Detection of small objects

[malhosary]

@AlexeyAB Hi,

Thank you for the great effort and work.
We are trying to detect multiple small objects (red marked rectangles in the image) (this is a sample image just to show you the size of objects that we want to detect)

Data Set:

• 6 classes
• 850 images for training and 200 images for validation
• Images resolution is 2688 x 1520.
• Items to be detected are static with fixed locations/positions (items are not moving)
• Training images are almost the same with some changes in light conditions and object rotations.
• Detection images are almost the same as training images.

Questions:
1- Which cfg file you recommend to use for training?
2- What is the recommended width and height to use for training and detection?
3- How to calculate correct anchors for my data set?
using: darknet detector calc_anchors data/obj.data -num_of_clusters 15 -final_width 16 -final_height 16 -width 832 -height 832 -show
Or using: darknet detector calc_anchors data/obj.data -num_of_clusters 4 -width 832 -height 832 -show
4- What is the num_of_clusters , final_width and final_height and how to calculate it for my data set?

[AlexeyAB]

@malhosary Hi,

1/2. I would recommend you to train yolov3.cfg with width=608 height=608 or better width=832 height=832, if your GPU have enough GPU-RAM and it allows to achive to reach the speed you need.

• Also add max=200 to each [yolo]-layer.
• Use random=1 batch=64 subdivisions=64

3. Use this command to calculate anchors, and attach the generated cloud of pointes to your message
   darknet detector calc_anchors data/obj.data -num_of_clusters 9 -width 832 -height 832 -show
   Write what anchors you get.

-num_of_clusters 9 - number of anchors
-width 832 -height 832 - input network size
-final_width 16 -final_height 16 - deprecated and absent

Also read: https://github.com/AlexeyAB/darknet#how-to-improve-object-detection

recalculate anchors for your dataset for width and height from cfg-file: darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416 then set the same 9 anchors in each of 3 [yolo]-layers in your cfg-file

(also you can change indexes of anchors masks= for each [yolo]-layer, so that 1st-[yolo]-layer has anchors larger than 60x60, 2nd larger than 30x30, 3rd remaining)

---

Use the latest version of this repository.

And train with flag -map
darknet detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -map
Train at least 6000 iterations or more.

850 images for training and 200 images for validation

Also I recommend you to use 2x - 12x more training images if possible.

[malhosary]

@AlexeyAB Thanks for your quick reply.

And this is the output for the command:

num_of_clusters = 9, width = 832, height = 832
 read labels from 686 images
 loaded          image: 686      box: 5618
 all loaded.

 calculating k-means++ ...


 avg IoU = 88.12 %

Saving anchors to the file: anchors.txt
anchors =   0,  0,   0,  0,  22, 43,  19, 73,  22, 67,  26, 65,  32, 69, 419,241, 570,391

[AlexeyAB]

Try to download the latest version of Darknet from this repository, and calc_anchors again.
I fixed anchors calculation.

Also it looks like you have 100-200 large objects, that occupy ~50%x50% of image, do you want to detect such large objects?

[malhosary]

No, i want to detect only small products in the refrigerator

[AlexeyAB]

So you shouldn't use such big objects in your training dataset.

anchors = 0, 0, 0, 0, 22, 43, 19, 73, 22, 67, 26, 65, 32, 69, 419,241, 570,391

Also there shouldn't be 0 in the anchors. It was a bug.

[malhosary]

Hers are the new anchors using the new version:

 num_of_clusters = 9, width = 832, height = 832
 read labels from 686 images
 loaded          image: 686      box: 5618
 all loaded.

 calculating k-means++ ...

 iterations = 3


 avg IoU = 91.67 %

Saving anchors to the file: anchors.txt
anchors =  20, 41,  22, 40,  23, 49,  21, 55,  20, 76,  29, 60,  26, 67,  32, 73, 496,318

[dreambit]

@AlexeyAB , you've said
Also add max=200 to each [yolo]-layer.
but in doc
for training with a large number of objects in each image, add the parameter max=200 or higher value in the last [yolo]-layer

which one is correct?

[AlexeyAB]

@dreambit

You should to add max=200 in the last 3rd [yolo]-layer.
But in this case it will be better to add max=200 and for 1st and 2nd yolo-layers.

[AlexeyAB]

@malhosary

You should remove such big objects from your training dataset, that occupys more than 25% of image, and then reclalculate anchors and run training.

[dreambit]

@AlexeyAB,
i have to detect big objects and small objects(car)
example:

What is your recommendation for these objects? Is it enough to use default yolov3 config with custom generated anchors? or i have to use

for training for both small and large objects use modified models:

Full-model: 5 yolo layers: >https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3_5l.cfg
Tiny-model: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny_3l.cfg
Spatial-full-model: 3 yolo layers: >https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-spp.cfg

Thanks.

[AlexeyAB]

@dreambit It is better to use yolov3_5l.cfg in your case with default anchors, but it can be slow.

[malhosary]

@AlexeyAB Hi,

Thanks for your recommendations, i have here some good results after training for 10 000 iterations with mAP: 94% and avg loss: 0.5

Green boxes are items that can be detected by my model, the issue is in Red boxes where the same items are in different locations but can't be detected (the same items with the same size but different location/position than locations in the training data set), what is your recommendations?

[AlexeyAB]

@malhosary

Yolo v3 can detect such objects with ~99.99% accuracy.

Just use more training images, about 2000 images for each class.
And check that each object is labeled on your training images.

https://github.com/AlexeyAB/darknet#how-to-improve-object-detection

check that each object are mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark

[guangwei]

Hello, @AlexeyAB ,
I train very large picture (3644 X 2478) directly, and want to detect small objects (some are 4X4 pixels) from these large pictures. I tried to use yolov3-tiny.cfg and change width&height to 1920*1920, recalculate the anchors while I don't know if this anchor value is good, please check the attached picture, the inference result is not good, what's your recommendations to improve this ?

output of calculate anchors:
num_of_clusters = 9, width = 1920, height = 1920
read labels from 201 images
loaded image: 201 box: 1554
all loaded.

calculating k-means++ ...

avg IoU = 67.99 %

Saving anchors to the file: anchors.txt
anchors = 12, 17, 14, 41, 34, 19, 25, 55, 50, 35, 18,111, 36, 79, 116, 27, 90, 69

[AlexeyAB]

@guangwei

Can you show anchors and point cloud if you calculate anchors for -width 3616 -height 2464 (values multiple of 32)?

[guangwei]

these are the annotations
0 0.244405021834 0.704148471616 0.00900655021834 0.0101892285298
0 0.453602620087 0.314956331878 0.0158296943231 0.0200145560408
0 0.547216157205 0.342066957787 0.0163755458515 0.0218340611354
0 0.544350436681 0.583333333333 0.0161026200873 0.0152838427948

I think the output of anchors is strange:
./darknet detector calc_anchors VOCdevkit/my.data -num_of_clusters 9 -width 3616 -height 2464 -show
calc_anchors start

num_of_clusters = 9, width = 3616, height = 2464
read labels from 201 images
loaded image: 201 box: 1554
all loaded.

calculating k-means++ ...

avg IoU = 68.13 %

Saving anchors to the file: anchors.txt
anchors = 23, 22, 26, 51, 63, 24, 47, 70, 95, 44, 33,142, 67,101, 219, 35, 169, 88

[AlexeyAB]

@guangwei

So try to train yolov3-tiny.cfg with width = 3616 height = 2464 batch=64 subdivisions=64 in cfg-file.

and these anchors = 23, 22, 26, 51, 63, 24, 47, 70, 95, 44, 33,142, 67,101, 219, 35, 169, 88 in each of 3 yolo-layers.

[wting861006]

@AlexeyAB Hi,
about this image,can you give me some advice？This is a picture of sperm from an electron microscope.

[AlexeyAB]

@wting861006 Hi,

Show me output anchors and cloud.png
darknet detector calc_anchors data/obj.data -num_of_clusters 9 -width 832 -height 832 -show

[wting861006]

@wting861006 Hi,

Show me output anchors and cloud.png
darknet detector calc_anchors data/obj.data -num_of_clusters 9 -width 832 -height 832 -show

[AlexeyAB]

@wting861006
So you should train yolov3.cfg or yolov3-tiny_3l.cfg with width=1024 height=1024 in cfg-file

and recalculated anchors
darknet detector calc_anchors data/obj.data -num_of_clusters 9 -width 1024 -height 1024 -show

[wting861006]

@wting861006
So you should train yolov3.cfg or yolov3-tiny_3l.cfg with width=1024 height=1024 in cfg-file

and recalculated anchors
darknet detector calc_anchors data/obj.data -num_of_clusters 9 -width 1024 -height 1024 -show

I try yolov3-tiny_3l.cfg,yolov3-spp.cfg,yolov3.cfg,yolov3-tyny_5l.cfg, width=1280 height=1280.But the effect is not good.I upload my trained data in this place,could you please tell me what the problem might be?many thanks.
trainingdata.zip

I use command:darknet detector train yolov3-tiny_3l.data yolov3-tiny_3l.cfg yolov3-tiny_3l_last.weights -gpus 0,1 -map -clear
`Region 16 Avg IOU: 0.692852, Class: 0.999723, Obj: 0.653182, No Obj: 0.028169, .5R: 0.869565, .75R: 0.469565, count: 115
Region 16 Avg IOU: 0.694693, Class: 0.999744, Obj: 0.656049, No Obj: 0.028376, .5R: 0.890000, .75R: 0.450000, count: 100
Region 23 Avg IOU: 0.702654, Class: 0.999788, Obj: 0.421438, No Obj: 0.001485, .5R: 0.941176, .75R: 0.352941, count: 17
Region 23 Avg IOU: 0.771926, Class: 0.999771, Obj: 0.598884, No Obj: 0.008508, .5R: 0.954545, .75R: 0.645455, count: 110
Region 30 Avg IOU: 0.714068, Class: 0.999623, Obj: 0.553517, No Obj: 0.002131, .5R: 0.930435, .75R: 0.400000, count: 115
Region 16 Avg IOU: 0.673378, Class: 0.999779, Obj: 0.622265, No Obj: 0.014256, .5R: 0.865385, .75R: 0.423077, count: 52
Region 30 Avg IOU: 0.763364, Class: 0.999440, Obj: 0.613998, No Obj: 0.002058, .5R: 0.973451, .75R: 0.681416, count: 113
Region 23 Avg IOU: 0.762509, Class: 0.999860, Obj: 0.494922, No Obj: 0.004321, .5R: 0.981132, .75R: 0.603774, count: 53
Region 16 Avg IOU: 0.694208, Class: 0.999713, Obj: 0.685896, No Obj: 0.027605, .5R: 0.887640, .75R: 0.483146, count: 89
Region 23 Avg IOU: 0.769485, Class: 0.999680, Obj: 0.619020, No Obj: 0.009675, .5R: 0.954545, .75R: 0.689394, count: 132
Region 30 Avg IOU: 0.743331, Class: 0.999843, Obj: 0.627433, No Obj: 0.003065, .5R: 0.958824, .75R: 0.535294, count: 170
Region 30 Avg IOU: 0.768199, Class: 0.999487, Obj: 0.570825, No Obj: 0.001791, .5R: 0.954545, .75R: 0.727273, count: 110
Syncing... Done!

