be18ea8..68ca89d41a8f11d64ff56583a34bd145c5eee63c 100644
--- a/configs/ppyolo/README_cn.md
+++ b/configs/ppyolo/README_cn.md
@@ -68,15 +68,25 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度：
### PP-YOLO 轻量级模型
-| 模型 | GPU个数 | 每GPU图片个数 | 模型体积 | 输入尺寸 | Box APval | Kirin 990 (FPS) | 模型下载 | 配置文件 |
-|:----------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: | :-------------: |------------: | :---------------------: |
-| PP-YOLO_MobileNetV3_large | 4 | 32 | 18MB | 320 | 22.0 | 14.1 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_large.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_mobilenet_v3_large.yml) |
-| PP-YOLO_MobileNetV3_small | 4 | 32 | 11MB | 320 | 16.8 | 21.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_small.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_mobilenet_v3_small.yml) |
+| 模型 | GPU个数 | 每GPU图片个数 | 模型体积 | 输入尺寸 | Box APval | Box AP50val | Kirin 990 1xCore (FPS) | 模型下载 | 预测模型下载 | 配置文件 |
+|:----------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: | :--------------------: | :--------------------: | :------: | :----------: | :------: |
+| PP-YOLO_MobileNetV3_large | 4 | 32 | 18MB | 320 | 23.2 | 42.6 | 14.1 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_large.pdparams) | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_large.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_mobilenet_v3_large.yml) |
+| PP-YOLO_MobileNetV3_small | 4 | 32 | 11MB | 320 | 17.2 | 33.8 | 21.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_small.pdparams) | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_large.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_mobilenet_v3_small.yml) |
-- PP-YOLO_MobileNetV3 模型使用COCO数据集中train2017作为训练集，使用val2017作为测试集，Box AP50val为`mAP(IoU=0.5:0.95)`评估结果。
+- PP-YOLO_MobileNetV3 模型使用COCO数据集中train2017作为训练集，使用val2017作为测试集，Box APval为`mAP(IoU=0.5:0.95)`评估结果, Box AP50val为`mAP(IoU=0.5)`评估结果。
- PP-YOLO_MobileNetV3 模型训练过程中使用4GPU，每GPU batch size为32进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](../../docs/FAQ.md)调整学习率和迭代次数。
- PP-YOLO_MobileNetV3 模型推理速度测试环境配置为麒麟990芯片单线程。
+### PP-YOLO 轻量级裁剪模型
+
+| 模型 | GPU 个数 | 每GPU图片个数 | 裁剪率 | Teacher模型 | 模型体积 | 输入尺寸 | Box APval | Kirin 990 1xCore (FPS) | 模型下载 | 预测模型下载 | 配置文件 |
+|:----------------------------:|:----------:|:-------------:| :---------: | :-----------------------: | :--------: | :----------:| :------------------: | :--------------------: | :------: | :----------: | :------: |
+| PP-YOLO_MobileNetV3_small | 4 | 32 | 75% | PP-YOLO_MobileNetV3_large | 4.2MB | 320 | 16.2 | 39.8 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_small_prune75_distillby_mobilenet_v3_large.pdparams) | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_small_prune75_distillby_mobilenet_v3_large.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_mobilenet_v3_small.yml) |
+
+- PP-YOLO 轻量级裁剪模型采用[蒸馏通道剪裁模型](../../slim/extentions/distill_pruned_model/README.md) 的方式训练得到，基于 PP-YOLO_MobileNetV3_small 模型对Head部分做卷积通道剪裁后使用 PP-YOLO_MobileNetV3_large 模型进行蒸馏训练
+- 卷积通道检测对Head部分剪裁掉75%的通道数，及剪裁参数为`--pruned_params="yolo_block.0.2.conv.weights,yolo_block.0.tip.conv.weights,yolo_block.1.2.conv.weights,yolo_block.1.tip.conv.weights" --pruned_ratios="0.75,0.75,0.75,0.75"`
+- PP-YOLO 轻量级裁剪模型的训练、评估、预测及模型导出方法见[蒸馏通道剪裁模型](../../slim/extentions/distill_pruned_model/README.md)
+
### Pascal VOC数据集上的PP-YOLO
PP-YOLO在Pascal VOC数据集上训练模型如下:
diff --git a/configs/ppyolo/ppyolo_mobilenet_v3_large.yml b/configs/ppyolo/ppyolo_mobilenet_v3_large.yml
index 4be1f4677f1357a5de35d8e7e57accd23b22aca3..262c6c0b94032d45e9d544f25cf674047dc08048 100755
--- a/configs/ppyolo/ppyolo_mobilenet_v3_large.yml
+++ b/configs/ppyolo/ppyolo_mobilenet_v3_large.yml
@@ -29,10 +29,11 @@ MobileNetV3:
YOLOv3Head:
anchor_masks: [[3, 4, 5], [0, 1, 2]]
- anchors: [[10, 14], [23, 27], [37, 58],
- [81, 82], [135, 169], [344, 319]]
+ anchors: [[11, 18], [34, 47], [51, 126],
+ [115, 71], [120, 195], [254, 235]]
norm_decay: 0.
