YOLO5Landmark实验记录

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前言

这里记录每一次运行的实验记录

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yolo5s6

本地运行&＃xff1a;

训练&＃xff1a;

Model Summary: 377 layers, 13149336 parameters, 13149336 gradients, 18.6 GFLOPS
## 测试 last.pt&＃xff1a;
0.925 0.982 0.958 0.913
nme:0.04828772978002897
Speed: 4.0/0.6/4.7 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp4
### 测试 best.pt&＃xff1a;
0.93 0.955 0.978 0.933
nme:0.04658510462525046
Speed: 4.1/0.7/4.8 ms inference/NMS/total per 640x640 image at batch-size 4

yolo5s6 &＃43; NoPretrain

本地运行&＃xff1a;

训练&＃xff1a;

377 layers, 13149336 parameters, 13149336 gradients, 18.6 GFLOPS

Transferred 155/480 items from weights/yolov5s.pt

测试

Fusing layers…
Model Summary: 300 layers, 13135096 parameters, 0 gradients, 18.4 GFLOPS

0.952 0.96 0.982 0.94
nme:0.04449336785480576
Speed: 4.0/0.6/4.6 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp6

yolo7stem

训练

Model Summary: 385 layers, 13142776 parameters, 13142776 gradients, 18.0 GFLOPS

Transferred 12/492 items from weights/yolov5s.pt

测试

Fusing layers…
Model Summary: 306 layers, 13128472 parameters, 0 gradients, 17.8 GFLOPS

0.946 0.951 0.979 0.931
nme:0.04381672315071253
Speed: 4.2/0.6/4.7 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp7

4gpu&＃43;32bs训练

all 554 554 0.923 0.968 0.979 0.931


nme:0.04370922955195993
Speed: 4.8/5.5/10.3 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp2

IOU&＃61;0.5 conf &＃61;0.1:
all 554 554 0.923 0.968 0.98 0.931
nme:0.04878550926798806
Speed: 4.7/3.8/8.5 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp3

yolo7 &＃43; RepVGGBlockStem

本地运行&＃xff1a;

训练&＃xff1a;

Model Summary: 395 layers, 13144024 parameters, 13144024 gradients, 18.2 GFLOPS

Transferred 30/504 items from weights/yolov5s6.pt

测试 best.pt&＃xff1a;

Fusing layers…
Model Summary: 308 layers, 13128472 parameters, 10144 gradients, 17.8 GFLOPS

0.943 0.958 0.978 0.929
nme:0.04634316943127357
Speed: 4.2/0.6/4.8 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp5

yolo7 &＃43; RepStem

本地运行&＃xff1a;

训练&＃xff1a;

Model Summary: 393 layers, 13144024 parameters, 13144024 gradients, 18.2 GFLOPS

Transferred 12/504 items from weights/yolov5s.pt

测试 best.pt&＃xff1a;

Fusing layers…
RepConv.fuse_repvgg_block
RepConv.fuse_repvgg_block
Model Summary: 306 layers, 13128472 parameters, 10144 gradients, 17.8 GFLOPS

0.952 0.939 0.981 0.935
nme:0.043321637678518515
Speed: 4.3/0.6/4.8 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp8

yolo7M &＃43; RepStem

python train.py --data w300_kpts.yaml --cfg yolov5m6_w300kpts_RepStem.yaml --weights weights/yolov5m6.pt --batch-size 32 --workers 4 --device 3 --img 640 --kpt-label

本地运行&＃xff1a;

训练&＃xff1a;

Model Summary: 528 layers, 36628248 parameters, 36628248 gradients, 53.6 GFLOPS

Transferred 42/684 items from weights/yolov5m6.pt

Image sizes 640 train, 640 test
Using 4 dataloader workers batch size 20

(yolo) root&＃64;f5e52d307978:~/Projects/yolo5landmark# python test300WV1.py --data w300_kpts.yaml --img 640 --conf 0.1 --iou 0.5 --batch-size 4 --device 2 --weights runs/train/exp6/weights/best.pt --kpt-label

测试 best.pt&＃xff1a;