(next mAP calculation at 2452 iterations)
Last accuracy mAP@0.5 = 19.26 %
2456: 64.943108, 68.233200 avg loss, 0.000020 rate, 1.354000 seconds, 9824 images

calculation mAP (mean average precision)...
3
detections_count = 3154, unique_truth_count = 1204
class_id = 0, name = sperm, ap = 46.22% (TP = 573, FP = 391)
class_id = 1, name = round cell, ap = 0.00% (TP = 0, FP = 0)
class_id = 2, name = red cell, ap = 0.00% (TP = 0, FP = 0)

for thresh = 0.25, precision = 0.59, recall = 0.48, F1-score = 0.53
for thresh = 0.25, TP = 573, FP = 391, FN = 631, average IoU = 37.42 %

IoU threshold = 50 %, used Area-Under-Curve for each unique Recall
mean average precision (mAP@0.50) = 0.154082, or 15.41 %
Total Detection Time: 1.000000 Seconds

Set -points flag:
-points 101 for MS COCO
-points 11 for PascalVOC 2007 (uncomment difficult in voc.data)
-points 0 (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset

mean_average_precision (mAP@0.5) = 0.154082
Loaded: 0.000000 seconds`

[AlexeyAB]

@wting861006 Hi,

calculation mAP (mean average precision)...
3
detections_count = 3154, unique_truth_count = 1204
class_id = 0, name = sperm, ap = 46.22% (TP = 573, FP = 391)
class_id = 1, name = round cell, ap = 0.00% (TP = 0, FP = 0)
class_id = 2, name = red cell, ap = 0.00% (TP = 0, FP = 0)

for thresh = 0.25, precision = 0.59, recall = 0.48, F1-score = 0.53
for thresh = 0.25, TP = 573, FP = 391, FN = 631, average IoU = 37.42 %

IoU threshold = 50 %, used Area-Under-Curve for each unique Recall
mean average precision (mAP@0.50) = 0.154082, or 15.41 %

• Actually you get not bad result mAP = 46.22% for 2500 iterations, just train more (you see mAP = 15.41% = 46.22/3 just because you don't have any images for class_id 1 & 2)

• As I see you have only 3 validaton images, it is very low number.

• Put labels in the same folder where are images

• How many training images do you have?

• For production - to use in real cases - you should have 200 - 2000 training images for each class: https://github.com/AlexeyAB/darknet#how-to-improve-object-detection

• Don't set so low learning rate 0.00001

Your labels look like good, but check that you labeled all of (sperm, round cell, red cell) objects on the image without any exception:

---

Train at least 6000 iterations by using this cfg-file without any changes (just if out of memory occurs then set random=0 in the last yolo-layer): yolov3-tiny_3l.cfg.txt

Use: https://github.com/AlexeyAB/darknet#how-to-train-tiny-yolo-to-detect-your-custom-objects
darknet detector train yolov3-tiny_3l.data yolov3-tiny_3l.cfg yolov3-tiny.conv.15 -map

After 2000 iterations you can run multi-gpu training:
darknet detector train yolov3-tiny_3l.data yolov3-tiny_3l.cfg backup/yolov3-tiny_3l_2000.weights -gpus 0,1 -map

[wting861006]

@wting861006 Hi,

calculation mAP (mean average precision)...
3
detections_count = 3154, unique_truth_count = 1204
class_id = 0, name = sperm, ap = 46.22% (TP = 573, FP = 391)
class_id = 1, name = round cell, ap = 0.00% (TP = 0, FP = 0)
class_id = 2, name = red cell, ap = 0.00% (TP = 0, FP = 0)
for thresh = 0.25, precision = 0.59, recall = 0.48, F1-score = 0.53
for thresh = 0.25, TP = 573, FP = 391, FN = 631, average IoU = 37.42 %
IoU threshold = 50 %, used Area-Under-Curve for each unique Recall
mean average precision (mAP@0.50) = 0.154082, or 15.41 %

• Actually you get not bad result mAP = 46.22% for 2500 iterations, just train more (you see mAP = 15.41% = 46.22/3 just because you don't have any images for class_id 1 & 2)
• As I see you have only 3 validaton images, it is very low number.
• Put labels in the same folder where are images
• How many training images do you have?
• For production - to use in real cases - you should have 200 - 2000 training images for each class: https://github.com/AlexeyAB/darknet#how-to-improve-object-detection
• Don't set so low learning rate 0.00001

Your labels look like good, but check that you labeled all of (sperm, round cell, red cell) objects on the image without any exception:

Train at least 6000 iterations by using this cfg-file without any changes (just if out of memory occurs then set random=0 in the last yolo-layer): yolov3-tiny_3l.cfg.txt

Use: https://github.com/AlexeyAB/darknet#how-to-train-tiny-yolo-to-detect-your-custom-objects
darknet detector train yolov3-tiny_3l.data yolov3-tiny_3l.cfg yolov3-tiny.conv.15 -map

After 2000 iterations you can run multi-gpu training:
darknet detector train yolov3-tiny_3l.data yolov3-tiny_3l.cfg backup/yolov3-tiny_3l_2000.weights -gpus 0,1 -map

@AlexeyAB Hi,I trained according to your suggestion. Except for the high loss, it seemed to be fine.

