Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/Sharpiless/Yolov3-MobileNet-Distillation

在Yolov3-MobileNet上进行模型蒸馏训练
https://github.com/Sharpiless/Yolov3-MobileNet-Distillation

Last synced: 12 days ago
JSON representation

在Yolov3-MobileNet上进行模型蒸馏训练

Host: GitHub
URL: https://github.com/Sharpiless/Yolov3-MobileNet-Distillation
Owner: Sharpiless
Created: 2021-04-30T05:12:45.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2021-04-30T05:15:42.000Z (about 3 years ago)
Last Synced: 2024-02-29T08:35:39.805Z (4 months ago)
Language: Jupyter Notebook
Size: 472 KB
Stars: 5
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

Lists

awesome-yolo-object-detection - Sharpiless/Yolov3-MobileNet-Distillation - MobileNet-Distillation?style=social"/> : 在Yolov3-MobileNet上进行模型蒸馏训练。 (Lighter and Deployment Frameworks)

README

# PaddleDetection知识蒸馏

知识蒸馏主要是让让新模型（通常是一个参数量更少的模型）近似原模型(模型即函数)。注意到，在机器学习中，我们常常假定输入到输出有一个潜在的函数关系，这个函数是未知的：从头学习一个新模型就是从有限的数据中近似一个未知的函数。如果让新模型近似原模型，因为原模型的函数是已知的，我们可以使用很多非训练集内的伪数据来训练新模型。

# 分类模型蒸馏：
原来我们需要让新模型的softmax分布与真实标签匹配，现在只需要让新模型与原模型在给定输入下的softmax分布匹配了。但是由于softmax函数是一个约等于arg max的近似，它所能描述的知识（对输出的概率描述）非常有限，一种常用的解决方法是直接让新旧模型匹配logits输出，即使用teacher model的logits输出作为student model的回归目标，并使用L2损失作为loss。

## one-stage检测模型蒸馏：

**基本思路**

One-stage目标检测任务的训练目标难度更大，因为teacher网络会预测出更多的背景bbox，如果直接用teacher的预测输出作为student学习的soft label会有严重的类别不均衡问题。解决这个问题需要引入新的方法。

主要是《Object detection at 200 Frames Per Second》这篇文章中提出了针对该问题的解决方案，即针对YOLOv3中分类、回归、objectness三个不同的head适配不同的蒸馏损失函数，并对分类和回归的损失函数用objectness分值进行抑制，以解决前景背景类别不均衡问题。

并且该文章使用未标注数据作为蒸馏损失，跟检测损失（有标注数据）加权求和作为最终的损失函数。

![](https://github.com/Sharpiless/Yolov3-MobileNet-Pruning/blob/main/model.png)

# 关于作者
> B站：[https://space.bilibili.com/470550823](https://space.bilibili.com/470550823)

> CSDN：[https://blog.csdn.net/weixin_44936889](https://blog.csdn.net/weixin_44936889)

> AI Studio：[https://aistudio.baidu.com/aistudio/personalcenter/thirdview/67156](https://aistudio.baidu.com/aistudio/personalcenter/thirdview/67156)

> Github：[https://github.com/Sharpiless](https://github.com/Sharpiless)

# 实验环境：

数据集：VOC2012

GPU：V100*1

Batch Size：8

Epoches：70000

```python
!rm -rf PaddleDetection/
```

```python
!unzip -oq data/data85000/PaddleDetection-master.zip -d ./
```

```python
%cd PaddleDetection-master/
```

/home/aistudio/PaddleDetection-master

```python
!pip install -r requirements.txt
```

```python
!pip install paddleslim
```

```python
!mkdir /home/aistudio/PaddleDetection-master/dataset/voc/VOCdevkit
```

```python
!unzip -oq ../data/data39480/VOC2012.zip -d dataset/voc/VOCdevkit/
```

# 划分数据集

```python
import os
from tqdm import tqdm
from random import shuffle

base = 'dataset/voc/'
img_base = 'VOCdevkit/VOC2012/JPEGImages/'
xml_base = 'VOCdevkit/VOC2012/Annotations/'

images_list = os.listdir(os.path.join(base, img_base))
shuffle(images_list)

split_num = int(0.9 * len(images_list))

with open(os.path.join(base, 'trainval.txt'), 'w') as f:
for im in tqdm(images_list[:split_num]):
img_id = im[:-4]
line = '{}{}.jpg {}{}.xml\n'.format(img_base, img_id, xml_base, img_id)
f.write(line)

with open(os.path.join(base, 'test.txt'), 'w') as f:
for im in tqdm(images_list[split_num:]):
img_id = im[:-4]
line = '{}{}.jpg {}{}.xml\n'.format(img_base, img_id, xml_base, img_id)
f.write(line)
```