conv_block_num: 0
+ coord_conv: true
scale_x_y: 1.05
yolo_loss: YOLOv3Loss
spp: true
@@ -42,11 +43,11 @@ YOLOv3Head:
nms_threshold: 0.45
nms_top_k: 1000
normalized: false
- score_threshold: 0.01
+ score_threshold: 0.005
drop_block: true
YOLOv3Loss:
- ignore_thresh: 0.7
+ ignore_thresh: 0.5
scale_x_y: 1.05
label_smooth: false
use_fine_grained_loss: true
@@ -54,11 +55,11 @@ YOLOv3Loss:
IouLoss:
loss_weight: 2.5
- max_height: 608
- max_width: 608
+ max_height: 512
+ max_width: 512
LearningRate:
- base_lr: 0.00666
+ base_lr: 0.005
schedulers:
- !PiecewiseDecay
gamma: 0.1
@@ -81,7 +82,7 @@ _READER_: 'ppyolo_reader.yml'
TrainReader:
inputs_def:
fields: ['image', 'gt_bbox', 'gt_class', 'gt_score']
- num_max_boxes: 50
+ num_max_boxes: 90
dataset:
!COCODataSet
image_dir: train2017
@@ -103,11 +104,11 @@ TrainReader:
is_normalized: false
- !NormalizeBox {}
- !PadBox
- num_max_boxes: 50
+ num_max_boxes: 90
- !BboxXYXY2XYWH {}
batch_transforms:
- !RandomShape
- sizes: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608]
+ sizes: [224, 256, 288, 320, 352, 384, 416, 448, 480, 512]
random_inter: True
- !NormalizeImage
mean: [0.485, 0.456, 0.406]
@@ -122,12 +123,14 @@ TrainReader:
# is set as false
- !Gt2YoloTarget
anchor_masks: [[3, 4, 5], [0, 1, 2]]
- anchors: [[10, 14], [23, 27], [37, 58],
- [81, 82], [135, 169], [344, 319]]
+ anchors: [[11, 18], [34, 47], [51, 126],
+ [115, 71], [120, 195], [254, 235]]
downsample_ratios: [32, 16]
+ iou_thresh: 0.25
+ num_classes: 80
batch_size: 32
shuffle: true
- mixup_epoch: 500
+ mixup_epoch: 200
drop_last: true
worker_num: 8
bufsize: 4
@@ -136,7 +139,7 @@ TrainReader:
EvalReader:
inputs_def:
fields: ['image', 'im_size', 'im_id']
- num_max_boxes: 50
+ num_max_boxes: 90
dataset:
!COCODataSet
image_dir: val2017
@@ -155,11 +158,35 @@ EvalReader:
is_scale: True
is_channel_first: false
- !PadBox
- num_max_boxes: 50
+ num_max_boxes: 90
- !Permute
to_bgr: false
channel_first: True
- batch_size: 8
+ batch_size: 1
drop_empty: false
worker_num: 2
bufsize: 4
+
+TestReader:
+ inputs_def:
+ image_shape: [3, 320, 320]
+ fields: ['image', 'im_size', 'im_id']
+ dataset:
+ !ImageFolder
+ anno_path: annotations/instances_val2017.json
+ with_background: false
+ sample_transforms:
+ - !DecodeImage
+ to_rgb: True
+ - !ResizeImage
+ target_size: 320
+ interp: 2
+ - !NormalizeImage
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ is_scale: True
+ is_channel_first: false
+ - !Permute
+ to_bgr: false
+ channel_first: True
+ batch_size: 1
diff --git a/configs/ppyolo/ppyolo_mobilenet_v3_small.yml b/configs/ppyolo/ppyolo_mobilenet_v3_small.yml
index eb70c8d2656f8746a04d2fcda51fa80f9c02c0e8..9c3e27976e285386331d8d52004f1a79ac11aec0 100755
--- a/configs/ppyolo/ppyolo_mobilenet_v3_small.yml
+++ b/configs/ppyolo/ppyolo_mobilenet_v3_small.yml
@@ -29,10 +29,11 @@ MobileNetV3:
YOLOv3Head:
anchor_masks: [[3, 4, 5], [0, 1, 2]]
- anchors: [[10, 14], [23, 27], [37, 58],
- [81, 82], [135, 169], [344, 319]]
+ anchors: [[11, 18], [34, 47], [51, 126],
+ [115, 71], [120, 195], [254, 235]]
norm_decay: 0.
