Here you can post your trained models on different Datasets - 3 files: cfg, weights, names

[AlexeyAB]

Here you can post your trained models on different Datasets - 3 files:

1. cfg
2. weights
3. names
4. Optional: accuracy mAP@0.5 or/and mAP@0.5...0.95
5. Optional: BFLOPS and/or Inference time (milliseconds)

---

COCO test-dev

Model	Size	BFLOPS	Inference time, ms	AP@.5:.95	AP@.5	AP@.75	URL
yolo_v3_tiny_pan3 aa_ae_mixup scale_giou (no sgdr).txt	416x416	8.4	6.4	18.8%	36.8%	17.5%	#3708 (comment)
yolov3-tiny-prn.cfg.txt	416x416	3.5	3.8	-	33.1%	-	URL
enet-coco.cfg.txt	416x416	3.7	22.7	-	45.5%	-	URL

---

ImageNet valid

Model	BFLOPs	Inference Time (ms)	Top1, %	URL
shufflenetv2 and weights	0.375	32	52%	URL
efficient_b0 and weights	0.915	76	71%	URL
MobileNet v2 and weights	0.85	-	67%	URL

[gameliee]

I would try
Dataset: Visdrone2019
1.cfg
yolov3-tiny_3l.cfg.txt
2. weights
https://drive.google.com/file/d/1fqWdpYpTJkKLnVhEiQQqm_SNvMp_qSE0/view?usp=sharing
3. names
visdrone2019.names.txt

Hope to see comments on these

[dexception]

MobileNet V2 Imagenet
Top 1: 0.670320, top 5: 0.876620
Iterations: 600,000

https://github.com/AlexeyAB/darknet/files/3310574/mobilenet_v2.cfg.txt
imagenet1k.data

mobilenetv2_last.weights
https://drive.google.com/file/d/1lW87XQtZIYKIqu8DHvJwBUupJBh4Zpdl/view?usp=sharing

[AlexeyAB]

@ntd94 Thanks. What mAP (accuracy) did you get on Visdrone2019 val/test?

[hasirk]

1. cfg:
   yolo-obj.cfg.txt

2. weights:
   https://bit.ly/2oa9Rt3

3. names

   • bike
   • bus
   • car
   • motor
   • person
   • rider
   • traffic light
   • traffic sign
   • train
   • truck

4. results:
   https://bit.ly/2nxWL8h

[ChenCong7375]

Any yolo_v3_tiny_pan3 aa_ae_mixup scale_giou weights? @AlexeyAB

[laclouis5]

Dataset

Custom small dataset with 6 classes (crop and stems), images are approx. 2 MPix with a 4/3 ratio.

For each class are tagged the whole plant and its stem. The stem annotation is a square box approx. the same relative size for each class and image.

There is large overlap for some bounding boxes, some plants are very small and others almost fit half the image.

Class	Images	Annotations
Bean	221	1118
Bean Stem	221	1176
Maize	489	889
Maize Stem	489	927
Leek	197	855
Leek Stem	197	927

Results

Model is trained for 10 000 steps using original parameters excepted for subdivisions that is adjusted to fit on the GPU (Nvidia GTX 1060 6GB). For old models (Tiny, Tiny 3L, ...) width and height are changed to 544 instead of 416.

I Will update this post as soon as I have new results. I will also try to compare AlexeyAB Darknet implementation with other realtime frameworks (CenterNet, Ultralytics, RetinaNet, ...).

Model	Config Files	Training Chart	mAP@0.5 (scratch / pre-trained)*	mAP@[0.5...0.95] (scratch / pre-trained)*	FPS
Yolo v4 (random=0, resize=1.5)	cfg		— / 90.00%	— / 50.73%	19fps
Yolo V3 CSR Spp Panet Optimal	cfg		— / 88.30%	— / 48.42%	18fps
Yolo v4 Tiny	cfg		— / 88.76%	— / 46.74%	146fps
Yolo V3 CSR Spp Panet	cfg		83.38% / 88.34%	42.21% / 45.88%	18fps
Tiny Yolo V3 Pan 3	cfg		87.16% / 87.46%	42.18% / 42.41%	65fps
Tiny Yolo V3 Pan Mixup	cfg		88.06%	40.00%	68fps
Tiny Yolo V3 Gaussian Matrix	cfg		86.84%	40.09%	—
Tiny Yolo V3 3L	cfg		83.93% / 84.41%	38.12% / 38.15%	120fps
Tiny Yolo V3 Prn	cfg		82.93%	33.82%	180fps
Tiny Yolo V3	cfg		82.39%	33.28%	140fps
Yolo V3 Spp Pan Scale	cfg		75.95%	31.76%	13fps

* Using pre-trained weights for deep networks such as yolo v3 and CSR significantly improves training stability and speed as well as the accuracy. This effect is less significant for shallower networks such as Tiny Yolo v3 Pan3.