YOLOv5 &＃xfffd; 2022-12-30 torch 1.11.0 CUDA:2 (NVIDIA GeForce RTX 3090, 24268.3125MB)

Fusing layers…
RepConv.fuse_repvgg_block
RepConv.fuse_repvgg_block
Model Summary: 411 layers, 36598104 parameters, 22128 gradients, 53.0 GFLOPS

all 554 554 0.931 0.977 0.983 0.936
nme:0.043028827631862665
Speed: 6.7/4.9/11.6 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp5

---

yolo7L &＃43; 4GPU &＃43; RepStem

python train.py --data w300_kpts.yaml --cfg yolov5l6_w300kpts_RepStem.yaml --weights weights/yolov5l6.pt --batch-size 16 --workers 4 --device 0,1,2,3 --img 640 --kpt-label

本地运行&＃xff1a;

训练&＃xff1a;

Model Summary: 663 layers, 78207960 parameters, 78207960 gradients, 119.4 GFLOPS

Transferred 54/864 items from weights/yolov5l6.pt
Scaled weight_decay &＃61; 0.00046875
Optimizer groups: 149 .bias, 149 conv.weight, 141 other

Image sizes 640 train, 640 test
Using 4 dataloader workers batch size 16
all 554 554 0.896 0.962 0.964 0.916
300 epochs completed in 15.514 hours.

(yolo) root&＃64;f5e52d307978:~/Projects/yolo5landmark# python test300WV1.py --data w300_kpts.yaml --img 640 --conf 0.1 --iou 0.5 --batch-size 4 --device 2 --weights runs/train/exp11/weights/best.pt --kpt-label

测试 best.pt&＃xff1a;

YOLOv5 &＃xfffd; 2022-12-30 torch 1.11.0 CUDA:2 (NVIDIA GeForce RTX 3090, 24268.3125MB)

Model Summary: 516 layers, 78158680 parameters, 38720 gradients, 118.4 GFLOPS

all 554 554 0.897 0.958 0.965 0.919
nme:0.04442429334286103
Speed: 6.2/0.7/6.9 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp6

yolo7L &＃43; 1GPU &＃43; RepStem

python train.py --data w300_kpts.yaml --cfg yolov5l6_w300kpts_RepStem.yaml --weights weights/yolov5l6.pt --batch-size 8 --workers 2 --device 0 --img 640 --kpt-label

本地运行&＃xff1a;

训练&＃xff1a;

Model Summary: 663 layers, 78207960 parameters, 78207960 gradients, 119.4 GFLOPS

Transferred 54/864 items from weights/yolov5l6.pt
Scaled weight_decay &＃61; 0.00046875
Optimizer groups: 149 .bias, 149 conv.weight, 141 other

Image sizes 640 train, 640 test
Using 2 dataloader workers batch size 8

0.956 0.946 0.981 0.933
300 epochs completed in 14.915 hours.

Optimizer stripped from runs/train/exp13/weights/last.pt, 157.0MB

(yolo) root&＃64;f5e52d307978:~/Projects/yolo5landmark# python test300WV1.py --data w300_kpts.yaml --img 640 --conf 0.001 --iou 0.65 --batch-size 4 --device 2 --weights runs/train/exp13/weights/best.pt --kpt-label

测试 best.pt&＃xff1a;

YOLOv5 &＃xfffd; 2022-12-30 torch 1.11.0 CUDA:2 (NVIDIA GeForce RTX 3090, 24268.3125MB)

Model Summary: 516 layers, 78158680 parameters, 38720 gradients, 118.4 GFLOPS

0.948 0.947 0.981 0.935
nme:0.04148737189304469
Speed: 7.7/2.9/10.5 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp7

yolo7 &＃43; RepStem &＃43; RepBot

本地运行&＃xff1a;

训练&＃xff1a;

Model Summary: 648 layers, 13497112 parameters, 13497112 gradients, 18.9 GFLOPS

Transferred 337/864 items from runs/train/exp5/weights/best.pt

测试 best.pt&＃xff1a;

