常用网站

openvino

openvino官方：https://docs.openvino.ai/latest/index.html

openvino开放的model：https://gitcode.net/openvinotoolkit/open_model_zoo

openvino例子：https://gitcode.net/openvinotoolkit/openvino_notebooks

drknet

darknet官方：https://pjreddie.com/darknet/

官方darknet git：https://gitcode.net/mirrors/pjreddie/darknet

其他darknet (cuda) 高版本 git：https://gitcode.net/mirrors/alexeyab/darknet

中文注释darknet git：https://gitcode.net/mirrors/hgpvision/darknet

将yolo的训练权重转换为.pb文件：https://gitcode.net/mirrors/mystic123/tensorflow-yolo-v3

预训练模型：

FPS on RTX 2070 (R) and Tesla V100 (V):

yolov4-p6.cfg - 1280x1280 - 72.1% mAP@0.5 (54.0% AP@0.5:0.95) - 32(V) FPS - xxx BFlops (xxx FMA) - 487 MB: yolov4-p6.weights
- pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-p6.conv.289
yolov4-p5.cfg - 896x896 - 70.0% mAP@0.5 (51.6% AP@0.5:0.95) - 43(V) FPS - xxx BFlops (xxx FMA) - 271 MB: yolov4-p5.weights
- pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-p5.conv.232
yolov4-csp-x-swish.cfg - 640x640 - 69.9% mAP@0.5 (51.5% AP@0.5:0.95) - 23(R) FPS / 50(V) FPS - 221 BFlops (110 FMA) - 381 MB: yolov4-csp-x-swish.weights
- pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp-x-swish.conv.192
yolov4-csp-swish.cfg - 640x640 - 68.7% mAP@0.5 (50.0% AP@0.5:0.95) - 70(V) FPS - 120 (60 FMA) - 202 MB: yolov4-csp-swish.weights
- pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp-swish.conv.164
yolov4x-mish.cfg - 640x640 - 68.5% mAP@0.5 (50.1% AP@0.5:0.95) - 23(R) FPS / 50(V) FPS - 221 BFlops (110 FMA) - 381 MB: yolov4x-mish.weights
- pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.conv.166
yolov4-csp.cfg - 202 MB: yolov4-csp.weights paper Scaled Yolo v4

just change width= and height= parameters in yolov4-csp.cfg file and use the same yolov4-csp.weights file for all cases:
- width=640 height=640 in cfg: 67.4% mAP@0.5 (48.7% AP@0.5:0.95) - 70(V) FPS - 120 (60 FMA) BFlops
- width=512 height=512 in cfg: 64.8% mAP@0.5 (46.2% AP@0.5:0.95) - 93(V) FPS - 77 (39 FMA) BFlops
- pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.conv.142
yolov4.cfg - 245 MB: yolov4.weights (Google-drive mirror yolov4.weights ) paper Yolo v4 just change width= and height= parameters in yolov4.cfg file and use the same yolov4.weights file for all cases:
- width=608 height=608 in cfg: 65.7% mAP@0.5 (43.5% AP@0.5:0.95) - 34(R) FPS / 62(V) FPS - 128.5 BFlops
- width=512 height=512 in cfg: 64.9% mAP@0.5 (43.0% AP@0.5:0.95) - 45(R) FPS / 83(V) FPS - 91.1 BFlops
- width=416 height=416 in cfg: 62.8% mAP@0.5 (41.2% AP@0.5:0.95) - 55(R) FPS / 96(V) FPS - 60.1 BFlops
- width=320 height=320 in cfg: 60% mAP@0.5 ( 38% AP@0.5:0.95) - 63(R) FPS / 123(V) FPS - 35.5 BFlops
yolov4-tiny.cfg - 40.2% mAP@0.5 - 371(1080Ti) FPS / 330(RTX2070) FPS - 6.9 BFlops - 23.1 MB: yolov4-tiny.weights
enet-coco.cfg (EfficientNetB0-Yolov3) - 45.5% mAP@0.5 - 55(R) FPS - 3.7 BFlops - 18.3 MB: enetb0-coco_final.weights
yolov3-openimages.cfg - 247 MB - 18(R) FPS - OpenImages dataset: yolov3-openimages.weights

CLICK ME - Yolo v3 models

csresnext50-panet-spp-original-optimal.cfg - 65.4% mAP@0.5 (43.2% AP@0.5:0.95) - 32(R) FPS - 100.5 BFlops - 217 MB: csresnext50-panet-spp-original-optimal_final.weights
yolov3-spp.cfg - 60.6% mAP@0.5 - 38(R) FPS - 141.5 BFlops - 240 MB: yolov3-spp.weights
csresnext50-panet-spp.cfg - 60.0% mAP@0.5 - 44 FPS - 71.3 BFlops - 217 MB: csresnext50-panet-spp_final.weights
yolov3.cfg - 55.3% mAP@0.5 - 66(R) FPS - 65.9 BFlops - 236 MB: yolov3.weights
yolov3-tiny.cfg - 33.1% mAP@0.5 - 345(R) FPS - 5.6 BFlops - 33.7 MB: yolov3-tiny.weights
yolov3-tiny-prn.cfg - 33.1% mAP@0.5 - 370(R) FPS - 3.5 BFlops - 18.8 MB: yolov3-tiny-prn.weights

CLICK ME - Yolo v2 models

yolov2.cfg (194 MB COCO Yolo v2) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov2.weights
yolo-voc.cfg (194 MB VOC Yolo v2) - requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo-voc.weights
yolov2-tiny.cfg (43 MB COCO Yolo v2) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov2-tiny.weights
yolov2-tiny-voc.cfg (60 MB VOC Yolo v2) - requires 1 GB GPU-RAM: http://pjreddie.com/media/files/yolov2-tiny-voc.weights
yolo9000.cfg (186 MB Yolo9000-model) - requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo9000.weights

预训练模型权重：

for yolov4.cfg, yolov4-custom.cfg (162 MB): yolov4.conv.137 (Google drive mirror yolov4.conv.137 )
for yolov4-tiny.cfg, yolov4-tiny-3l.cfg, yolov4-tiny-custom.cfg (19 MB): yolov4-tiny.conv.29
for csresnext50-panet-spp.cfg (133 MB): csresnext50-panet-spp.conv.112
for yolov3.cfg, yolov3-spp.cfg (154 MB): darknet53.conv.74
for yolov3-tiny-prn.cfg , yolov3-tiny.cfg (6 MB): yolov3-tiny.conv.11
for enet-coco.cfg (EfficientNetB0-Yolov3) (14 MB): enetb0-coco.conv.132