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AlexeyAB / darknet

YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
http://pjreddie.com/darknet/
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Labels not showing on bounding boxes #7649

Open kXborg opened 3 years ago

kXborg commented 3 years ago

Trained model on colab. While testing it on samples, the detections are good but labels and accuracy is not showing. Only a black tape over the bounding boxes.

kXborg commented 3 years ago

Similar to this, https://stackoverflow.com/questions/62286056/yolo-cannot-display-the-class-name-on-prediction-picture/67277333#67277333

ekesdf commented 3 years ago

Did you check your file.names if the amount of class in that is equal to those trained in the model ?

kXborg commented 3 years ago

Yes. It is correct. No issues with that

ekesdf commented 3 years ago

what for a cmd do you used for prediction ??

kXborg commented 3 years ago

test_path = "darknet/data/test/test1.jpg" !darknet/darknet detector test custom_data/labelled_data.data darknet/cfg/yolov4-tiny-tealeaf.cfg backup/yolov4-tiny-tealeaf_best.weights {test_path} -dont_Show

ekesdf commented 3 years ago

and the output given using this cmd ? and try it with out "-dont_show"

kXborg commented 3 years ago

CUDA-version: 11000 (11020), cuDNN: 7.6.5, GPU count: 1
OpenCV version: 3.2.0 Cannot load image data/labels/32_0.png Cannot load image data/labels/33_0.png Cannot load image data/labels/34_0.png Cannot load image data/labels/35_0.png Cannot load image data/labels/36_0.png Cannot load image data/labels/37_0.png Cannot load image data/labels/38_0.png Cannot load image data/labels/39_0.png Cannot load image data/labels/40_0.png Cannot load image data/labels/41_0.png Cannot load image data/labels/42_0.png Cannot load image data/labels/43_0.png Cannot load image data/labels/44_0.png Cannot load image data/labels/45_0.png Cannot load image data/labels/46_0.png Cannot load image data/labels/47_0.png Cannot load image data/labels/48_0.png Cannot load image data/labels/49_0.png Cannot load image data/labels/50_0.png Cannot load image data/labels/51_0.png Cannot load image data/labels/52_0.png Cannot load image data/labels/53_0.png Cannot load image data/labels/54_0.png Cannot load image data/labels/55_0.png Cannot load image data/labels/56_0.png Cannot load image data/labels/57_0.png Cannot load image data/labels/58_0.png Cannot load image data/labels/59_0.png Cannot load image data/labels/60_0.png Cannot load image data/labels/61_0.png Cannot load image data/labels/62_0.png Cannot load image data/labels/63_0.png Cannot load image data/labels/64_0.png Cannot load image data/labels/65_0.png Cannot load image data/labels/66_0.png Cannot load image data/labels/67_0.png Cannot load image data/labels/68_0.png Cannot load image data/labels/69_0.png Cannot load image data/labels/70_0.png Cannot load image data/labels/71_0.png Cannot load image data/labels/72_0.png Cannot load image data/labels/73_0.png Cannot load image data/labels/74_0.png Cannot load image data/labels/75_0.png Cannot load image data/labels/76_0.png Cannot load image data/labels/77_0.png Cannot load image data/labels/78_0.png Cannot load image data/labels/79_0.png Cannot load image data/labels/80_0.png Cannot load image data/labels/81_0.png Cannot load image 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Cannot load image data/labels/63_1.png Cannot load image data/labels/64_1.png Cannot load image data/labels/65_1.png Cannot load image data/labels/66_1.png Cannot load image data/labels/67_1.png Cannot load image data/labels/68_1.png Cannot load image data/labels/69_1.png Cannot load image data/labels/70_1.png Cannot load image data/labels/71_1.png Cannot load image data/labels/72_1.png Cannot load image data/labels/73_1.png Cannot load image data/labels/74_1.png Cannot load image data/labels/75_1.png Cannot load image data/labels/76_1.png Cannot load image data/labels/77_1.png Cannot load image