Model Summary: 411 layers, 13130488 parameters, 3083104 gradients, 17.8 GFLOPS

0.941 0.958 0.98 0.934
nme:0.04262262558182343
Speed: 4.7/0.6/5.3 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp9

yolo7 &＃43; RepStem &＃43; RepBot &＃43; IKeypoint

本地运行&＃xff1a;

训练&＃xff1a;

Model Summary: 658 layers, 13498464 parameters, 13498464 gradients, 18.9 GFLOPS
Transferred 862/872 items from runs/train/exp6/weights/best.pt

测试 best.pt&＃xff1a;

Model Summary: 421 layers, 13131840 parameters, 3083104 gradients, 17.8 GFLOPS

0.934 0.975 0.98 0.938
nme:0.04131272826509372
Speed: 4.7/0.6/5.3 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp15

yolo7 &＃43; RepStem &＃43; ShuffleBot

本地运行&＃xff1a;

训练&＃xff1a;

Model Summary: 423 layers, 9911656 parameters, 9911656 gradients, 12.1 GFLOPS
Transferred 322/594 items from runs/train/exp5/weights/best.pt

测试 best.pt&＃xff1a;

Fusing layers…
RepConv.fuse_repvgg_block
RepConv.fuse_repvgg_block
Model Summary: 366 layers, 9900136 parameters, 10144 gradients, 11.8 GFLOPS

0.951 0.942 0.977 0.929
nme:0.04308880185174582
Speed: 4.0/0.6/4.6 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp10

yolo7 &＃43; RepStem &＃43; Shuffle2Bot

本地运行&＃xff1a;

训练&＃xff1a;

Model Summary: 423 layers, 13254648 parameters, 13254648 gradients, 17.2 GFLOPS
Transferred 246/594 items from runs/train/exp8/weights/best.pt

测试 best.pt&＃xff1a;

Fusing layers…
RepConv.fuse_repvgg_block
RepConv.fuse_repvgg_block
Model Summary: 366 layers, 13239864 parameters, 10144 gradients, 16.9 GFLOPS

0.936 0.955 0.978 0.931
nme:0.042702530044724414
Speed: 4.7/0.6/5.4 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp11

yolo7 &＃43; RepStem &＃43; RepShuffle2Bot (RepVGG Block试下&＃xff0c;看能否解决维度大于2)

本地运行&＃xff1a;

训练&＃xff1a;

Model Summary: 558 layers, 13268760 parameters, 13268760 gradients, 17.3 GFLOPS
Transferred 577/834 items from runs/train/exp9/weights/best.pt

测试 best.pt&＃xff1a;

switch_to_deploy
Model Summary: 411 layers, 13241880 parameters, 30304 gradients, 16.9 GFLOPS

0.922 0.968 0.979 0.932
nme:0.04306018083638277
Speed: 4.8/0.6/5.4 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp16

yolo7 &＃43; RepStem &＃43; RepShuffle2Bot &＃43; Ikeypoint (RepVGG Block能解决维度大于2)

本地运行&＃xff1a;

训练&＃xff1a;

Model Summary: 568 layers, 13270112 parameters, 13270112 gradients, 17.3 GFLOPS
Transferred 832/842 items from runs/train/exp13/weights/best.pt

测试 best.pt&＃xff1a;

switch_to_deploy
Model Summary: 421 layers, 13243232 parameters, 30304 gradients, 16.9 GFLOPS

0.937 0.962 0.976 0.933
nme:0.039646686695125256
Speed: 4.8/0.7/5.5 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp17

yolo7 &＃43; RepStem &＃43; RepShuffle2Bot &＃43; Ikeypoint &＃43;DW-CNN KPoint

本地运行&＃xff1a;

训练&＃xff1a;

Model Summary: 748 layers, 15824992 parameters, 15824992 gradients, 20.5 GFLOPS
Transferred 832/1106 items from runs/train/exp15/weights/best.pt Scaled weight_decay &＃61; 0.0005
Optimizer groups: 193 .bias, 163 conv.weight, 185 other
Image sizes 640 train, 640 test Using 2 dataloader workers with BS&＃61;8 Runs/train/exp18 Starting training for 320 epochs…

