pytorch_yolov3实验记录
- 安装
- git项目
- 配置coco数据集
- 修改data/coco2014.data文件
- 模型测试
- 输出
- 测试结果展示
- 小结
安装
复制代码
1
2
3
4
5
6
7
8
9
10
11环境介绍 Ubuntu18.04 Anaconda Python 3.7.7 GCC 7.3.0 torch 1.5 pycocotools 2.0 *必须使用numpy ==1.17.5 GPU 2070S 8G pip install -r requirements.txt
git项目
复制代码
1
2git clone https://github.com/ultralytics/yolov3.git
配置coco数据集
复制代码
1
2
3
4
5
6
7
8
9
10
11
12
13cd yolov3 mkdir coco coco路径展示 -------coco /images /trian2014 /val2014 /label /train2014 /val2014 |--5k.part |--tranvalo5k.part
复制代码
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16cd coco coco2014下载路径:https://pjreddie.com/media/files/train2014.zip https://pjreddie.com/media/files/val2014.zip 这个是Darknet的镜像路径,使用迅雷下载,速度比官方的速度快 解压文件:unzip -q train2014.zip unzip -q val2014.zip 下载注释文件:https://pjreddie.com/media/files/instances_train-val2014.zip https://pjreddie.com/media/files/coco/5k.part https://pjreddie.com/media/files/coco/trainvalno5k.part https://pjreddie.com/media/files/coco/labels.tgz tar xzf labels.tgz unzip -q instances_train-val2014.zip *在coco文件夹下执行 paste <(awk "{print "$PWD"}" <5k.part) 5k.part | tr -d 't' > 5k.txt paste <(awk "{print "$PWD"}" <trainvalno5k.part) trainvalno5k.part | tr -d 't' > trainvalno5k.txt
修改data/coco2014.data文件
复制代码
1
2
3
4
5
6
7
8
9原来内容展示 classes=80 train=../coco/train2014.txt valid=../coco/val2014.txt names=data/coco.names **修改路径,建议使用绝对路径 train= ../coco/trainvalno5k.txt valid= ../coco/5k.txt
模型测试
复制代码
1
2
3cd yolov3 python3 test.py --data data/coco2014.data --cfg cfg/yolov3-spp.cfg --weights weights/yolov3/yolov3-spp.pt --save-json --img-size 320
输出
复制代码
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15Namespace(augment=False, batch_size=16, cfg='cfg/yolov3.cfg', conf_thres=0.001, data='data/coco2014.data', device='', img_size=320, iou_thres=0.6, save_json=True, single_cls=False, task='test', weights='weights/yolov3/yolov3.pt') Using CUDA device0 _CudaDeviceProperties(name='GeForce RTX 2070 SUPER', total_memory=7979MB) Model Summary: 222 layers, 6.19491e+07 parameters, 6.19491e+07 gradients Fusing layers... Model Summary: 150 layers, 6.19228e+07 parameters, 6.19228e+07 gradients Caching labels /home/gob/yolov3/coco/labels/val2014.npy (4954 found, 0 missing, 46 empty, 197 duplicate, for 5000 images): 100%|█████████████████| 5000/5000 [00:00<00:00, 26131.86it/s] Class Images Targets P R mAP@0.5 F1: 100%|███████████████████████████████████████████████████████████████| 313/313 [00:51<00:00, 6.05it/s]
测试结果展示
复制代码
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69yolov3-spp测试结果 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.314 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.534 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.331 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.105 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.348 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.489 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.278 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.446 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.490 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.250 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.558 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.688 yolov3测试结果 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.295 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.521 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.306 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.111 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.331 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.455 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.265 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.430 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.474 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.236 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.545 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.661
小结
之前下载了来路不明的coco数据集,确缺少了很多图片。在这里也遇到了很多报错,但都一步步的解决了,除了基本的配置问题,路径问题之外,也对test.py和train.py文件的代码进行了修改。后期还会继续记录实验过程。。。。
最后
以上就是单薄发箍最近收集整理的关于pytorch_yolov3实验记录安装的全部内容,更多相关pytorch_yolov3实验记录安装内容请搜索靠谱客的其他文章。
本图文内容来源于网友提供,作为学习参考使用,或来自网络收集整理,版权属于原作者所有。
发表评论 取消回复