conv_block_num: 0
+ coord_conv: true
scale_x_y: 1.05
yolo_loss: YOLOv3Loss
spp: true
@@ -42,11 +43,11 @@ YOLOv3Head:
nms_threshold: 0.45
nms_top_k: 1000
normalized: false
- score_threshold: 0.01
+ score_threshold: 0.005
drop_block: true
YOLOv3Loss:
- ignore_thresh: 0.7
+ ignore_thresh: 0.5
scale_x_y: 1.05
label_smooth: false
use_fine_grained_loss: true
@@ -54,11 +55,11 @@ YOLOv3Loss:
IouLoss:
loss_weight: 2.5
- max_height: 608
- max_width: 608
+ max_height: 512
+ max_width: 512
LearningRate:
- base_lr: 0.00666
+ base_lr: 0.005
schedulers:
- !PiecewiseDecay
gamma: 0.1
@@ -81,7 +82,7 @@ _READER_: 'ppyolo_reader.yml'
TrainReader:
inputs_def:
fields: ['image', 'gt_bbox', 'gt_class', 'gt_score']
- num_max_boxes: 50
+ num_max_boxes: 90
dataset:
!COCODataSet
image_dir: train2017
@@ -103,11 +104,11 @@ TrainReader:
is_normalized: false
- !NormalizeBox {}
- !PadBox
- num_max_boxes: 50
+ num_max_boxes: 90
- !BboxXYXY2XYWH {}
batch_transforms:
- !RandomShape
- sizes: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608]
+ sizes: [224, 256, 288, 320, 352, 384, 416, 448, 480, 512]
random_inter: True
- !NormalizeImage
mean: [0.485, 0.456, 0.406]
@@ -122,12 +123,14 @@ TrainReader:
# is set as false
- !Gt2YoloTarget
anchor_masks: [[3, 4, 5], [0, 1, 2]]
- anchors: [[10, 14], [23, 27], [37, 58],
- [81, 82], [135, 169], [344, 319]]
+ anchors: [[11, 18], [34, 47], [51, 126],
+ [115, 71], [120, 195], [254, 235]]
downsample_ratios: [32, 16]
+ iou_thresh: 0.25
+ num_classes: 80
batch_size: 32
shuffle: true
- mixup_epoch: 500
+ mixup_epoch: 200
drop_last: true
worker_num: 8
bufsize: 4
@@ -136,7 +139,7 @@ TrainReader:
EvalReader:
inputs_def:
fields: ['image', 'im_size', 'im_id']
- num_max_boxes: 50
+ num_max_boxes: 90
dataset:
!COCODataSet
image_dir: val2017
@@ -155,11 +158,35 @@ EvalReader:
is_scale: True
is_channel_first: false
- !PadBox
- num_max_boxes: 50
+ num_max_boxes: 90
- !Permute
to_bgr: false
channel_first: True
- batch_size: 8
+ batch_size: 1
drop_empty: false
worker_num: 2
bufsize: 4
+
+TestReader:
+ inputs_def:
+ image_shape: [3, 320, 320]
+ fields: ['image', 'im_size', 'im_id']
+ dataset:
+ !ImageFolder
+ anno_path: annotations/instances_val2017.json
+ with_background: false
+ sample_transforms:
+ - !DecodeImage
+ to_rgb: True
+ - !ResizeImage
+ target_size: 320
+ interp: 2
+ - !NormalizeImage
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ is_scale: True
+ is_channel_first: false
+ - !Permute
+ to_bgr: false
+ channel_first: True
+ batch_size: 1
diff --git a/ppdet/data/transform/batch_operators.py b/ppdet/data/transform/batch_operators.py
index c0e9bd6e0e4af3940326d0a99264fda745292ba5..40267e1cac8751b212b3199c4b34654b7848832c 100644
--- a/ppdet/data/transform/batch_operators.py
+++ b/ppdet/data/transform/batch_operators.py
@@ -286,7 +286,8 @@ class Gt2YoloTarget(BaseOperator):
iou = jaccard_overlap(
[0., 0., gw, gh],
[0., 0., an_hw[mask_i, 0], an_hw[mask_i, 1]])
- if iou > self.iou_thresh:
+ if iou > self.iou_thresh and target[idx, 5, gj,
+ gi] == 0.:
# x, y, w, h, scale
target[idx, 0, gj, gi] = gx * grid_w - gi
target[idx, 1, gj, gi] = gy * grid_h - gj
diff --git a/ppdet/modeling/anchor_heads/yolo_head.py b/ppdet/modeling/anchor_heads/yolo_head.py
index a0c3d2bc40f97c24b5469df72539225eef5d77a1..df8a375cf18c94f7d0e0746ece9c4a3ab48f58b7 100644
--- a/ppdet/modeling/anchor_heads/yolo_head.py
+++ b/ppdet/modeling/anchor_heads/yolo_head.py
@@ -241,10 +241,11 @@ class YOLOv3Head(object):
padding=0,
name='{}.{}.0'.format(name, j))
if self.use_spp and is_first and j == 1:
+ c = conv.shape[1]
conv = self._spp_module(conv, name="spp")
conv = self._conv_bn(
conv,
- 512,
+ c,
filter_size=1,
stride=1,
padding=0,
@@ -264,7 +265,15 @@ class YOLOv3Head(object):
is_test=is_test)
if self.use_spp and conv_block_num == 0 and is_first:
+ c = conv.shape[1]
conv = self._spp_module(conv, name="spp")
+ conv = self._conv_bn(
+ conv,
+ c,
+ filter_size=1,
+ stride=1,
+ padding=0,
+ name='{}.spp.conv'.format(name))
if self.drop_block and (is_first or conv_block_num == 0):
conv = DropBlock(