Model	Config Files	Training Chart	mAP@0.5	mAP@[0.5...0.95]	FPS
CenterNet dla-34 512x512*	—	—	75.7%	41.6%	25fps
Ultralytics Yolov3-SPP 512x512	—	—	—	—

* Using pre-trained weights from ctdet_coco_dla_2x.pth

[Kyuuki93]

DataSet was one-class custom data with 33216 images for training and 4324 images for validation.

Experimental results will be updated continuously.

Model	CFG	AP@.5	AP@.75
spp,mse	cfg	89.52%	51.72%
spp,mse,it=0.213	-	92.01%	60.49%
spp,giou(in=0.07)	-	89.55%	51.25%
spp,giou(in=0.5)	-	90.08%	59.55%
spp,ciou(in=0.5)	-	89.49%	58.39%
spp,giou,gs(in,un=0.5)	cfg	91.39%	58.01%
spp,ciou,gs(in,un=0.5)	-	89.81%	59.01%
spp,giou,gs,mixup(in,un=0.5)	-	89.51%	58.73%
spp,giou,gs,mosaic(in,un=.5)	-	90.48%	60.02%
spp,giou,gs(in,un=0.5,it=0.213)	-	91.89%	63.53%
spp,giou,gs,swish(in,un=0.5,it=0.213)	-	91.54%	60.70%
spp,giou,gs,mosaic(in,un=0.5,it=0.213)	-	91.82%	63.89%
spp,mse,it=0.213,asff(in,un=0.5,it=0.213)	cfg	92.45%	61.83%
spp,giou,it=0.213,asff(in,un=0.5,it=0.213)	-	92.33%	63.74%
spp,giou,it=0.213,asff(softmax),rfb(bn=0)	-	91.57%	64.95%
spp,giou,it=0.213,asff(softmax),rfb(bn=1)	-	92.32%	60.05%
spp,giou,it=0.213,asff(softmax),dropblock(size=7) ,rfb(bn=0)	-	91.65%	61.35%
spp,giou,it=0.213,asff(softmax),dropblock(size=7) ,rfb(bn=1)	-	92.12%	63.88%

A summary,

method	AP@.5	AP@.75
iou_thresh	+	+
mse -> giou	-	+
giou -> ciou	-	+
gaussian_yolo	+	-
mixup	-	-
moasic	=	+
swish	-	-
asff	+	+
rfb	?	?
dropblock	?	?

[sctrueew]

@Kyuuki93 Hi,
Can you share the spp,giou,gs,mosaic(in,un=0.5,it=0.213) cfg file?

Thanks!

[Kyuuki93]

@Kyuuki93 Hi,
Can you share the spp,giou,gs,mosaic(in,un=0.5,it=0.213) cfg file?

Thanks!

just download spp,giou,gs(in,un=0.07), then

1. change iou_normalizer and uc_normalizer to 0.5 which now is 0.07
2. add mosaic = 1 in [net] session
3. add iou_thresh = 0.213 in all [Gaussion_yolo] session

[Kyuuki93]

In my application, precision, recall, AP@0.5 with high thresh, e.g. thresh = 0.7 or 0.85, were more important, based on #3874 (comment), create a new table here,

Model	AP@.5	AP@.75	precision (th=0.85)	recall(th=0.85)	precision (th=0.7)	recall(th=0.7)
spp,mse	89.50%	51.75%	0.98	0.20	0.97	0.36
spp,giou	90.09%	59.55%	0.98	0.25	0.97	0.40
spp,ciou	89.88%	58.39%	0.99	0.22	0.97	0.38
spp,giou,gs	91.39%	58.01%	0.99	0.05	0.97	0.47
spp,ciou,gs	89.82%	59.01%	0.99	0.02	0.99	0.24
spp,giou,gs,mixup	89.51%	58.73%	0.98	0.01	0.98	0.20
spp,giou,gs,mosaic	90.47%	63.89%	1.00	0.01	0.99	0.22
spp,mse,it	92.01%	60.49%	0.97	0.60	0.95	0.72
spp,giou,it	91.79%	63.09%	0.96	0.57	0.95	0.71
spp,giou,it,igt=0.85	92.01%	62.84%	0.98	0.37	0.97	0.55
spp,giou,it,igt=1.0	91.43%	61.82%	0.98	0.21	0.97	0.43
spp,giou,it,tt=0.7	nan	-	-	-	-	-
spp,giou,it,tt=0.85	78.01%	28.27%	0.97	0.06	0.95	0.21
spp,giou,gs,it	91.87%	63.53%	0.99	0.16	0.97	0.52
spp,mse,it,asff	92.49%	61.83%	0.97	0.57	0.95	0.70
spp,giou,it,asff
spp,giou,it,asff,rfb	92.32%	60.05%	0.98	0.43	0.96	0.62
csresnext50-panet,spp,giou,it	92.80%	64.16%	0.97	0.51	0.96	0.67

[ramdhan1989]

I would try
Dataset: Visdrone2019
1.cfg
yolov3-tiny_3l.cfg.txt
2. weights
https://drive.google.com/file/d/1fqWdpYpTJkKLnVhEiQQqm_SNvMp_qSE0/view?usp=sharing
3. names
visdrone2019.names.txt

Hope to see comments on these

I tried to my own drone video. the result is really good. Please advice how to get the similar performance ? for my purposes, I need to separate truck category into several truck type, person, car, bus and motorcycle. is there any dataset that could be used especially for aerial view image ?