data/labels/78_1.png Cannot load image data/labels/79_1.png Cannot load image data/labels/80_1.png Cannot load image data/labels/81_1.png Cannot load image data/labels/82_1.png Cannot load image data/labels/83_1.png Cannot load image data/labels/84_1.png Cannot load image data/labels/85_1.png Cannot load image data/labels/86_1.png Cannot load image data/labels/87_1.png Cannot load image 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data/labels/124_7.png Cannot load image data/labels/125_7.png Cannot load image data/labels/126_7.png 0 : compute_capability = 750, cudnn_half = 0, GPU: Tesla T4 net.optimized_memory = 0 mini_batch = 1, batch = 1, time_steps = 1, train = 0 layer filters size/strd(dil) input output 0 Create CUDA-stream - 0 Create cudnn-handle 0 conv 32 3 x 3/ 2 416 x 416 x 3 -> 208 x 208 x 32 0.075 BF 1 conv 64 3 x 3/ 2 208 x 208 x 32 -> 104 x 104 x 64 0.399 BF 2 conv 64 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.797 BF 3 route 2 1/2 -> 104 x 104 x 32 4 conv 32 3 x 3/ 1 104 x 104 x 32 -> 104 x 104 x 32 0.199 BF 5 conv 32 3 x 3/ 1 104 x 104 x 32 -> 104 x 104 x 32 0.199 BF 6 route 5 4 -> 104 x 104 x 64 7 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF 8 route 2 7 -> 104 x 104 x 128 9 max 2x 2/ 2 104 x 104 x 128 -> 52 x 52 x 128 0.001 BF 10 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 11 route 10 1/2 -> 52 x 52 x 64 12 conv 64 3 x 3/ 1 52 x 52 x 64 -> 52 x 52 x 64 0.199 BF 13 conv 64 3 x 3/ 1 52 x 52 x 64 -> 52 x 52 x 64 0.199 BF 14 route 13 12 -> 52 x 52 x 128 15 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 16 route 10 15 -> 52 x 52 x 256 17 max 2x 2/ 2 52 x 52 x 256 -> 26 x 26 x 256 0.001 BF 18 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 19 route 18 1/2 -> 26 x 26 x 128 20 conv 128 3 x 3/ 1 26 x 26 x 128 -> 26 x 26 x 128 0.199 BF 21 conv 128 3 x 3/ 1 26 x 26 x 128 -> 26 x 26 x 128 0.199 BF 22 route 21 20 -> 26 x 26 x 256 23 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 24 route 18 23 -> 26 x 26 x 512 25 max 2x 2/ 2 26 x 26 x 512 -> 13 x 13 x 512 0.000 BF 26 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF 27 conv 256 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 256 0.044 BF 28 conv 512 3 x 3/ 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BF 29 conv 21 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 21 0.004 BF 30 yolo [yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05 nms_kind: greedynms (1), beta = 0.600000 31 route 27 -> 13 x 13 x 256 32 conv 128 1 x 1/ 1 13 x 13 x 256 -> 13 x 13 x 128 0.011 BF 33 upsample 2x 13 x 13 x 128 -> 26 x 26 x 128 34 route 33 23 -> 26 x 26 x 384 35 conv 256 3 x 3/ 1 26 x 26 x 384 -> 26 x 26 x 256 1.196 BF 36 conv 21 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 21 0.007 BF 37 yolo [yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05 nms_kind: greedynms (1), beta = 0.600000 Total BFLOPS 6.789 avg_outputs = 299797 Allocate additional workspace_size = 26.22 MB Loading weights from backup/yolov4-tiny-tealeaf_best.weights... seen 64, trained: 89 K-images (1 Kilo-batches_64) Done! Loaded 38 layers from weights-file Detection layer: 30 - type = 28 Detection layer: 37 - type = 28 darknet/data/test/test1.jpg: Predicted in 5.278000 milli-seconds. good: 100% good: 99% good: 98% good: 100% good: 99% good: 99% good: 98% good: 99% good: 99% Unable to init server: Could not connect: Connection refused

(predictions:1189): Gtk-WARNING **: 02:35:21.700: cannot open display:

kXborg commented 3 years ago

Same result when dont_show flag is removed

ekesdf commented 3 years ago

detect.txt use this code as a .py file and it will give the same or even better results predicted

ekesdf commented 3 years ago

Well the whole class and accuracy drawing of darknet is defective, so I have to look further in to it, but the program should help you out until it is fixed :)

kXborg commented 3 years ago

Hi, well I don't know the reason. It seems like a bug. The model worked well after I deployed it to an OAK-D.