100%|██████████| 15412/15412 [00:00<00:00, 915578.85it/s]
100%|██████████| 1713/1713 [00:00<00:00, 792766.50it/s]

## 正常训练：
正常训练反而是收敛最快的。

```python
!python tools/train.py \
-c configs/yolov3_mobilenet_v1_voc.yml --eval
```

2021-04-30 13:02:48,341 - INFO - iter: 29200, lr: 0.001000, 'loss': '19.766043', eta: 2:01:38, batch_cost: 0.17888 sec, ips: 44.72353 images/sec

## L2蒸馏损失训练
实验结果：
Best test box ap: 55.303840240819966, in step: 69999

```python
!python slim/distillation/distill.py \
-c configs/yolov3_mobilenet_v1_voc.yml \
-t configs/yolov3_r34_voc.yml \
--teacher_pretrained https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34_voc.tar
```

2021-04-29 15:50:48,397 - INFO - step 68500 lr 0.000010, loss 138.481812, distill_loss 120.378197, teacher_loss 15.744522
2021-04-29 15:51:09,905 - INFO - step 68600 lr 0.000010, loss 160.502563, distill_loss 146.543015, teacher_loss 10.307454
2021-04-29 15:51:29,715 - INFO - step 68700 lr 0.000010, loss 107.800804, distill_loss 96.591446, teacher_loss 11.338940
2021-04-29 15:51:50,172 - INFO - step 68800 lr 0.000010, loss 119.590355, distill_loss 106.403137, teacher_loss 14.288318
2021-04-29 15:52:10,472 - INFO - step 68900 lr 0.000010, loss 122.257706, distill_loss 97.294144, teacher_loss 19.427813
2021-04-29 15:52:50,220 - INFO - step 69100 lr 0.000010, loss 144.336548, distill_loss 129.560059, teacher_loss 14.828430
2021-04-29 15:53:09,471 - INFO - step 69200 lr 0.000010, loss 124.607025, distill_loss 112.543617, teacher_loss 12.816269
2021-04-29 15:53:29,804 - INFO - step 69300 lr 0.000010, loss 123.180389, distill_loss 106.882095, teacher_loss 13.914233
2021-04-29 15:53:52,323 - INFO - step 69400 lr 0.000010, loss 129.621185, distill_loss 106.162689, teacher_loss 19.629433
2021-04-29 15:54:12,425 - INFO - step 69500 lr 0.000010, loss 127.870285, distill_loss 112.545990, teacher_loss 12.473295
2021-04-29 15:54:32,816 - INFO - step 69600 lr 0.000010, loss 194.085114, distill_loss 181.748535, teacher_loss 12.321056
2021-04-29 15:54:53,872 - INFO - step 69700 lr 0.000010, loss 214.038483, distill_loss 186.034805, teacher_loss 23.179140
2021-04-29 15:55:13,472 - INFO - step 69800 lr 0.000010, loss 123.184227, distill_loss 106.268097, teacher_loss 16.398609
2021-04-29 15:55:33,172 - INFO - step 69900 lr 0.000010, loss 91.018211, distill_loss 76.597687, teacher_loss 10.691896
2021-04-29 15:55:52,595 - INFO - Save model to output/yolov3_mobilenet_v1_voc/model_final.
2021-04-29 15:55:56,586 - INFO - Test iter 0
2021-04-29 15:56:05,482 - INFO - Test iter 100
2021-04-29 15:56:12,581 - INFO - Test iter 200
2021-04-29 15:56:13,775 - INFO - Test finish iter 215
2021-04-29 15:56:13,775 - INFO - Total number of images: 1713, inference time: 99.09352407371594 fps.
2021-04-29 15:56:13,776 - INFO - Start evaluate...
2021-04-29 15:56:14,063 - INFO - Accumulating evaluatation results...
2021-04-29 15:56:14,077 - INFO - mAP(0.50, 11point) = 55.30%
2021-04-29 15:56:14,077 - INFO - Save model to output/yolov3_mobilenet_v1_voc/best_model.
2021-04-29 15:56:18,180 - INFO - Best test box ap: 55.303840240819966, in step: 69999

```python
!mkdir L2distill
!mv output/yolov3_mobilenet_v1_voc/model_final.* L2distill
```