(next mAP calculation at 8040 iterations)
Last accuracy mAP@0.5 = 23.18 %
7999: 28.282316, 28.417795 avg loss, 0.000200 rate, 32.724000 seconds, 1023872 images
Loaded: 0.001000 seconds
Region 16 Avg IOU: 0.728459, Class: 0.999942, Obj: 0.871590, No Obj: 0.027605, .5R: 0.916667, .75R: 0.500000, count: 24
Region 16 Avg IOU: 0.789409, Class: 0.999979, Obj: 0.855757, No Obj: 0.037393, .5R: 0.970588, .75R: 0.705882, count: 34
Region 23 Avg IOU: 0.728356, Class: 0.999971, Obj: 0.852199, No Obj: 0.010514, .5R: 0.925373, .75R: 0.582090, count: 134
Region 23 Avg IOU: 0.831161, Class: 0.999964, Obj: 0.903209, No Obj: 0.014844, .5R: 0.985714, .75R: 0.835714, count: 140
Region 30 Avg IOU: 0.727796, Class: 0.999994, Obj: 0.706936, No Obj: 0.001159, .5R: 0.905660, .75R: 0.566038, count: 53
Region 16 Avg IOU: 0.791163, Class: 0.999981, Obj: 0.865418, No Obj: 0.044673, .5R: 0.911111, .75R: 0.755556, count: 45
Region 30 Avg IOU: 0.821844, Class: 0.999983, Obj: 0.858697, No Obj: 0.004129, .5R: 0.959016, .75R: 0.827869, count: 122
Region 23 Avg IOU: 0.815609, Class: 0.999944, Obj: 0.864114, No Obj: 0.016482, .5R: 0.978571, .75R: 0.828571, count: 140
Region 16 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.000105, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 23 Avg IOU: 0.757245, Class: 0.999986, Obj: 0.819335, No Obj: 0.002877, .5R: 0.928571, .75R: 0.678571, count: 28
Region 30 Avg IOU: 0.836744, Class: 0.999990, Obj: 0.870285, No Obj: 0.004231, .5R: 0.982759, .75R: 0.870690, count: 116
Region 16 Avg IOU: 0.758645, Class: 0.999965, Obj: 0.888311, No Obj: 0.053629, .5R: 0.921569, .75R: 0.686275, count: 51
Region 23 Avg IOU: 0.822879, Class: 0.999961, Obj: 0.917018, No Obj: 0.015650, .5R: 0.992857, .75R: 0.842857, count: 140
Region 30 Avg IOU: 0.746448, Class: 0.999994, Obj: 0.850215, No Obj: 0.007791, .5R: 0.920000, .75R: 0.608571, count: 350
Region 16 Avg IOU: 0.817286, Class: 0.999964, Obj: 0.893398, No Obj: 0.020972, .5R: 1.000000, .75R: 0.777778, count: 18
Region 23 Avg IOU: 0.804021, Class: 0.999966, Obj: 0.889353, No Obj: 0.017437, .5R: 0.961538, .75R: 0.774725, count: 182
Region 30 Avg IOU: 0.756729, Class: 0.999978, Obj: 0.756026, No Obj: 0.002091, .5R: 0.857143, .75R: 0.698413, count: 63
Region 16 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.000008, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 23 Avg IOU: 0.790081, Class: 0.999975, Obj: 0.821583, No Obj: 0.005539, .5R: 0.963636, .75R: 0.745455, count: 55
Region 30 Avg IOU: 0.823043, Class: 0.999965, Obj: 0.858511, No Obj: 0.004140, .5R: 0.961165, .75R: 0.844660, count: 103
Region 16 Avg IOU: 0.708436, Class: 0.999981, Obj: 0.865744, No Obj: 0.054155, .5R: 0.843137, .75R: 0.568627, count: 51
Region 23 Avg IOU: 0.817897, Class: 0.999963, Obj: 0.882496, No Obj: 0.014603, .5R: 0.971429, .75R: 0.821429, count: 140
Region 30 Avg IOU: 0.779259, Class: 0.999994, Obj: 0.855687, No Obj: 0.007194, .5R: 0.950658, .75R: 0.703947, count: 304
Region 16 Avg IOU: 0.800896, Class: 0.999968, Obj: 0.882048, No Obj: 0.025618, .5R: 1.000000, .75R: 0.772727, count: 22
Region 30 Avg IOU: 0.825269, Class: 0.999958, Obj: 0.858482, No Obj: 0.002874, .5R: 0.926471, .75R: 0.852941, count: 68
Region 23 Avg IOU: 0.817418, Class: 0.999966, Obj: 0.867868, No Obj: 0.015459, .5R: 0.987097, .75R: 0.819355, count: 155
Region 16 Avg IOU: 0.736228, Class: 0.999954, Obj: 0.822897, No Obj: 0.067031, .5R: 0.954545, .75R: 0.500000, count: 66
Region 23 Avg IOU: 0.715319, Class: 0.999950, Obj: 0.736167, No Obj: 0.008709, .5R: 0.921569, .75R: 0.509804, count: 102
Region 30 Avg IOU: 0.673622, Class: 0.999986, Obj: 0.586671, No Obj: 0.000254, .5R: 0.818182, .75R: 0.545455, count: 11
Region 16 Avg IOU: 0.777791, Class: 0.999965, Obj: 0.925473, No Obj: 0.036930, .5R: 0.965517, .75R: 0.724138, count: 29
Region 30 Avg IOU: 0.843717, Class: 0.999981, Obj: 0.910572, No Obj: 0.005666, .5R: 0.988506, .75R: 0.896552, count: 174
Region 23 Avg IOU: 0.757773, Class: 0.999961, Obj: 0.871485, No Obj: 0.012311, .5R: 0.948905, .75R: 0.649635, count: 137
Region 16 Avg IOU: 0.789821, Class: 0.999988, Obj: 0.832196, No Obj: 0.021670, .5R: 1.000000, .75R: 0.700000, count: 20
Region 23 Avg IOU: 0.818793, Class: 0.999945, Obj: 0.851732, No Obj: 0.016416, .5R: 0.974684, .75R: 0.803797, count: 158
Region 30 Avg IOU: 0.675039, Class: 0.999972, Obj: 0.710975, No Obj: 0.000938, .5R: 0.678571, .75R: 0.535714, count: 28
Region 16 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.001054, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 23 Avg IOU: 0.758965, Class: 0.999989, Obj: 0.869013, No Obj: 0.009182, .5R: 0.953271, .75R: 0.579439, count: 107
Region 30 Avg IOU: 0.841728, Class: 0.999973, Obj: 0.907757, No Obj: 0.008206, .5R: 0.983806, .75R: 0.890688, count: 247
Region 16 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.000098, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 30 Avg IOU: 0.776824, Class: 0.999994, Obj: 0.801551, No Obj: 0.005489, .5R: 0.934579, .75R: 0.719626, count: 214
Region 16 Avg IOU: 0.784695, Class: 0.999976, Obj: 0.921864, No Obj: 0.054677, .5R: 0.979592, .75R: 0.693878, count: 49
Region 23 Avg IOU: 0.837545, Class: 0.999980, Obj: 0.940718, No Obj: 0.005687, .5R: 1.000000, .75R: 0.916667, count: 48
Region 23 Avg IOU: 0.784718, Class: 0.999907, Obj: 0.797513, No Obj: 0.015029, .5R: 0.940476, .75R: 0.750000, count: 168
Region 30 Avg IOU: 0.819371, Class: 0.999931, Obj: 0.830734, No Obj: 0.002151, .5R: 0.944444, .75R: 0.814815, count: 54
Region 16 Avg IOU: 0.788925, Class: 0.999984, Obj: 0.945039, No Obj: 0.018906, .5R: 0.933333, .75R: 0.666667, count: 15
Region 30 Avg IOU: 0.822104, Class: 0.999988, Obj: 0.909026, No Obj: 0.010283, .5R: 0.967302, .75R: 0.839237, count: 367
Region 23 Avg IOU: 0.827880, Class: 0.999988, Obj: 0.912221, No Obj: 0.013392, .5R: 0.990909, .75R: 0.845455, count: 110
Region 16 Avg IOU: 0.796615, Class: 0.999987, Obj: 0.961586, No Obj: 0.017919, .5R: 1.000000, .75R: 0.818182, count: 11
Region 23 Avg IOU: 0.796908, Class: 0.999990, Obj: 0.926985, No Obj: 0.012506, .5R: 0.929688, .75R: 0.796875, count: 128
Region 30 Avg IOU: 0.840303, Class: 0.999994, Obj: 0.927013, No Obj: 0.006714, .5R: 0.995169, .75R: 0.859903, count: 207
Region 30 Avg IOU: 0.824875, Class: 0.999990, Obj: 0.898489, No Obj: 0.004171, .5R: 0.985075, .75R: 0.865672, count: 134
Region 16 Avg IOU: 0.690382, Class: 0.999947, Obj: 0.848882, No Obj: 0.092834, .5R: 0.855556, .75R: 0.422222, count: 90
Region 16 Avg IOU: 0.826808, Class: 0.999971, Obj: 0.945638, No Obj: 0.009363, .5R: 1.000000, .75R: 0.875000, count: 8
Region 23 Avg IOU: 0.749426, Class: 0.999720, Obj: 0.699263, No Obj: 0.006849, .5R: 0.955556, .75R: 0.644444, count: 90
Region 23 Avg IOU: 0.844091, Class: 0.999984, Obj: 0.941422, No Obj: 0.012538, .5R: 1.000000, .75R: 0.858586, count: 99
Region 30 Avg IOU: 0.632242, Class: 0.999931, Obj: 0.518282, No Obj: 0.000337, .5R: 0.777778, .75R: 0.222222, count: 9
Region 16 Avg IOU: 0.754437, Class: 0.999987, Obj: 0.881478, No Obj: 0.025438, .5R: 0.920000, .75R: 0.720000, count: 25
Region 23 Avg IOU: 0.838224, Class: 0.999958, Obj: 0.910537, No Obj: 0.018389, .5R: 0.970930, .75R: 0.848837, count: 172
Region 30 Avg IOU: 0.832407, Class: 0.999986, Obj: 0.928840, No Obj: 0.008180, .5R: 0.985240, .75R: 0.841328, count: 271
Region 16 Avg IOU: 0.800871, Class: 0.999975, Obj: 0.927815, No Obj: 0.010643, .5R: 1.000000, .75R: 0.800000, count: 10
Region 23 Avg IOU: 0.787905, Class: 0.999957, Obj: 0.903203, No Obj: 0.012896, .5R: 0.924528, .75R: 0.773585, count: 106
Region 30 Avg IOU: 0.831920, Class: 0.999983, Obj: 0.908863, No Obj: 0.005794, .5R: 0.961290, .75R: 0.864516, count: 155
Region 16 Avg IOU: 0.720263, Class: 0.999958, Obj: 0.844352, No Obj: 0.049831, .5R: 0.886364, .75R: 0.545455, count: 44
Region 23 Avg IOU: 0.741264, Class: 0.999968, Obj: 0.757226, No Obj: 0.009297, .5R: 0.918033, .75R: 0.590164, count: 122
Region 30 Avg IOU: 0.833528, Class: 0.999974, Obj: 0.901680, No Obj: 0.006262, .5R: 0.963351, .75R: 0.853403, count: 191
Region 16 Avg IOU: 0.722023, Class: 0.999967, Obj: 0.846855, No Obj: 0.052589, .5R: 0.921569, .75R: 0.529412, count: 51
Region 30 Avg IOU: 0.731805, Class: 0.999984, Obj: 0.676473, No Obj: 0.000944, .5R: 0.923077, .75R: 0.589744, count: 39
Region 23 Avg IOU: 0.747586, Class: 0.999965, Obj: 0.808995, No Obj: 0.016198, .5R: 0.934426, .75R: 0.606557, count: 183
Region 16 Avg IOU: 0.779799, Class: 0.999982, Obj: 0.854611, No Obj: 0.016208, .5R: 1.000000, .75R: 0.529412, count: 17
Region 23 Avg IOU: 0.785591, Class: 0.999984, Obj: 0.886845, No Obj: 0.015702, .5R: 0.968354, .75R: 0.734177, count: 158
Region 30 Avg IOU: 0.749853, Class: 0.999991, Obj: 0.741158, No Obj: 0.000832, .5R: 0.913043, .75R: 0.608696, count: 23
Region 16 Avg IOU: 0.764985, Class: 0.999960, Obj: 0.833427, No Obj: 0.038541, .5R: 0.906250, .75R: 0.718750, count: 32
Region 23 Avg IOU: 0.743274, Class: 0.999985, Obj: 0.821249, No Obj: 0.009999, .5R: 0.910569, .75R: 0.609756, count: 123
Region 30 Avg IOU: 0.804083, Class: 0.999985, Obj: 0.893455, No Obj: 0.003931, .5R: 0.966387, .75R: 0.764706, count: 119
Region 16 Avg IOU: 0.763691, Class: 0.999979, Obj: 0.893420, No Obj: 0.021691, .5R: 1.000000, .75R: 0.600000, count: 20
Region 23 Avg IOU: 0.834073, Class: 0.999990, Obj: 0.929929, No Obj: 0.012755, .5R: 0.991525, .75R: 0.872881, count: 118