测试 best.pt&＃xff1a;

switch_to_deploy
Model Summary: 421 layers, 13243232 parameters, 30304 gradients, 16.9 GFLOPS

0.937 0.962 0.976 0.933
nme:0.039646686695125256
Speed: 4.8/0.7/5.5 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp17

yolo7m &＃43; RepStem &＃43; RepShuffle2Bot &＃43; Ikeypoint

训练&＃xff1a;2GPU

python train.py --data w300_kpts.yaml --cfg yolov5m6_w300kpts_RepStem_RepShuffle2Bot_IKeypoint.yaml --weights runs/train/exp6/weights/best.pt --batch-size 16 --workers 4 --device 3,4 --img 640 --kpt-label
Logging results to runs/train/exp17
0.948 0.964 0.983 0.94
300 epochs completed in 9.100 hours.

Optimizer stripped from runs/train/exp17/weights/last.pt, 62.4MB

测试 best.pt&＃xff1a;

(yolo) root&＃64;f5e52d307978:~/Projects/yolo5landmark# python test300WV1.py --data w300_kpts.yaml --img 640 --conf 0.001 --iou 0.65 --batch-size 4 --device 2 --weights runs/train/exp17/weights/best.pt --kpt-label

0.952 0.962 0.984 0.941
nme:0.041504019045823154
Speed: 7.6/2.2/9.9 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp9

训练&＃xff08;1GPU&＃xff09;

python train.py --data w300_kpts.yaml --cfg yolov5m6_w300kpts_RepStem_RepShuffle2Bot_IKeypoint.yaml --weights runs/train/exp6/weights/best.pt --batch-size 8 --workers 2 --device 7 --img 640 --kpt-label

Model Summary: 868 layers, 30795360 parameters, 30795360 gradients, 40.3 GFLOPS
Transferred 201/1352 items from runs/train/exp6/weights/best.pt
Image sizes 640 train, 640 test
Using 2 dataloader workers
Logging results to runs/train/exp18

测试 best.pt&＃xff1a;

(yolo) root&＃64;f5e52d307978:~/Projects/yolo5landmark# python test300WV1.py --data w300_kpts.yaml --img 640 --conf 0.001 --iou 0.65 --batch-size 4 --device 2 --weights runs/train/exp18/weights/best.pt --kpt-label

0.938 0.962 0.975 0.93
nme:0.04204403050927501
Speed: 8.0/3.0/11.0 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp10

yolo7L &＃43; RepStem &＃43; RepShuffle2Bot &＃43; Ikeypoint

训练&＃xff1a;4GPU

python train.py --data w300_kpts.yaml --cfg yolov5l6_w300kpts_RepStem_RepShuffle2Bot_IKeypoint.yaml --weights runs/train/exp13/weights/best.pt --batch-size 16 --workers 4 --device 3,4,5,6 --img 640 --kpt-label

Model Summary: 1168 layers, 56945568 parameters, 56945568 gradients, 75.5 GFLOPS

Transferred 201/1862 items from runs/train/exp13/weights/best.pt
Image sizes 640 train, 640 test
Using 4 dataloader workers
Logging results to runs/train/exp19

测试 best.pt&＃xff1a;

(yolo) root&＃64;f5e52d307978:~/Projects/yolo5landmark# python test300WV1.py --data w300_kpts.yaml --img 640 --conf 0.001 --iou 0.65 --batch-size 4 --device 2 --weights runs/train/exp17/weights/best.pt --kpt-label

0.952 0.962 0.984 0.941
nme:0.041504019045823154
Speed: 7.6/2.2/9.9 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp9

训练&＃xff08;1GPU&＃xff09;

python train.py --data w300_kpts.yaml --cfg yolov5l6_w300kpts_RepStem_RepShuffle2Bot_IKeypoint.yaml --weights runs/train/exp13/weights/best.pt --batch-size 8 --workers 2 --device 7 --img 640 --kpt-label

Model Summary: 1168 layers, 56945568 parameters, 56945568 gradients, 75.5 GFLOPS

Transferred 201/1862 items from runs/train/exp13/weights/best.pt
Image sizes 640 train, 640 test
Using 2 dataloader workers
Logging results to runs/train/exp20
Starting training for 300 epochs…

