YOLOv3复现代码合集涵盖 5 种常用深度学习框架:
Project |
Infernece |
Train |
Star |
gluoncv |
√ |
√ |
3187 |
3.1使用YOLOv3训练、使用Mask-RCNN训练、理解ResNet、模型部署、人脸识别、文本分类等:
3.2基于yolo3 与crnn 实现中文自然场景文字检测及识别
3.3 YOLOv3 in PyTorch > ONNX > CoreML > iOS
3.4YoloV3/tiny-YoloV3+RaspberryPi3/Ubuntu LaptopPC+NCS/NCS2+USB Camera+Python+OpenVINO
5.1 Enriching Variety of Layer-wise Learning Information by Gradient Combination
Model |
Size |
[email protected] |
BFLOPs |
EfficientNet_b0-PRN |
416x416 |
45.5 |
3.730 |
EfficientNet_b0-PRN |
320x320 |
41.0 |
2.208 |
5.2 Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving
5.3 YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection
Model |
model Size |
mAP(voc 2007) |
computational cost(ops) |
Tiny YOLOv2[13] |
60.5MB |
57.1% |
6.97B |
Tiny YOLOv3[14] |
33.4MB |
58.4% |
5.52B |
YOLO Nano |
4.0MB |
69.1% |
4.57B |
5.4YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers
DataSet |
mAP |
FPS |
PASCAL VOC |
33.57 |
21 |
COCO |
12.26 |
21 |
5.5 SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications
5.6 Strongeryolo-pytorch - Pytorch implementation of Stronger-Yolo with channel-pruning
Performance on VOC2007 Test(mAP) after pruning
Model |
Backbone |
MAP |
Flops(G) |
Params(M) |
strongerv3 |
Mobilev2 |
79.6 |
4.33 |
6.775 |
strongerv3-sparsed |
Mobilev2 |
77.4 |
4.33 |
6.775 |
strongerv3-Pruned(30% pruned) |
Mobilev2 |
77.1 |
3.14 |
3.36 |
strongerv2 |
Darknet53 |
80.2 |
49.8 |
61.6 |
strongerv2-sparsed |
Darknet53 |
78.1 |
49.8 |
61.6 |
strongerv2-Pruned(20% pruned) |
Darknet53 |
76.8 |
49.8 |
45.2 |
5.7 Learning Spatial Fusion for Single-Shot Object Detection
System |
test-dev mAP |
Time (V100) |
Time (2080ti) |
YOLOv3 608 |
33.0 |
20ms |
24ms |
YOLOv3 608+ BoFs |
37.0 |
20ms |
24ms |
YOLOv3 608(ours baseline) |
38.8 |
20ms |
24ms |
YOLOv3 608+ ASFF |
40.6 |
22ms |
28ms |
YOLOv3 608+ ASFF* |
42.4 |
22ms |
29ms |
YOLOv3 800+ ASFF* |
43.9 |
34ms |
40ms |