Region 30 Avg IOU: 0.755934, Class: 0.999963, Obj: 0.655496, No Obj: 0.000669, .5R: 0.888889, .75R: 0.703704, count: 27
Region 16 Avg IOU: 0.878477, Class: 0.999975, Obj: 0.957937, No Obj: 0.011833, .5R: 1.000000, .75R: 1.000000, count: 6
Region 23 Avg IOU: 0.831948, Class: 0.999934, Obj: 0.921172, No Obj: 0.011971, .5R: 0.980769, .75R: 0.865385, count: 104
Region 30 Avg IOU: 0.839456, Class: 0.999987, Obj: 0.895580, No Obj: 0.003585, .5R: 0.967742, .75R: 0.881720, count: 93
Region 16 Avg IOU: 0.798397, Class: 0.999991, Obj: 0.967870, No Obj: 0.009056, .5R: 1.000000, .75R: 0.600000, count: 5
Region 23 Avg IOU: 0.819347, Class: 0.999958, Obj: 0.889570, No Obj: 0.013659, .5R: 0.984733, .75R: 0.854962, count: 131
Region 30 Avg IOU: 0.835097, Class: 0.999989, Obj: 0.922987, No Obj: 0.008175, .5R: 0.992509, .75R: 0.868914, count: 267
Region 16 Avg IOU: 0.761256, Class: 0.999963, Obj: 0.870063, No Obj: 0.035201, .5R: 1.000000, .75R: 0.625000, count: 32
Region 23 Avg IOU: 0.757937, Class: 0.999963, Obj: 0.838941, No Obj: 0.012911, .5R: 0.904459, .75R: 0.656051, count: 157
Region 30 Avg IOU: 0.831408, Class: 0.999986, Obj: 0.912502, No Obj: 0.007425, .5R: 0.965517, .75R: 0.862069, count: 232
Region 16 Avg IOU: 0.872516, Class: 0.999996, Obj: 0.968698, No Obj: 0.001569, .5R: 1.000000, .75R: 1.000000, count: 1
Region 23 Avg IOU: 0.831201, Class: 0.999980, Obj: 0.847159, No Obj: 0.005075, .5R: 1.000000, .75R: 0.823529, count: 34
Region 30 Avg IOU: 0.772045, Class: 0.999981, Obj: 0.742372, No Obj: 0.000761, .5R: 0.928571, .75R: 0.607143, count: 28
Region 16 Avg IOU: 0.748502, Class: 0.999989, Obj: 0.782190, No Obj: 0.004269, .5R: 1.000000, .75R: 0.666667, count: 3
Region 23 Avg IOU: 0.757831, Class: 0.999986, Obj: 0.854116, No Obj: 0.012950, .5R: 0.912162, .75R: 0.662162, count: 148
Region 30 Avg IOU: 0.793263, Class: 0.999996, Obj: 0.892331, No Obj: 0.007779, .5R: 0.964630, .75R: 0.758842, count: 311
Region 16 Avg IOU: 0.768529, Class: 0.999971, Obj: 0.919163, No Obj: 0.069179, .5R: 0.933333, .75R: 0.716667, count: 60
Region 23 Avg IOU: 0.794949, Class: 0.999985, Obj: 0.867421, No Obj: 0.013788, .5R: 0.967320, .75R: 0.745098, count: 153
Region 30 Avg IOU: 0.808955, Class: 0.999987, Obj: 0.879069, No Obj: 0.005687, .5R: 0.957447, .75R: 0.803191, count: 188
Region 16 Avg IOU: 0.857313, Class: 0.999990, Obj: 0.393047, No Obj: 0.000647, .5R: 1.000000, .75R: 1.000000, count: 1
Region 23 Avg IOU: 0.817555, Class: 0.999979, Obj: 0.870660, No Obj: 0.003504, .5R: 1.000000, .75R: 0.782609, count: 23
Region 30 Avg IOU: 0.785536, Class: 0.999971, Obj: 0.706055, No Obj: 0.001377, .5R: 0.976190, .75R: 0.690476, count: 42
Region 16 Avg IOU: 0.813957, Class: 0.999990, Obj: 0.921338, No Obj: 0.017380, .5R: 1.000000, .75R: 0.785714, count: 14
Region 23 Avg IOU: 0.802159, Class: 0.999948, Obj: 0.865003, No Obj: 0.015187, .5R: 0.968944, .75R: 0.782609, count: 161
Region 30 Avg IOU: 0.789212, Class: 0.999994, Obj: 0.910847, No Obj: 0.009042, .5R: 0.953039, .75R: 0.718232, count: 362
Region 16 Avg IOU: 0.765696, Class: 0.999980, Obj: 0.961934, No Obj: 0.006056, .5R: 1.000000, .75R: 0.600000, count: 5
Region 23 Avg IOU: 0.809739, Class: 0.999994, Obj: 0.896761, No Obj: 0.006397, .5R: 0.984375, .75R: 0.796875, count: 64
Region 30 Avg IOU: 0.828939, Class: 0.999979, Obj: 0.863415, No Obj: 0.006236, .5R: 0.981221, .75R: 0.845070, count: 213
Region 16 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.001079, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 23 Avg IOU: 0.778810, Class: 0.999992, Obj: 0.775562, No Obj: 0.002977, .5R: 1.000000, .75R: 0.695652, count: 23
Region 30 Avg IOU: 0.804343, Class: 0.999993, Obj: 0.884081, No Obj: 0.006174, .5R: 0.974359, .75R: 0.786325, count: 234
Region 16 Avg IOU: 0.796003, Class: 0.999980, Obj: 0.883842, No Obj: 0.025013, .5R: 0.958333, .75R: 0.708333, count: 24
Region 23 Avg IOU: 0.822314, Class: 0.999964, Obj: 0.904165, No Obj: 0.013927, .5R: 0.968000, .75R: 0.808000, count: 125
Region 30 Avg IOU: 0.776246, Class: 0.999993, Obj: 0.876647, No Obj: 0.008473, .5R: 0.964187, .75R: 0.705234, count: 363
Region 16 Avg IOU: 0.853101, Class: 0.999896, Obj: 0.840699, No Obj: 0.003954, .5R: 1.000000, .75R: 1.000000, count: 2
Region 23 Avg IOU: 0.778022, Class: 0.999961, Obj: 0.860394, No Obj: 0.012448, .5R: 0.962687, .75R: 0.716418, count: 134
Region 30 Avg IOU: 0.838394, Class: 0.999974, Obj: 0.895975, No Obj: 0.006170, .5R: 0.985294, .75R: 0.901961, count: 204
Region 16 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.000184, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 23 Avg IOU: 0.833173, Class: 0.999980, Obj: 0.913155, No Obj: 0.006219, .5R: 1.000000, .75R: 0.844828, count: 58
Region 30 Avg IOU: 0.815699, Class: 0.999993, Obj: 0.890091, No Obj: 0.006385, .5R: 0.967593, .75R: 0.810185, count: 216
Region 16 Avg IOU: 0.764944, Class: 0.999989, Obj: 0.921200, No Obj: 0.038511, .5R: 0.935484, .75R: 0.709677, count: 31
Region 23 Avg IOU: 0.792637, Class: 0.999948, Obj: 0.857670, No Obj: 0.014153, .5R: 0.969697, .75R: 0.757576, count: 132
Region 30 Avg IOU: 0.819246, Class: 0.999993, Obj: 0.920825, No Obj: 0.009363, .5R: 0.956923, .75R: 0.833846, count: 325
Region 16 Avg IOU: 0.803521, Class: 0.999977, Obj: 0.904919, No Obj: 0.053224, .5R: 1.000000, .75R: 0.745098, count: 51
Region 23 Avg IOU: 0.810103, Class: 0.999979, Obj: 0.890915, No Obj: 0.012620, .5R: 0.964286, .75R: 0.830357, count: 112
Region 30 Avg IOU: 0.830770, Class: 0.999968, Obj: 0.868687, No Obj: 0.003308, .5R: 0.979167, .75R: 0.885417, count: 96
Region 16 Avg IOU: 0.740964, Class: 0.999967, Obj: 0.854958, No Obj: 0.057035, .5R: 0.925926, .75R: 0.518519, count: 54
Region 23 Avg IOU: 0.755742, Class: 0.999931, Obj: 0.834745, No Obj: 0.010322, .5R: 0.938053, .75R: 0.637168, count: 113
Region 30 Avg IOU: 0.827658, Class: 0.999938, Obj: 0.875942, No Obj: 0.002322, .5R: 0.952381, .75R: 0.920635, count: 63
Region 16 Avg IOU: 0.757504, Class: 0.999974, Obj: 0.869383, No Obj: 0.055546, .5R: 0.924528, .75R: 0.603774, count: 53
Region 23 Avg IOU: 0.790637, Class: 0.999908, Obj: 0.842142, No Obj: 0.013648, .5R: 0.972028, .75R: 0.734266, count: 143
Region 30 Avg IOU: 0.742443, Class: 0.999960, Obj: 0.675435, No Obj: 0.001483, .5R: 0.919355, .75R: 0.661290, count: 62
Region 16 Avg IOU: 0.775539, Class: 0.999967, Obj: 0.871145, No Obj: 0.040769, .5R: 0.945946, .75R: 0.675676, count: 37
Region 23 Avg IOU: 0.779890, Class: 0.999971, Obj: 0.865131, No Obj: 0.015537, .5R: 0.923077, .75R: 0.737179, count: 156
Region 30 Avg IOU: 0.837252, Class: 0.999950, Obj: 0.820496, No Obj: 0.002040, .5R: 0.972973, .75R: 0.905405, count: 74
Region 16 Avg IOU: 0.771877, Class: 0.999973, Obj: 0.890585, No Obj: 0.021097, .5R: 0.944444, .75R: 0.611111, count: 18
Region 23 Avg IOU: 0.692963, Class: 0.999978, Obj: 0.786946, No Obj: 0.012610, .5R: 0.847561, .75R: 0.487805, count: 164
Region 30 Avg IOU: 0.793773, Class: 0.999967, Obj: 0.803041, No Obj: 0.002347, .5R: 0.958333, .75R: 0.708333, count: 72
Region 16 Avg IOU: 0.730396, Class: 0.999964, Obj: 0.947181, No Obj: 0.014562, .5R: 1.000000, .75R: 0.600000, count: 10
Region 23 Avg IOU: 0.729929, Class: 0.999987, Obj: 0.821545, No Obj: 0.015871, .5R: 0.882927, .75R: 0.570732, count: 205
Region 30 Avg IOU: 0.756141, Class: 0.999974, Obj: 0.615413, No Obj: 0.000946, .5R: 0.947368, .75R: 0.631579, count: 38
Region 16 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.000578, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 23 Avg IOU: 0.807425, Class: 0.999967, Obj: 0.681013, No Obj: 0.002491, .5R: 1.000000, .75R: 0.714286, count: 21
Region 30 Avg IOU: 0.759417, Class: 0.999969, Obj: 0.781542, No Obj: 0.001754, .5R: 0.918033, .75R: 0.672131, count: 61
Region 16 Avg IOU: 0.692464, Class: 0.999970, Obj: 0.742669, No Obj: 0.010214, .5R: 0.800000, .75R: 0.500000, count: 10
Region 23 Avg IOU: 0.786766, Class: 0.999970, Obj: 0.822876, No Obj: 0.014264, .5R: 0.958904, .75R: 0.732877, count: 146
Region 30 Avg IOU: 0.763948, Class: 0.999993, Obj: 0.827462, No Obj: 0.008469, .5R: 0.944598, .75R: 0.656510, count: 361
Region 16 Avg IOU: 0.799430, Class: 0.999984, Obj: 0.807299, No Obj: 0.009658, .5R: 1.000000, .75R: 0.800000, count: 10
Region 30 Avg IOU: 0.810641, Class: 0.999980, Obj: 0.873936, No Obj: 0.006066, .5R: 0.960784, .75R: 0.799020, count: 204
Region 23 Avg IOU: 0.792055, Class: 0.999969, Obj: 0.888835, No Obj: 0.012960, .5R: 0.968750, .75R: 0.726563, count: 128
Region 16 Avg IOU: 0.836822, Class: 0.999986, Obj: 0.880808, No Obj: 0.025861, .5R: 0.954545, .75R: 0.909091, count: 22
Region 23 Avg IOU: 0.821071, Class: 0.999991, Obj: 0.912370, No Obj: 0.014157, .5R: 0.975806, .75R: 0.798387, count: 124
Region 30 Avg IOU: 0.822604, Class: 0.999988, Obj: 0.861707, No Obj: 0.007797, .5R: 0.967857, .75R: 0.835714, count: 280
Region 30 Avg IOU: 0.828177, Class: 0.999988, Obj: 0.899844, No Obj: 0.005043, .5R: 0.947712, .75R: 0.895425, count: 153
Region 16 Avg IOU: 0.845334, Class: 0.999987, Obj: 0.961435, No Obj: 0.025912, .5R: 1.000000, .75R: 0.850000, count: 20
Region 16 Avg IOU: 0.748366, Class: 0.999984, Obj: 0.899828, No Obj: 0.041463, .5R: 0.916667, .75R: 0.611111, count: 36
Region 23 Avg IOU: 0.821120, Class: 0.999963, Obj: 0.892257, No Obj: 0.015993, .5R: 0.978417, .75R: 0.856115, count: 139