测试 best.pt&＃xff1a;

(yolo) root&＃64;f5e52d307978:~/Projects/yolo5landmark# python test300WV1.py --data w300_kpts.yaml --img 640 --conf 0.001 --iou 0.65 --batch-size 4 --device 2 --weights runs/train/exp18/weights/best.pt --kpt-label

0.938 0.962 0.975 0.93
nme:0.04204403050927501
Speed: 8.0/3.0/11.0 ms inference/NMS/total per 640x640 image at batch-size 4
Results saved to runs/test/exp10

yolo5L &＃43; RepStem &＃43; RepShuffle2Bot &＃43; Ikeypoint (RepVGG Block能解决维度大于2)

本地运行&＃xff1a;

训练&＃xff1a;

Model Summary: 1168 layers, 56945568 parameters, 56945568 gradients, 75.5 GFLOPS
Transferred 1862/1862 items from runs/train/exp17/weights/last.pt

测试 best.pt&＃xff1a;

switch_to_deploy
Model Summary: 841 layers, 56841376 parameters, 159680 gradients, 74.2 GFLOPS

nme:0.041191850604322004
Speed: 19.4/0.7/20.2 ms inference/NMS/total per 640x640 image at batch-size 1
Results saved to runs/test/exp19

yolo7 &＃43; RepStem &＃43; RepShuffle2Bot (RepConv因为维度为1,导致fuse出错)

本地运行&＃xff1a;

训练&＃xff1a;

Model Summary: 543 layers, 13268760 parameters, 13268760 gradients, 17.3 GFLOPS

Transferred 577/834 items from runs/train/exp9/weights/best.pt

测试 best.pt&＃xff1a;

Model Summary: 396 layers, 13241880 parameters, 30304 gradients, 16.9 GFLOPS

0.842 0.791 0.794 0.598
nme:0.1406149227627627
Speed: 4.8/0.6/5.4 ms inference/NMS/total per 640x640 image at batch-size 4

yolo7 &＃43; RepStem &＃43; RepShuffle2Bot (RepConv后去掉BN层)

本地运行&＃xff1a;

训练&＃xff1a;

Model Summary: 528 layers, 13264728 parameters, 13264728 gradients, 17.3 GFLOPS

Transferred 412/759 items from runs/train/exp9/weights/best.pt

测试 best.pt&＃xff1a;

出错

yolo7 &＃43; RepStem &＃43; RepShuffle2Bot (DW换成group/2)

本地运行&＃xff1a;

训练&＃xff1a;

Model Summary: 543 layers, 9928792 parameters, 9928792 gradients, 12.2 GFLOPS

Transferred 246/834 items from runs/train/exp9/weights/best.pt

测试 best.pt&＃xff1a;

出错

二、使用步骤

1.引入库

代码如下&＃xff08;示例&＃xff09;&＃xff1a;