## Fine-gained蒸馏损失训练

《Object detection at 200 Frames Per Second》
好家伙，这么复杂结果还不如上一个L2损失。

实验结果：
Best test box ap: 44.40395830547559, in step: 66000

## 注：
该方法需要两个模型的回归目标、分类目标一一对应，因此需要观察模型输出并手动给出对应pair，因此目前只支持yolo。

```python
!python slim/distillation/distill.py \
-c configs/yolov3_mobilenet_v1_voc.yml -o use_fine_grained_loss=true\
-t configs/yolov3_r34_voc.yml \
--teacher_pretrained https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34_voc.tar \
-r output/yolov3_mobilenet_v1_voc/38000
```

2021-04-30 11:18:53,827 - INFO - step 68400 lr 0.000010, loss 21.284683, distill_loss 11.928459, teacher_loss 9.073975
2021-04-30 11:19:14,828 - INFO - step 68500 lr 0.000010, loss 27.378477, distill_loss 11.061569, teacher_loss 15.730307
2021-04-30 11:19:36,127 - INFO - step 68600 lr 0.000010, loss 19.714718, distill_loss 11.936641, teacher_loss 7.292910
2021-04-30 11:19:57,737 - INFO - step 68700 lr 0.000010, loss 48.085701, distill_loss 31.413290, teacher_loss 13.044544
2021-04-30 11:20:16,128 - INFO - step 68800 lr 0.000010, loss 51.778694, distill_loss 17.455339, teacher_loss 24.325071
2021-04-30 11:20:41,966 - INFO - step 68900 lr 0.000010, loss 42.863945, distill_loss 30.657480, teacher_loss 15.654951
2021-04-30 11:21:03,053 - INFO - step 69000 lr 0.000010, loss 27.961176, distill_loss 12.550332, teacher_loss 14.125258
2021-04-30 11:21:22,872 - INFO - step 69100 lr 0.000010, loss 43.735428, distill_loss 19.787128, teacher_loss 16.448784
2021-04-30 11:21:43,249 - INFO - step 69200 lr 0.000010, loss 28.012207, distill_loss 14.070652, teacher_loss 10.715887
2021-04-30 11:22:05,127 - INFO - step 69300 lr 0.000010, loss 38.618095, distill_loss 18.164053, teacher_loss 17.802208
2021-04-30 11:22:25,139 - INFO - step 69400 lr 0.000010, loss 48.910019, distill_loss 23.584068, teacher_loss 18.871468
2021-04-30 11:22:46,030 - INFO - step 69500 lr 0.000010, loss 29.586151, distill_loss 12.643631, teacher_loss 15.415883
2021-04-30 11:23:07,627 - INFO - step 69600 lr 0.000010, loss 33.945679, distill_loss 17.252405, teacher_loss 14.374951
2021-04-30 11:23:28,778 - INFO - step 69700 lr 0.000010, loss 33.225529, distill_loss 12.227975, teacher_loss 16.934584
2021-04-30 11:23:49,270 - INFO - step 69800 lr 0.000010, loss 30.932293, distill_loss 16.380510, teacher_loss 13.527035
2021-04-30 11:24:10,253 - INFO - step 69900 lr 0.000010, loss 25.630695, distill_loss 13.440517, teacher_loss 9.713081
2021-04-30 11:24:32,141 - INFO - Save model to output/yolov3_mobilenet_v1_voc/model_final.
2021-04-30 11:24:35,957 - INFO - Test iter 0
2021-04-30 11:24:44,138 - INFO - Test iter 100
2021-04-30 11:24:52,015 - INFO - Test iter 200
2021-04-30 11:24:52,835 - INFO - Test finish iter 215
2021-04-30 11:24:52,836 - INFO - Total number of images: 1713, inference time: 100.76612474202045 fps.
2021-04-30 11:24:52,837 - INFO - Start evaluate...
2021-04-30 11:24:53,403 - INFO - Accumulating evaluatation results...
2021-04-30 11:24:53,466 - INFO - mAP(0.50, 11point) = 42.63%
2021-04-30 11:24:53,471 - INFO - Best test box ap: 44.40395830547559, in step: 66000

```python
!mkdir FineGaineddistill
!mv output/yolov3_mobilenet_v1_voc/model_final.* FineGaineddistill
```

```python
!rm -rf output/
```

# 我的公众号：

![](https://github.com/Sharpiless/Yolov3-MobileNet-Pruning/blob/main/code.png)