Region 23 Avg IOU: 0.842402, Class: 0.999962, Obj: 0.892871, No Obj: 0.015367, .5R: 0.985294, .75R: 0.904412, count: 136
Region 30 Avg IOU: 0.846450, Class: 0.999969, Obj: 0.886495, No Obj: 0.004609, .5R: 0.992188, .75R: 0.906250, count: 128
Region 16 Avg IOU: 0.672694, Class: 0.999962, Obj: 0.943238, No Obj: 0.006091, .5R: 1.000000, .75R: 0.250000, count: 4
Region 30 Avg IOU: 0.847050, Class: 0.999987, Obj: 0.899536, No Obj: 0.006868, .5R: 0.973404, .75R: 0.898936, count: 188
Region 16 Avg IOU: 0.688844, Class: 0.999965, Obj: 0.664333, No Obj: 0.013326, .5R: 0.928571, .75R: 0.428571, count: 14
Region 23 Avg IOU: 0.718276, Class: 0.999991, Obj: 0.817508, No Obj: 0.014432, .5R: 0.883249, .75R: 0.563452, count: 197
Region 23 Avg IOU: 0.725209, Class: 0.999950, Obj: 0.780371, No Obj: 0.010743, .5R: 0.908497, .75R: 0.542484, count: 153
Region 30 Avg IOU: 0.785260, Class: 0.999994, Obj: 0.808276, No Obj: 0.002375, .5R: 0.941176, .75R: 0.682353, count: 85
Region 16 Avg IOU: 0.777749, Class: 0.999969, Obj: 0.910252, No Obj: 0.066879, .5R: 0.950820, .75R: 0.672131, count: 61
Region 30 Avg IOU: 0.738516, Class: 0.999985, Obj: 0.681831, No Obj: 0.002636, .5R: 0.918033, .75R: 0.598361, count: 122
Region 23 Avg IOU: 0.797863, Class: 0.999964, Obj: 0.859742, No Obj: 0.012267, .5R: 0.945736, .75R: 0.790698, count: 129
Region 16 Avg IOU: 0.928579, Class: 0.999975, Obj: 0.997926, No Obj: 0.002298, .5R: 1.000000, .75R: 1.000000, count: 1
Region 23 Avg IOU: 0.803332, Class: 0.999971, Obj: 0.848549, No Obj: 0.006755, .5R: 0.967213, .75R: 0.803279, count: 61
Region 30 Avg IOU: 0.792241, Class: 0.999955, Obj: 0.760447, No Obj: 0.001448, .5R: 0.951219, .75R: 0.780488, count: 41
Region 16 Avg IOU: 0.741723, Class: 0.999963, Obj: 0.822805, No Obj: 0.069265, .5R: 0.897059, .75R: 0.588235, count: 68
Region 23 Avg IOU: 0.788396, Class: 0.999942, Obj: 0.816687, No Obj: 0.012999, .5R: 0.977273, .75R: 0.742424, count: 132
Region 30 Avg IOU: 0.806839, Class: 0.999992, Obj: 0.921134, No Obj: 0.007306, .5R: 0.960714, .75R: 0.785714, count: 280
Region 16 Avg IOU: 0.772473, Class: 0.999983, Obj: 0.865483, No Obj: 0.035309, .5R: 0.937500, .75R: 0.687500, count: 32
Region 23 Avg IOU: 0.760208, Class: 0.999947, Obj: 0.798656, No Obj: 0.010803, .5R: 0.931624, .75R: 0.623932, count: 117
Region 30 Avg IOU: 0.791559, Class: 0.999984, Obj: 0.808366, No Obj: 0.001942, .5R: 0.943396, .75R: 0.792453, count: 53
Region 16 Avg IOU: 0.824709, Class: 0.999984, Obj: 0.900123, No Obj: 0.031624, .5R: 1.000000, .75R: 0.916667, count: 24
Region 23 Avg IOU: 0.829783, Class: 0.999974, Obj: 0.928299, No Obj: 0.014739, .5R: 0.964286, .75R: 0.892857, count: 140
Region 30 Avg IOU: 0.813559, Class: 0.999965, Obj: 0.757607, No Obj: 0.001566, .5R: 0.976744, .75R: 0.813953, count: 43
Region 16 Avg IOU: 0.782935, Class: 0.999967, Obj: 0.827323, No Obj: 0.054261, .5R: 0.960784, .75R: 0.666667, count: 51
Region 23 Avg IOU: 0.783351, Class: 0.999974, Obj: 0.851599, No Obj: 0.012230, .5R: 0.929688, .75R: 0.750000, count: 128
Region 30 Avg IOU: 0.832326, Class: 0.999977, Obj: 0.877686, No Obj: 0.005439, .5R: 0.982036, .75R: 0.850299, count: 167
Region 30 Avg IOU: 0.818918, Class: 0.999992, Obj: 0.849400, No Obj: 0.001754, .5R: 0.981132, .75R: 0.792453, count: 53
Region 16 Avg IOU: 0.738812, Class: 0.999975, Obj: 0.843109, No Obj: 0.076680, .5R: 0.888889, .75R: 0.597222, count: 72
Region 16 Avg IOU: 0.739819, Class: 0.999935, Obj: 0.861053, No Obj: 0.073286, .5R: 0.942857, .75R: 0.600000, count: 70
Region 23 Avg IOU: 0.783096, Class: 0.999920, Obj: 0.808885, No Obj: 0.017017, .5R: 0.944444, .75R: 0.709877, count: 162
Region 23 Avg IOU: 0.700198, Class: 0.999966, Obj: 0.753024, No Obj: 0.008921, .5R: 0.831858, .75R: 0.495575, count: 113
Region 30 Avg IOU: 0.611063, Class: 0.999870, Obj: 0.344368, No Obj: 0.000286, .5R: 0.666667, .75R: 0.333333, count: 18
Region 16 Avg IOU: 0.747940, Class: 0.999982, Obj: 0.877716, No Obj: 0.017431, .5R: 0.812500, .75R: 0.625000, count: 16
Region 30 Avg IOU: 0.801591, Class: 0.999963, Obj: 0.794851, No Obj: 0.002891, .5R: 0.945652, .75R: 0.804348, count: 92
Region 23 Avg IOU: 0.817040, Class: 0.999910, Obj: 0.882889, No Obj: 0.014809, .5R: 0.969697, .75R: 0.810606, count: 132
Region 16 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.000328, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 23 Avg IOU: 0.811264, Class: 0.999982, Obj: 0.861043, No Obj: 0.003635, .5R: 1.000000, .75R: 0.807692, count: 26
Region 30 Avg IOU: 0.834934, Class: 0.999977, Obj: 0.898675, No Obj: 0.006247, .5R: 0.972527, .75R: 0.890110, count: 182
Region 16 Avg IOU: 0.707852, Class: 0.999944, Obj: 0.858346, No Obj: 0.098443, .5R: 0.903226, .75R: 0.526882, count: 93
Region 23 Avg IOU: 0.747902, Class: 0.999942, Obj: 0.732162, No Obj: 0.007036, .5R: 0.951219, .75R: 0.609756, count: 82
Region 30 Avg IOU: 0.786284, Class: 0.999992, Obj: 0.906183, No Obj: 0.010794, .5R: 0.951542, .75R: 0.746696, count: 454
Region 30 Avg IOU: 0.624224, Class: 0.999793, Obj: 0.468810, No Obj: 0.000325, .5R: 0.500000, .75R: 0.428571, count: 14
Region 16 Avg IOU: 0.823359, Class: 0.999987, Obj: 0.937686, No Obj: 0.033137, .5R: 1.000000, .75R: 0.769231, count: 26
Region 16 Avg IOU: 0.801340, Class: 0.999977, Obj: 0.755342, No Obj: 0.017258, .5R: 0.944444, .75R: 0.722222, count: 18
Region 23 Avg IOU: 0.818220, Class: 0.999974, Obj: 0.881579, No Obj: 0.015712, .5R: 0.954248, .75R: 0.849673, count: 153
Region 23 Avg IOU: 0.838390, Class: 0.999971, Obj: 0.915203, No Obj: 0.016098, .5R: 1.000000, .75R: 0.884615, count: 130
Region 30 Avg IOU: 0.831255, Class: 0.999963, Obj: 0.893451, No Obj: 0.003975, .5R: 0.990385, .75R: 0.826923, count: 104
Region 16 Avg IOU: 0.714124, Class: 0.999946, Obj: 0.858500, No Obj: 0.042607, .5R: 0.948718, .75R: 0.384615, count: 39
Region 23 Avg IOU: 0.702976, Class: 0.999946, Obj: 0.734318, No Obj: 0.012985, .5R: 0.885714, .75R: 0.480000, count: 175
Region 30 Avg IOU: 0.842396, Class: 0.999958, Obj: 0.905178, No Obj: 0.008607, .5R: 0.988550, .75R: 0.881679, count: 262
Region 16 Avg IOU: 0.768322, Class: 0.999997, Obj: 0.917121, No Obj: 0.001788, .5R: 1.000000, .75R: 1.000000, count: 1
Region 23 Avg IOU: 0.823302, Class: 0.999965, Obj: 0.918525, No Obj: 0.007879, .5R: 0.984127, .75R: 0.888889, count: 63
Region 30 Avg IOU: 0.695026, Class: 0.999994, Obj: 0.587920, No Obj: 0.000497, .5R: 0.785714, .75R: 0.571429, count: 14
Region 16 Avg IOU: 0.807127, Class: 0.999989, Obj: 0.903408, No Obj: 0.014255, .5R: 1.000000, .75R: 0.750000, count: 12
Region 23 Avg IOU: 0.831283, Class: 0.999972, Obj: 0.900378, No Obj: 0.013053, .5R: 0.981818, .75R: 0.900000, count: 110
Region 30 Avg IOU: 0.814325, Class: 0.999992, Obj: 0.899325, No Obj: 0.008392, .5R: 0.968153, .75R: 0.805732, count: 314
Region 16 Avg IOU: 0.819781, Class: 0.999989, Obj: 0.903841, No Obj: 0.034691, .5R: 1.000000, .75R: 0.884615, count: 26
Region 30 Avg IOU: 0.837198, Class: 0.999992, Obj: 0.912624, No Obj: 0.006308, .5R: 0.975369, .75R: 0.866995, count: 203
Region 16 Avg IOU: 0.761521, Class: 0.999985, Obj: 0.833169, No Obj: 0.041113, .5R: 0.925000, .75R: 0.675000, count: 40
Region 23 Avg IOU: 0.817798, Class: 0.999978, Obj: 0.931391, No Obj: 0.017814, .5R: 0.967949, .75R: 0.833333, count: 156
Region 23 Avg IOU: 0.804336, Class: 0.999967, Obj: 0.873768, No Obj: 0.015891, .5R: 0.979452, .75R: 0.787671, count: 146
Region 30 Avg IOU: 0.831505, Class: 0.999959, Obj: 0.855128, No Obj: 0.003824, .5R: 0.972727, .75R: 0.872727, count: 110
Region 30 Avg IOU: 0.841639, Class: 0.999935, Obj: 0.900327, No Obj: 0.006237, .5R: 0.994350, .75R: 0.841808, count: 177
Region 16 Avg IOU: 0.723505, Class: 0.999952, Obj: 0.846215, No Obj: 0.102748, .5R: 0.922330, .75R: 0.524272, count: 103
Region 16 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.001620, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 23 Avg IOU: 0.772472, Class: 0.999922, Obj: 0.715603, No Obj: 0.007661, .5R: 0.903614, .75R: 0.698795, count: 83
Region 23 Avg IOU: 0.811732, Class: 0.999970, Obj: 0.865797, No Obj: 0.004987, .5R: 0.937500, .75R: 0.781250, count: 32
Region 30 Avg IOU: 0.653523, Class: 0.999835, Obj: 0.428542, No Obj: 0.000505, .5R: 0.642857, .75R: 0.571429, count: 14
Region 16 Avg IOU: 0.795611, Class: 0.999977, Obj: 0.846498, No Obj: 0.014837, .5R: 1.000000, .75R: 0.750000, count: 12
Region 23 Avg IOU: 0.756210, Class: 0.999974, Obj: 0.850234, No Obj: 0.014752, .5R: 0.916201, .75R: 0.675978, count: 179
Region 30 Avg IOU: 0.798154, Class: 0.999991, Obj: 0.903927, No Obj: 0.010909, .5R: 0.957589, .75R: 0.761161, count: 448
Region 16 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.000088, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 30 Avg IOU: 0.793542, Class: 0.999987, Obj: 0.849811, No Obj: 0.003215, .5R: 0.962264, .75R: 0.726415, count: 106
Region 23 Avg IOU: 0.807716, Class: 0.999967, Obj: 0.909803, No Obj: 0.005633, .5R: 0.942308, .75R: 0.846154, count: 52
Region 16 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.000556, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 23 Avg IOU: 0.728218, Class: 0.999957, Obj: 0.765718, No Obj: 0.001632, .5R: 0.818182, .75R: 0.454545, count: 11
Region 30 Avg IOU: 0.795240, Class: 0.999993, Obj: 0.879535, No Obj: 0.008378, .5R: 0.957187, .75R: 0.743119, count: 327