{
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid&＃61;830387
"version": "0.2.0",
"configurations": [
{
"name": "Python: Current File",
"type": "python",
"request": "launch",
"program": "${file}",
"console": "integratedTerminal",
"args": [
// // preprocess data_300W or generate300W challenge and common dataset
// "data_300W"
// "COFW"
// "data_300W_COFW_WFLW"
// // train yolo_pose
// "--data","coco_kpts.yaml",
// "--cfg", "yolov5s6_kpts.yaml",
// "--weights", "weights/yolov5s6.pt",
// "--batch-size", "4",
// "--img", "640",
// "--kpt-label"
// // // train yolo_facepose
// "--data","w300_kpts.yaml",
// // "--cfg", "yolov5s6_w300kpts_SimpleRepStem_ShuffleCSPAIAuxDetect.yaml",
// "--cfg", "yolov5s6_w300kpts_SimpleRepStem_ShuffleCSPA.yaml", // exp90 117 118 122
// // "--cfg", "yolov5s6_w300kpts_SimpleRepStem_ResX.yaml", // exp77 &＃43; 113
// // "--cfg", "yolov5s6_w300kpts_SimpleRepStemRepBot.yaml", //119
// // "--cfg", "yolov5s6_w300kpts_SimpleRepStemGhostBot.yaml",
// // "--cfg", "yolov5s6_w300kpts_SimpleRepStem.yaml", //110
// // "--resume", "runs/train/exp72/weights/last.pt",
// // "--cfg", "yolov5s6_w300kpts_stem_rep.yaml",
// // "--resume", "runs/train/exp57/weights/last.pt",
// // "--cfg", "yolov5s6_w300kpts.yaml", //109
// // "--resume", "runs/train/exp109/weights/last.pt",
// // "--cfg", "yolov5s6_w300kpts_stem_yolo7.yaml",
// // "--cfg", "yolov5s6_w300kpts_repmore.yaml",
// // "--resume", "runs/train/exp49/weights/last.pt",
// // "--cfg", "yolov5s6_w300kpts_ti_lite.yaml",
// // "--resume", "runs/train/exp28/weights/last.pt",
// // "--resume", "runs/train/exp/weights/last.pt",
// // "--cfg", "yolov5s6_w300kpts_ti_lite_deconv.yaml",
// // "--resume", "runs/train/exp27/weights/last.pt",
// // "--cfg", "yolov5l6_w300kpts_ti_lite.yaml",
// // "--resume", "runs/train/exp22/weights/last.pt",
// "--weights", "weights/yolov5s.pt",
// // "--weights", "weights/yolov5s6_640_ti_lite.pt",
// "--batch-size", "8",
// "--img", "640",
// // "--img", "960",
// // "--workers","8",
// "--epochs","300",
// // "--bbox_interval","30",
// "--kpt-label",
// "--adam"
// // "--facekpt-label"
// // // python train.py --data coco_kpts.yaml --cfg yolov5s6_kpts.yaml --weights &＃39;path to the pre-trained ckpts&＃39; --batch-size 64 --img 640 --kpt-label
// // //test yolo_facepose
// "--data","w300_kpts.yaml",
// // "--img", "960",
// "--img", "640",
// "--iou"," 0.5", // use default value 0.65
// "--conf", "0.1", //original
// // "--conf", "0.02",
// // "--conf", "0.3",
// "--batch-size","16",
// // "--weights", "weights/yolov5s6.pt",
// "--weights", "runs/train/exp118/weights/best.pt",
// "--kpt-label"
// //"--facekpt-label"
// // python test.py --data coco_kpts.yaml --img 960 --conf 0.001 --iou 0.65 --weights "path to the pre-trained ckpt" --kpt-label
// to run detect_face
"--img-size", "800",
"--iou"," 0.65",
"--conf", "0.003", //original
// "--conf", "0.1",
"--weights", "runs/train/exp19/weights/best.pt",
"--source", "data/images/zidane.jpg"
// "--image", "data/images/jordan.jpg"
// "--image", "data/images/she2.jpg"
// "--image", "data/images/lfpw_testset_image_0178.png"
// "--image", "data/yolo_300W/images/test/ibug_image_047_1.jpg"
// "--image", "data/yolo_300W/images/test/ibug_image_048.jpg" //bad
// "--image", "data/yolo_300W/images/test/ibug_image_076_1.jpg"
// "--image", "data/yolo_300W/images/test/ibug_image_065_1.jpg"
// "--image", "data/yolo_300W/images/test/helen_testset_2988557119_1.jpg"
// "--image", "data/yolo_300W/images/train/helen_trainset_2203538277_1.jpg"
// "--image", "data/yolo_300W/images/test/helen_testset_315336719_1.jpg"
// "--image", "data/yolo_300W/images/test/helen_testset_2973812613_1.jpg"
// "--kpt-label"

// // to export model
// "--weights", "runs/train/exp19/weights/best.pt",
// "--batch", "1",
// "--img", "640",
// "--simplify",
// "--export-nms"
// // python export.py --weights "path to the pre-trained ckpt" --img 640 --batch 1 --simplify --export-nms # export at 640x640 with batch size 1
]
}
]
}
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings(&＃39;ignore&＃39;)
import ssl
ssl._create_default_https_context &＃61; ssl._create_unverified_context