[titanbender]

@laclouis5: What is the difference between

mAP@0.5 (scratch / pre-trained)* and mAP@[0.5...0.95] (scratch / pre-trained)*?

From how I read it the mAP is both calculated at 50%.

[laclouis5]

@titanbender,

No, mAP@[0.5...0.95] is the average AP over ten different IoU thresholds ranging from 0.5 to 0.95 by 0.05 increment.

[DayBreak-u]

Here you can post your trained models on different Datasets - 3 files:

1. cfg
2. weights
3. names
4. Optional: accuracy mAP@0.5 or/and mAP@0.5...0.95
5. Optional: BFLOPS and/or Inference time (milliseconds)

COCO test-dev

Model Size BFLOPS Inference time, ms AP@.5:.95 AP@.5 AP@.75 URL
yolo_v3_tiny_pan3 aa_ae_mixup scale_giou (no sgdr).txt 416x416 8.4 6.4 18.8% 36.8% 17.5% #3708 (comment)
yolov3-tiny-prn.cfg.txt 416x416 3.5 3.8 - 33.1% - URL
enet-coco.cfg.txt 416x416 3.7 22.7 - 45.5% - URL
ImageNet valid

Model BFLOPs Inference Time (ms) Top1, % URL
shufflenetv2 and weights 0.375 32 52% URL
efficient_b0 and weights 0.915 76 71% URL
MobileNet v2 and weights 0.85 - 67% URL

mobilenetv2 cfg file is error, stride 16 need repeat 7 times,but it‘s stride 8 repeat 7 times, this add the bflops

[colinlin1982]

Pascal VOC 2007+2012
yolov3-tiny-prn with mosaic and ciou, input_size 352x352, 2.45BFlops, map@0.5=64.26%
(backbone from yolov3-tiny-prn.weights for coco).
yolov3-tiny-prn.cfg.txt
and weights
yolov3-tiny-prn_352@64.26.weights.zip

[usamahjundia]

@laclouis5 hello there, is there a list / compilation of all those custom configs you used? i cannot seem to find them on cfg/ folder

[laclouis5]

@laclouis5 hello there, is there a list / compilation of all those custom configs you used? i cannot seem to find them on cfg/ folder

Hi @usamahjundia, all cfg files are attached in #3874 (comment), 2nd column.

[usamahjundia]

@laclouis5 hello there, is there a list / compilation of all those custom configs you used? i cannot seem to find them on cfg/ folder

Hi @usamahjundia, all cfg files are attached in #3874 (comment), 2nd column.

Thanks, but that is unfortunately not what i meant

What i meant was, where did you discover them in the first place? or are they custom-made?

Sorry for the confusion

[laclouis5]

@usamahjundia They are regular cfg files developed in this repo, I did not customise them. You can found them in the cfg folder of the repo.

[ShaneHsieh]

@laclouis5 hello
what is different with (scratch / pre-trained)?
and Tiny Yolo V3 Pan 3 is show 87.16% / 87.46%, but the Training Chart is show the best map is 89%,this is why?
thank.

[laclouis5]

@ShaneHsieh Error on my side, I updated the post with the good image.

Pre-trained is for models trained with initial weights from models trained on MS Coco. See the training tutorial of this repo for more information on how to do that. From scratch is without pre-trained weights.

[ShaneHsieh]

@laclouis5 Thank.

[dog-qiuqiu]

MobileNetV2-YOLOv3-Lite&Nano Darknet

Mobile inference frameworks benchmark (4*ARM_CPU)

Network	VOC mAP(0.5)	COCO mAP(0.5)	Resolution	Inference time (NCNN/Kirin 990)	Inference time (MNN arm82/Kirin 990)	FLOPS	Weight size
MobileNetV2-YOLOv3-Lite	72.61	36.57	320	33 ms	18 ms	1.8BFlops	8.0MB
MobileNetV2-YOLOv3-Nano	65.27	30.13	320	13 ms	5 ms	0.5BFlops	3.0MB
MobileNetV2-YOLOv3-Fastest	46.55	&	320	8.2 ms	2.4 ms	0.13BFlops	700kb

https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3