Region 16 Avg IOU: 0.804749, Class: 0.999978, Obj: 0.534415, No Obj: 0.000905, .5R: 1.000000, .75R: 1.000000, count: 1
Region 30 Avg IOU: 0.743168, Class: 0.999996, Obj: 0.859791, No Obj: 0.008270, .5R: 0.914667, .75R: 0.610667, count: 375
Region 23 Avg IOU: 0.835718, Class: 0.999976, Obj: 0.899011, No Obj: 0.003652, .5R: 1.000000, .75R: 0.923077, count: 26
Region 16 Avg IOU: 0.728175, Class: 0.999966, Obj: 0.808297, No Obj: 0.048727, .5R: 0.891304, .75R: 0.586957, count: 46
Region 23 Avg IOU: 0.730284, Class: 0.999971, Obj: 0.805616, No Obj: 0.010071, .5R: 0.916667, .75R: 0.522727, count: 132
Region 30 Avg IOU: 0.728481, Class: 0.999985, Obj: 0.625471, No Obj: 0.000746, .5R: 0.903226, .75R: 0.612903, count: 31
Region 16 Avg IOU: 0.756243, Class: 0.999973, Obj: 0.885283, No Obj: 0.066740, .5R: 0.888889, .75R: 0.730159, count: 63
Region 30 Avg IOU: 0.798812, Class: 0.999996, Obj: 0.867919, No Obj: 0.008740, .5R: 0.963788, .75R: 0.757660, count: 359
Region 23 Avg IOU: 0.818781, Class: 0.999962, Obj: 0.885960, No Obj: 0.013148, .5R: 0.974138, .75R: 0.844828, count: 116
Region 16 Avg IOU: 0.830917, Class: 0.999974, Obj: 0.963580, No Obj: 0.013290, .5R: 1.000000, .75R: 0.909091, count: 11
Region 23 Avg IOU: 0.834156, Class: 0.999974, Obj: 0.923455, No Obj: 0.013683, .5R: 0.983871, .75R: 0.846774, count: 124
Region 30 Avg IOU: 0.822619, Class: 0.999986, Obj: 0.846776, No Obj: 0.002133, .5R: 0.968750, .75R: 0.828125, count: 64
Region 16 Avg IOU: 0.735456, Class: 0.999920, Obj: 0.934690, No Obj: 0.003623, .5R: 1.000000, .75R: 0.666667, count: 3
Region 23 Avg IOU: 0.746354, Class: 0.999983, Obj: 0.839408, No Obj: 0.014395, .5R: 0.935135, .75R: 0.616216, count: 185
Region 30 Avg IOU: 0.825580, Class: 0.999987, Obj: 0.893646, No Obj: 0.007345, .5R: 0.974684, .75R: 0.848101, count: 237
Region 16 Avg IOU: 0.765672, Class: 0.999973, Obj: 0.825999, No Obj: 0.069791, .5R: 0.957143, .75R: 0.671429, count: 70
Region 23 Avg IOU: 0.785987, Class: 0.999898, Obj: 0.795942, No Obj: 0.015613, .5R: 0.940789, .75R: 0.756579, count: 152
Region 30 Avg IOU: 0.789481, Class: 0.999984, Obj: 0.826173, No Obj: 0.004271, .5R: 0.967742, .75R: 0.716129, count: 155
Region 16 Avg IOU: 0.868873, Class: 0.999998, Obj: 0.983086, No Obj: 0.008609, .5R: 1.000000, .75R: 1.000000, count: 3
Region 23 Avg IOU: 0.822496, Class: 0.999979, Obj: 0.897885, No Obj: 0.009936, .5R: 0.989362, .75R: 0.808511, count: 94
Region 30 Avg IOU: 0.813476, Class: 0.999960, Obj: 0.791598, No Obj: 0.002429, .5R: 0.955882, .75R: 0.794118, count: 68
Region 16 Avg IOU: 0.839227, Class: 0.999984, Obj: 0.977139, No Obj: 0.026227, .5R: 1.000000, .75R: 0.894737, count: 19
Region 23 Avg IOU: 0.819447, Class: 0.999970, Obj: 0.891195, No Obj: 0.011929, .5R: 0.959016, .75R: 0.827869, count: 122
Region 30 Avg IOU: 0.809976, Class: 0.999973, Obj: 0.868464, No Obj: 0.006420, .5R: 0.948357, .75R: 0.793427, count: 213
Region 16 Avg IOU: 0.811305, Class: 0.999956, Obj: 0.934606, No Obj: 0.010881, .5R: 1.000000, .75R: 0.714286, count: 7
Region 23 Avg IOU: 0.834152, Class: 0.999989, Obj: 0.925958, No Obj: 0.011167, .5R: 0.980000, .75R: 0.870000, count: 100
Region 30 Avg IOU: 0.829069, Class: 0.999992, Obj: 0.900042, No Obj: 0.004545, .5R: 0.968000, .75R: 0.880000, count: 125
Region 16 Avg IOU: 0.830712, Class: 0.999970, Obj: 0.962832, No Obj: 0.019166, .5R: 1.000000, .75R: 0.812500, count: 16
Region 23 Avg IOU: 0.759505, Class: 0.999970, Obj: 0.862703, No Obj: 0.017954, .5R: 0.924883, .75R: 0.685446, count: 213
Region 30 Avg IOU: 0.823933, Class: 0.999906, Obj: 0.906162, No Obj: 0.006840, .5R: 0.966346, .75R: 0.841346, count: 208
Region 16 Avg IOU: 0.828858, Class: 0.999945, Obj: 0.999447, No Obj: 0.002872, .5R: 1.000000, .75R: 1.000000, count: 1
Region 30 Avg IOU: 0.767867, Class: 0.999990, Obj: 0.831326, No Obj: 0.001834, .5R: 0.882353, .75R: 0.705882, count: 51
Region 16 Avg IOU: 0.683896, Class: 0.999997, Obj: 0.933464, No Obj: 0.002387, .5R: 1.000000, .75R: 0.000000, count: 1
Region 23 Avg IOU: 0.823557, Class: 0.999982, Obj: 0.911689, No Obj: 0.006363, .5R: 1.000000, .75R: 0.872340, count: 47
Region 23 Avg IOU: 0.739290, Class: 0.999989, Obj: 0.859175, No Obj: 0.013444, .5R: 0.895062, .75R: 0.641975, count: 162
Region 30 Avg IOU: 0.788183, Class: 0.999990, Obj: 0.826461, No Obj: 0.003887, .5R: 0.958042, .75R: 0.727273, count: 143
Region 30 Avg IOU: 0.821235, Class: 0.999995, Obj: 0.932197, No Obj: 0.008962, .5R: 0.964497, .75R: 0.828402, count: 338
Region 16 Avg IOU: 0.844340, Class: 0.999995, Obj: 0.743565, No Obj: 0.002285, .5R: 1.000000, .75R: 1.000000, count: 3
Region 16 Avg IOU: 0.686679, Class: 0.999951, Obj: 0.831300, No Obj: 0.081555, .5R: 0.835443, .75R: 0.481013, count: 79
Region 23 Avg IOU: 0.814791, Class: 0.999987, Obj: 0.917592, No Obj: 0.009837, .5R: 0.976471, .75R: 0.800000, count: 85
Region 23 Avg IOU: 0.756628, Class: 0.999937, Obj: 0.760754, No Obj: 0.009383, .5R: 0.973913, .75R: 0.652174, count: 115
Region 30 Avg IOU: 0.794926, Class: 0.999774, Obj: 0.671351, No Obj: 0.000859, .5R: 0.969697, .75R: 0.787879, count: 33
Region 16 Avg IOU: 0.734111, Class: 0.999964, Obj: 0.865978, No Obj: 0.083815, .5R: 0.930233, .75R: 0.546512, count: 86
Region 30 Avg IOU: 0.826153, Class: 0.999995, Obj: 0.924704, No Obj: 0.006579, .5R: 0.963801, .75R: 0.859729, count: 221
Region 23 Avg IOU: 0.786931, Class: 0.999970, Obj: 0.848437, No Obj: 0.011661, .5R: 0.966102, .75R: 0.720339, count: 118
Region 16 Avg IOU: 0.739137, Class: 0.999966, Obj: 0.835421, No Obj: 0.044483, .5R: 0.894737, .75R: 0.526316, count: 38
Region 23 Avg IOU: 0.787825, Class: 0.999905, Obj: 0.826967, No Obj: 0.013027, .5R: 0.978417, .75R: 0.741007, count: 139
Region 30 Avg IOU: 0.708966, Class: 0.999966, Obj: 0.688849, No Obj: 0.001135, .5R: 0.805556, .75R: 0.527778, count: 36
Region 16 Avg IOU: 0.687439, Class: 0.999965, Obj: 0.905719, No Obj: 0.004698, .5R: 1.000000, .75R: 0.333333, count: 3
Region 30 Avg IOU: 0.793689, Class: 0.999981, Obj: 0.826000, No Obj: 0.002307, .5R: 0.920000, .75R: 0.773333, count: 75
Region 16 Avg IOU: 0.695431, Class: 0.999961, Obj: 0.779716, No Obj: 0.089711, .5R: 0.851064, .75R: 0.478723, count: 94
Region 23 Avg IOU: 0.808466, Class: 0.999976, Obj: 0.919033, No Obj: 0.008497, .5R: 0.987342, .75R: 0.835443, count: 79
Region 23 Avg IOU: 0.744129, Class: 0.999778, Obj: 0.707510, No Obj: 0.010251, .5R: 0.937500, .75R: 0.633929, count: 112
Region 30 Avg IOU: 0.762583, Class: 0.999985, Obj: 0.628209, No Obj: 0.000842, .5R: 0.964286, .75R: 0.642857, count: 28
Region 16 Avg IOU: 0.817415, Class: 0.999990, Obj: 0.935950, No Obj: 0.024665, .5R: 1.000000, .75R: 0.842105, count: 19
Region 30 Avg IOU: 0.801141, Class: 0.999991, Obj: 0.891405, No Obj: 0.006743, .5R: 0.973485, .75R: 0.776515, count: 264
Region 16 Avg IOU: 0.809184, Class: 0.999970, Obj: 0.876042, No Obj: 0.029681, .5R: 1.000000, .75R: 0.695652, count: 23
Region 23 Avg IOU: 0.828909, Class: 0.999964, Obj: 0.894897, No Obj: 0.018004, .5R: 0.968553, .75R: 0.849057, count: 159
Region 23 Avg IOU: 0.821317, Class: 0.999967, Obj: 0.904160, No Obj: 0.015156, .5R: 0.964789, .75R: 0.823944, count: 142
Region 30 Avg IOU: 0.841089, Class: 0.999968, Obj: 0.892012, No Obj: 0.007526, .5R: 0.981481, .75R: 0.870370, count: 216
Region 30 Avg IOU: 0.840739, Class: 0.999976, Obj: 0.888457, No Obj: 0.005702, .5R: 0.993976, .75R: 0.867470, count: 166
Region 16 Avg IOU: 0.781864, Class: 0.999953, Obj: 0.881708, No Obj: 0.041029, .5R: 0.972222, .75R: 0.666667, count: 36
Region 16 Avg IOU: 0.764376, Class: 0.999987, Obj: 0.878910, No Obj: 0.051864, .5R: 0.913043, .75R: 0.652174, count: 46
Region 23 Avg IOU: 0.751753, Class: 0.999971, Obj: 0.836366, No Obj: 0.012956, .5R: 0.949045, .75R: 0.611465, count: 157
Region 23 Avg IOU: 0.804121, Class: 0.999928, Obj: 0.861406, No Obj: 0.017627, .5R: 0.969697, .75R: 0.763636, count: 165
Region 30 Avg IOU: 0.677028, Class: 0.999953, Obj: 0.525213, No Obj: 0.000462, .5R: 0.750000, .75R: 0.562500, count: 16
Region 16 Avg IOU: 0.782719, Class: 0.999969, Obj: 0.899336, No Obj: 0.044243, .5R: 0.953488, .75R: 0.651163, count: 43
Region 23 Avg IOU: 0.753807, Class: 0.999974, Obj: 0.851542, No Obj: 0.014725, .5R: 0.925714, .75R: 0.645714, count: 175
Region 30 Avg IOU: 0.834854, Class: 0.999956, Obj: 0.849065, No Obj: 0.003577, .5R: 0.990000, .75R: 0.820000, count: 100
Region 16 Avg IOU: 0.749831, Class: 0.999984, Obj: 0.838192, No Obj: 0.050088, .5R: 0.956522, .75R: 0.478261, count: 46
Region 23 Avg IOU: 0.814223, Class: 0.999950, Obj: 0.866806, No Obj: 0.020239, .5R: 0.978261, .75R: 0.815217, count: 184
Region 30 Avg IOU: 0.721071, Class: 0.999985, Obj: 0.644253, No Obj: 0.000839, .5R: 0.826087, .75R: 0.565217, count: 23
Region 16 Avg IOU: 0.784772, Class: 0.999982, Obj: 0.901517, No Obj: 0.016070, .5R: 0.928571, .75R: 0.785714, count: 14
Region 23 Avg IOU: 0.827615, Class: 0.999980, Obj: 0.913604, No Obj: 0.014455, .5R: 0.984496, .75R: 0.813953, count: 129
Region 30 Avg IOU: 0.833156, Class: 0.999951, Obj: 0.862914, No Obj: 0.005820, .5R: 0.975904, .75R: 0.897590, count: 166
Region 16 Avg IOU: 0.726489, Class: 0.999987, Obj: 0.897475, No Obj: 0.012095, .5R: 0.833333, .75R: 0.583333, count: 12
Region 23 Avg IOU: 0.837501, Class: 0.999979, Obj: 0.910322, No Obj: 0.009851, .5R: 1.000000, .75R: 0.895349, count: 86