bharath5673 commented 3 years ago

ok @kukil-kashyap-borgohain i think i got this//

can u check ur makefile once inside darknet folder?

it shud be in this way

GPU=1
CUDNN=1
CUDNN_HALF=0 # ###mainly this one has to be 0
OPENCV=1
AVX=0
OPENMP=1
LIBSO=0
ZED_CAMERA=0
ZED_CAMERA_v2_8=0

make clean <- rebuild <- again and run demo

kXborg commented 3 years ago

Yes, this works. On my system, I compiled darknet without CUDA and CUDNN. So all are zero.

GPU=0
CUDNN=0
CUDNN_HALF=0
OPENCV=1
AVX=0
OPENMP=0
LIBSO=0
ZED_CAMERA=0
ZED_CAMERA_v2_8=0

This works well, but on colab it is producing strange result. Do you have any idea on that?

bharath5673 commented 3 years ago 
GPU=1
CUDNN=1
CUDNN_HALF=0  # or 1
OPENCV=1
AVX=0
OPENMP=1
LIBSO=0
ZED_CAMERA=0
ZED_CAMERA_v2_8=0

this settings works pretty well on colab too//

ok u can check this NoteBook, and its runReady

bharath5673 commented 3 years ago

Yes, this works. On my system, I compiled darknet without CUDA and CUDNN. So all are zero.

GPU=0
CUDNN=0
CUDNN_HALF=0
OPENCV=1
AVX=0
OPENMP=0
LIBSO=0
ZED_CAMERA=0
ZED_CAMERA_v2_8=0

This works well, but on colab it is producing strange result. Do you have any idea on that?

yes i just tested ,, even ur setting works well on colab

kXborg commented 3 years ago

Please try my notebook if u get time. You already have access to it.

kalikhademi commented 3 years ago

Please try my notebook if u get time. You already have access to it.

How did you change the test path so it is not showing the cannot load picture again?

ekesdf commented 3 years ago

Please try my notebook if u get time. You already have access to it.

How did you change the test path so it is not showing the cannot load picture again?

he just added the -dont_show argument to his test cmd

ridvanozdemir commented 3 years ago

is your image gray scale? I got some issue when i make chanel number 1 for gray scale image training, changing it back to 3 was the only solution I could find.

bharath5673 commented 3 years ago

ithink this problem is soved.. the actual problem is with the colab or environments, this may happens sometimes,, rebuilding everything from the start is the solution..

hadign20 commented 2 years ago

ok @kukil-kashyap-borgohain i think i got this//

can u check ur makefile once inside darknet folder?

it shud be in this way

GPU=1
CUDNN=1
CUDNN_HALF=0 # ###mainly this one has to be 0
OPENCV=1
AVX=0
OPENMP=1
LIBSO=0
ZED_CAMERA=0
ZED_CAMERA_v2_8=0

make clean <- rebuild <- again and run demo

I have the same problem (on windows). I tried setting Cudnn_Half to off but the problem is still there. only the bounding box and a black box is showing for each object without any text for the label.

ekesdf commented 2 years ago

Hi @hadi-ghnd, @bharath5673 the problem is already known. The problem is not the naming of the objects itself, it is the way darknet displays them in the image. So if you want to implement a new function that can "print" the text into an image in C, feel free.

hadign20 commented 2 years ago

@ekesdf Thanks. The problem is solved. I looked at the source code and found out the program uses the names in the labels folder to create the new labels. So we have to add everything in the data folder to the data folder of our custom dataset.

I built darknet in a folder called build. So inside darknet/build/Release looks like this:

├───Release
│   ├───backup
│   ├───cfg
│   ├───data
│   │   ├───img
│   │   ├───labels

image

hadign20 commented 2 years ago

Hi,

Thank you. I added it to the comment.

On Tue, Nov 2, 2021 at 11:20 AM Spezie.py @.***> wrote:

Hi,

Can you plz comment your current dataset structure so everyone with the same problem can solve it to your example they.

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