Region 30 Avg IOU: 0.844345, Class: 0.999991, Obj: 0.913254, No Obj: 0.006977, .5R: 1.000000, .75R: 0.883117, count: 231
Region 16 Avg IOU: 0.823989, Class: 0.999997, Obj: 0.723179, No Obj: 0.003171, .5R: 1.000000, .75R: 1.000000, count: 3
Region 23 Avg IOU: 0.810352, Class: 0.999977, Obj: 0.880109, No Obj: 0.008153, .5R: 0.985294, .75R: 0.779412, count: 68
Region 30 Avg IOU: 0.838214, Class: 0.999997, Obj: 0.923439, No Obj: 0.004910, .5R: 0.968153, .75R: 0.878981, count: 157
Region 16 Avg IOU: 0.855575, Class: 0.999993, Obj: 0.998527, No Obj: 0.003324, .5R: 1.000000, .75R: 1.000000, count: 2
Region 23 Avg IOU: 0.841747, Class: 0.999961, Obj: 0.896440, No Obj: 0.007184, .5R: 1.000000, .75R: 0.931035, count: 58
Region 30 Avg IOU: 0.800070, Class: 0.999987, Obj: 0.869960, No Obj: 0.010351, .5R: 0.947090, .75R: 0.748677, count: 378
Region 16 Avg IOU: 0.851512, Class: 0.999986, Obj: 0.990764, No Obj: 0.007276, .5R: 1.000000, .75R: 0.833333, count: 6
Region 23 Avg IOU: 0.810471, Class: 0.999994, Obj: 0.928741, No Obj: 0.009660, .5R: 0.965909, .75R: 0.829545, count: 88
Region 30 Avg IOU: 0.820121, Class: 0.999990, Obj: 0.913992, No Obj: 0.010407, .5R: 0.980978, .75R: 0.812500, count: 368
Region 16 Avg IOU: 0.715895, Class: 0.999975, Obj: 0.795910, No Obj: 0.030892, .5R: 0.838710, .75R: 0.483871, count: 31
Region 23 Avg IOU: 0.748909, Class: 0.999960, Obj: 0.819991, No Obj: 0.014581, .5R: 0.932203, .75R: 0.638418, count: 177
Region 30 Avg IOU: 0.830100, Class: 0.999993, Obj: 0.908200, No Obj: 0.007252, .5R: 0.982833, .75R: 0.858369, count: 233
Region 16 Avg IOU: 0.764322, Class: 0.999986, Obj: 0.894828, No Obj: 0.022776, .5R: 0.952381, .75R: 0.666667, count: 21
Region 23 Avg IOU: 0.840994, Class: 0.999973, Obj: 0.921263, No Obj: 0.015174, .5R: 0.984375, .75R: 0.890625, count: 128
Region 30 Avg IOU: 0.800070, Class: 0.999990, Obj: 0.769804, No Obj: 0.002574, .5R: 0.980198, .75R: 0.762376, count: 101
Region 16 Avg IOU: 0.829293, Class: 0.999980, Obj: 0.932202, No Obj: 0.008098, .5R: 1.000000, .75R: 1.000000, count: 4
Region 23 Avg IOU: 0.784154, Class: 0.999987, Obj: 0.886683, No Obj: 0.014075, .5R: 0.947368, .75R: 0.743421, count: 152
Region 30 Avg IOU: 0.836122, Class: 0.999989, Obj: 0.900905, No Obj: 0.005857, .5R: 0.958824, .75R: 0.858824, count: 170
Region 16 Avg IOU: 0.786238, Class: 0.999982, Obj: 0.903853, No Obj: 0.013818, .5R: 1.000000, .75R: 0.727273, count: 11
Region 23 Avg IOU: 0.847247, Class: 0.999983, Obj: 0.929622, No Obj: 0.011979, .5R: 1.000000, .75R: 0.920455, count: 88
Region 30 Avg IOU: 0.784996, Class: 0.999989, Obj: 0.840831, No Obj: 0.004312, .5R: 0.949367, .75R: 0.702532, count: 158
Region 16 Avg IOU: 0.821757, Class: 0.999986, Obj: 0.856214, No Obj: 0.016934, .5R: 1.000000, .75R: 0.769231, count: 13
Region 23 Avg IOU: 0.833958, Class: 0.999971, Obj: 0.920705, No Obj: 0.015500, .5R: 0.969925, .75R: 0.887218, count: 133
Region 30 Avg IOU: 0.836080, Class: 0.999994, Obj: 0.911600, No Obj: 0.005904, .5R: 0.975000, .75R: 0.880000, count: 200
Region 16 Avg IOU: 0.860321, Class: 0.999982, Obj: 0.943921, No Obj: 0.013424, .5R: 1.000000, .75R: 0.909091, count: 11
Region 23 Avg IOU: 0.846539, Class: 0.999977, Obj: 0.907655, No Obj: 0.012140, .5R: 1.000000, .75R: 0.897196, count: 107
Region 30 Avg IOU: 0.846313, Class: 0.999963, Obj: 0.919429, No Obj: 0.010122, .5R: 0.974922, .75R: 0.890282, count: 319
Region 16 Avg IOU: 0.804526, Class: 0.999985, Obj: 0.883302, No Obj: 0.014614, .5R: 1.000000, .75R: 0.785714, count: 14
Region 30 Avg IOU: 0.827951, Class: 0.999989, Obj: 0.913083, No Obj: 0.006716, .5R: 0.970874, .75R: 0.844660, count: 206
Region 23 Avg IOU: 0.831154, Class: 0.999964, Obj: 0.906198, No Obj: 0.014267, .5R: 0.958333, .75R: 0.866667, count: 120
Region 16 Avg IOU: 0.699172, Class: 0.999980, Obj: 0.731805, No Obj: 0.010880, .5R: 0.900000, .75R: 0.200000, count: 10
Region 23 Avg IOU: 0.761019, Class: 0.999982, Obj: 0.804587, No Obj: 0.009781, .5R: 0.946903, .75R: 0.672566, count: 113
Region 30 Avg IOU: 0.851578, Class: 0.999990, Obj: 0.925308, No Obj: 0.007755, .5R: 0.995851, .75R: 0.921162, count: 241
Region 30 Avg IOU: 0.780085, Class: 0.999993, Obj: 0.804418, No Obj: 0.004507, .5R: 0.962162, .75R: 0.724324, count: 185
Region 16 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.000318, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 16 Avg IOU: 0.616816, Class: 0.999960, Obj: 0.959947, No Obj: 0.004170, .5R: 1.000000, .75R: 0.000000, count: 1
Region 23 Avg IOU: 0.819485, Class: 0.999991, Obj: 0.907204, No Obj: 0.004251, .5R: 1.000000, .75R: 0.766667, count: 30
Region 23 Avg IOU: 0.820923, Class: 0.999995, Obj: 0.885072, No Obj: 0.004653, .5R: 1.000000, .75R: 0.790698, count: 43
Region 30 Avg IOU: 0.799465, Class: 0.999995, Obj: 0.878893, No Obj: 0.008053, .5R: 0.979290, .75R: 0.751479, count: 338
Region 30 Avg IOU: 0.807331, Class: 0.999991, Obj: 0.915093, No Obj: 0.010080, .5R: 0.979221, .75R: 0.789610, count: 385
Region 16 Avg IOU: 0.789021, Class: 0.999979, Obj: 0.922653, No Obj: 0.019710, .5R: 0.937500, .75R: 0.812500, count: 16
Region 16 Avg IOU: 0.679676, Class: 0.999958, Obj: 0.769796, No Obj: 0.018956, .5R: 0.842105, .75R: 0.315789, count: 19
Region 23 Avg IOU: 0.695239, Class: 0.999983, Obj: 0.775054, No Obj: 0.009923, .5R: 0.898551, .75R: 0.398551, count: 138
Region 23 Avg IOU: 0.810022, Class: 0.999976, Obj: 0.866698, No Obj: 0.013862, .5R: 0.969231, .75R: 0.807692, count: 130
Region 30 Avg IOU: 0.734211, Class: 0.999998, Obj: 0.642476, No Obj: 0.000749, .5R: 0.939394, .75R: 0.545455, count: 33
Region 16 Avg IOU: 0.767734, Class: 0.999976, Obj: 0.833824, No Obj: 0.023392, .5R: 0.954545, .75R: 0.727273, count: 22
Region 23 Avg IOU: 0.802979, Class: 0.999989, Obj: 0.883146, No Obj: 0.011415, .5R: 0.950000, .75R: 0.770000, count: 100
Region 30 Avg IOU: 0.837209, Class: 0.999987, Obj: 0.903611, No Obj: 0.007106, .5R: 0.973958, .75R: 0.869792, count: 192
Region 16 Avg IOU: 0.668998, Class: 0.999981, Obj: 0.987828, No Obj: 0.010998, .5R: 0.750000, .75R: 0.500000, count: 8
Region 23 Avg IOU: 0.827531, Class: 0.999971, Obj: 0.911575, No Obj: 0.012374, .5R: 0.974138, .75R: 0.844828, count: 116
Region 30 Avg IOU: 0.820669, Class: 0.999988, Obj: 0.896960, No Obj: 0.003688, .5R: 0.981308, .75R: 0.869159, count: 107
Region 16 Avg IOU: 0.806319, Class: 0.999984, Obj: 0.971032, No Obj: 0.024281, .5R: 1.000000, .75R: 0.684211, count: 19
Region 23 Avg IOU: 0.812562, Class: 0.999981, Obj: 0.916872, No Obj: 0.010771, .5R: 0.980198, .75R: 0.762376, count: 101
Region 30 Avg IOU: 0.841251, Class: 0.999986, Obj: 0.909663, No Obj: 0.007547, .5R: 0.996109, .75R: 0.887160, count: 257
Region 30 Avg IOU: 0.808331, Class: 0.999990, Obj: 0.874044, No Obj: 0.003221, .5R: 0.941176, .75R: 0.803922, count: 102
Region 16 Avg IOU: 0.810921, Class: 0.999983, Obj: 0.711703, No Obj: 0.014809, .5R: 1.000000, .75R: 0.800000, count: 15
Region 16 Avg IOU: 0.731560, Class: 0.999985, Obj: 0.856055, No Obj: 0.040721, .5R: 0.882353, .75R: 0.647059, count: 34
Region 23 Avg IOU: 0.826202, Class: 0.999980, Obj: 0.916940, No Obj: 0.013367, .5R: 0.973913, .75R: 0.860870, count: 115
Region 23 Avg IOU: 0.822452, Class: 0.999928, Obj: 0.852402, No Obj: 0.019589, .5R: 0.983784, .75R: 0.837838, count: 185
Region 30 Avg IOU: 0.825854, Class: 0.999987, Obj: 0.906565, No Obj: 0.006243, .5R: 0.972222, .75R: 0.842593, count: 216
Region 16 Avg IOU: 0.814519, Class: 0.999987, Obj: 0.933728, No Obj: 0.002511, .5R: 1.000000, .75R: 1.000000, count: 2
Region 30 Avg IOU: 0.839198, Class: 0.999977, Obj: 0.873315, No Obj: 0.005345, .5R: 0.973856, .75R: 0.882353, count: 153
Region 16 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.002313, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 23 Avg IOU: 0.832005, Class: 0.999976, Obj: 0.895711, No Obj: 0.006831, .5R: 1.000000, .75R: 0.925926, count: 54
Region 23 Avg IOU: 0.828190, Class: 0.999992, Obj: 0.938989, No Obj: 0.006300, .5R: 1.000000, .75R: 0.934783, count: 46
Region 30 Avg IOU: 0.819601, Class: 0.999992, Obj: 0.909005, No Obj: 0.008410, .5R: 0.968847, .75R: 0.819315, count: 321
Region 16 Avg IOU: 0.776401, Class: 0.999954, Obj: 0.868690, No Obj: 0.011609, .5R: 1.000000, .75R: 0.800000, count: 10
Region 30 Avg IOU: 0.816808, Class: 0.999992, Obj: 0.920379, No Obj: 0.009651, .5R: 0.964481, .75R: 0.833333, count: 366
Region 23 Avg IOU: 0.753238, Class: 0.999982, Obj: 0.858025, No Obj: 0.015354, .5R: 0.914894, .75R: 0.659574, count: 188
Region 16 Avg IOU: 0.807330, Class: 0.999975, Obj: 0.864445, No Obj: 0.015280, .5R: 1.000000, .75R: 0.777778, count: 9
Region 23 Avg IOU: 0.798456, Class: 0.999975, Obj: 0.930415, No Obj: 0.011012, .5R: 0.954955, .75R: 0.810811, count: 111
Region 30 Avg IOU: 0.804671, Class: 0.999982, Obj: 0.837926, No Obj: 0.004253, .5R: 0.972789, .75R: 0.775510, count: 147
Region 30 Avg IOU: 0.831704, Class: 0.999980, Obj: 0.867492, No Obj: 0.005674, .5R: 0.985148, .75R: 0.856436, count: 202
Region 16 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.000009, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 23 Avg IOU: 0.832461, Class: 0.999994, Obj: 0.798156, No Obj: 0.001867, .5R: 1.000000, .75R: 0.818182, count: 11
Region 30 Avg IOU: 0.752878, Class: 0.999991, Obj: 0.838917, No Obj: 0.008637, .5R: 0.937811, .75R: 0.609453, count: 402
Syncing... Done!

(next mAP calculation at 8040 iterations)
Last accuracy mAP@0.5 = 23.18 %
8004: 29.937290, 28.569744 avg loss, 0.000200 rate, 31.780000 seconds, 1024512 images

E:\trainingdata\baochuang\test\newtest\3-14all\train>darknet detector map baocuang.data yolov3-tiny_3l_1.cfg yolov3-tiny_3l_1_final.weights
compute_capability = 610, cudnn_half = 0
layer filters size input output
0 conv 16 3 x 3 / 1 1024 x1024 x 3 -> 1024 x1024 x 16 0.906 BF
1 max 2 x 2 / 2 1024 x1024 x 16 -> 512 x 512 x 16 0.017 BF
2 conv 32 3 x 3 / 1 512 x 512 x 16 -> 512 x 512 x 32 2.416 BF
3 max 2 x 2 / 2 512 x 512 x 32 -> 256 x 256 x 32 0.008 BF
4 conv 64 3 x 3 / 1 256 x 256 x 32 -> 256 x 256 x 64 2.416 BF
5 max 2 x 2 / 2 256 x 256 x 64 -> 128 x 128 x 64 0.004 BF
6 conv 128 3 x 3 / 1 128 x 128 x 64 -> 128 x 128 x 128 2.416 BF
7 max 2 x 2 / 2 128 x 128 x 128 -> 64 x 64 x 128 0.002 BF
8 conv 256 3 x 3 / 1 64 x 64 x 128 -> 64 x 64 x 256 2.416 BF
9 max 2 x 2 / 2 64 x 64 x 256 -> 32 x 32 x 256 0.001 BF
10 conv 512 3 x 3 / 1 32 x 32 x 256 -> 32 x 32 x 512 2.416 BF
11 max 2 x 2 / 1 32 x 32 x 512 -> 32 x 32 x 512 0.002 BF
12 conv 1024 3 x 3 / 1 32 x 32 x 512 -> 32 x 32 x1024 9.664 BF
13 conv 256 1 x 1 / 1 32 x 32 x1024 -> 32 x 32 x 256 0.537 BF
14 conv 512 3 x 3 / 1 32 x 32 x 256 -> 32 x 32 x 512 2.416 BF
15 conv 8 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 8 0.008 BF
16 yolo
17 route 13
18 conv 128 1 x 1 / 1 32 x 32 x 256 -> 32 x 32 x 128 0.067 BF
19 upsample 2x 32 x 32 x 128 -> 64 x 64 x 128
20 route 19 8
21 conv 256 3 x 3 / 1 64 x 64 x 384 -> 64 x 64 x 256 7.248 BF
22 conv 32 1 x 1 / 1 64 x 64 x 256 -> 64 x 64 x 32 0.067 BF
23 yolo
24 route 21
25 conv 128 1 x 1 / 1 64 x 64 x 256 -> 64 x 64 x 128 0.268 BF
26 upsample 2x 64 x 64 x 128 -> 128 x 128 x 128
27 route 26 6
28 conv 128 3 x 3 / 1 128 x 128 x 256 -> 128 x 128 x 128 9.664 BF
29 conv 32 1 x 1 / 1 128 x 128 x 128 -> 128 x 128 x 32 0.134 BF
30 yolo
Total BFLOPS 43.093
Allocate additional workspace_size = 52.43 MB
Loading weights from yolov3-tiny_3l_1_final.weights...
seen 64
Done!

calculation mAP (mean average precision)...
3
detections_count = 1727, unique_truth_count = 1204
class_id = 0, name = sperm, ap = 69.15% (TP = 956, FP = 310)
class_id = 1, name = round cell, ap = 0.00% (TP = 0, FP = 0)
class_id = 2, name = red cell, ap = 0.00% (TP = 0, FP = 0)

for thresh = 0.25, precision = 0.76, recall = 0.79, F1-score = 0.77
for thresh = 0.25, TP = 956, FP = 310, FN = 248, average IoU = 49.44 %

IoU threshold = 50 %, used Area-Under-Curve for each unique Recall
mean average precision (mAP@0.50) = 0.230516, or 23.05 %
Total Detection Time: 0.000000 Seconds

Set -points flag:
-points 101 for MS COCO
-points 11 for PascalVOC 2007 (uncomment difficult in voc.data)
-points 0 (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset

[AlexeyAB]

@wting861006

class_id = 0, name = sperm, ap = 69.15% (TP = 956, FP = 310)

mAP ~ 70% is a good result for Yolov3 Tiny 3L.