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praneeth0609 opened this issue on 6 Aug 2020 · 24 comments
praneeth0609 opened this issue on 6 Aug 2020 · 24 comments
hello...
Currently, I'm working on custom object detection like birds, etc.
I need to detect small objects also. but am not able to detect small objects. To get detect small objects, what I need to do.
Here are my questions:
@praneeth0609 I am also working currently on a project to detect objects which are even smaller than mentioned by you. The objects are small stones, nuts, bolts, screws etc. I am also facing the same issue that I have trained Yolov3 and Yolov4 on my custom dataset but not able to get good results. So any solution?
check this repo and search "small object" , he explained how to train for small objects, smaller than 16 x 16.
@MuhammadAsadJaved . Yes I have checked that repo but not getting good results!
@MuhammadAsadJaved , thanks for your reply.
I have checked https://github.com/AlexeyAB/darknet and i followed those instructions.
can you give some suggestions on below quires:
hello @pjreddie @MuhammadAsadJaved
I have a camera with 30X optical Zoom, and I want to detect birds, which are at long distances.
give me some suggestions:
@MuhammadAsadJaved @praneeth0609 . I have tried the training of model for smaller objects as specified on https://github.com/AlexeyAB/darknet. But still not getting good results on small objects like bolts, screws, stones etc. Kindly solve this issue?
I haven't worked with this problem, I just saw this information so shared with you.
On Mon, Aug 10, 2020 at 11:02 AM Farjad3253 ***@***.***> wrote: @MuhammadAsadJaved @praneeth0609 . I have tried the training of model for smaller objects as specified on https://github.com/AlexeyAB/darknet. But still not getting good results on small objects like bolts, screws, stones etc. Kindly solve this issue? — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#2256 (comment)>, or unsubscribe .
When you're going to detect the object, try to set higher values on width and height on your cfg file. I managed to detect really small objects on a image 4000x3000 with the width and height on 2560.
@Tuumix . What was the input size which you specified in the cfg file?
@Farjad3253 for training it was 768 and for detection 2560
@Tuumix . Ok you mean that for training it was 768x768 and while testing you used 2560x2569? Is it so?
Yes, for training I cropped the image in 768x768x and for detection I use the full resolution image but the cfg file I used 2560x2560
@Tuumix . Ok I got your point that while you were training you specified the resolution as 768x768 in .cfg file!
Yees, sorry if my explanation was a little bit confusing haha
@Tuumix . No problem dear! Atleast you tried to help us. Thanks!
Yes, for training I cropped the image in 768x768x and for detection I use the full resolution image but the cfg file I used 2560x2560
@Tuumix thank you for your suggestion.
how you did the crop operation. is it cropping the object or resizing the full image to 768x768. can you share your .cfg file here. it will be more helpful.
@praneeth0609 I cropped the object to 768x768.
@Tuumix thank you for your replay. I want to know some details regarding the training of yolov4.
present I'm training custom data using yolov4.

this is how I'm getting the loss and mAP graph. can you clarify the bellow questions?
If I understand correctly, your training network size was 768x768, and you cropped your training images to that resolution.
During detection, your input image resolution is 4000x3000 and forwarded to detection network of 2560x2560.
Did you scale down the evaluation image size of 4000x3000 to network size of 2560x2560, or did you slide the evaluation image in the size of 2560x2560 (and probably 200px strive for overlaps) before forward to the network for detection?
increase network resolution in your .cfg-file (height=608, width=608 or any value multiple of 32) - it will increase precision
Do you think you would get better accuracy if your training size was higher than 768?
We have a similar use case to detect small objects (~200px) in a 4k image. We bumped the training size to 1056x1056 (the largest training resolution we can fit with 16GB GPU RAM), but the detection accuracy of small objects is not very, thus investigating what other parameters (training network size, training image size; detection network size, detection image size).
If we have to use two different GPU s 2060 and 2070, for OpenCV dnn or darkent , do we need to use separate versions of cuda and CUDNN...?
If we use two separate versions can I use them whenever I need or will the older version will be overwritten by the new version?
You can use same cuda and cudnn. + you can also install multiple cuda and cuDNN and just chanage ~/.bashrc to mention the cuda and cudnn version you are willing to use. Then version in the bashrc will be used.
On Sun, Sep 27, 2020 at 9:41 PM praneeth0609 ***@***.***> wrote: If we have to use two different GPU s 2060 and 2070, for OpenCV dnn or darkent , do we need to use separate versions of cuda and CUDNN...? If we use two separate versions can I use them whenever I need or will the older version will be overwritten by the new version? — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#2256 (comment)>, or unsubscribe .
@kangks can you share the cfg file for training with 1056x1056
@MuhammadAsadJaved currently I'm using intel i7 9th gen, Geforce 2060 6 GB GPU, Linux os, and am trying to detect the custom objects using yolov4, but am getting Low processing rate. can I upgrade to 2070 8 GB, if I can..? what are the advantages of 2070 over 2060. can you suggest the best GPU for Yolo v4 object detection with a decent frame rate ...?
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NNPACK was used to optimize Darknet without using a GPU. It is useful for embedded devices using ARM CPUs.
Idein's qmkl is also used to accelerate the SGEMM using the GPU. This is slower than NNPACK on NEON-capable devices, and primarily useful for ARM CPUs without NEON.
The NNPACK implementation in Darknet was improved to use transform-based convolution computation, allowing for 40%+ faster inference performance on non-initial frames. This is most useful for repeated inferences, ie. video, or if Darknet is left open to continue processing input instead of allowed to terminate after processing input.
Log in to Raspberry Pi using SSH.
Install PeachPy and confu
Install clang (I'm not sure why we need this, NNPACK doesn't use it unless you specifically target it).
If you are compiling for the Pi Zero, run python ./configure.py --backend scalar, otherwise run python ./configure.py --backend auto It's also recommended to examine and edit https://github.com/digitalbrain79/NNPACK-darknet/blob/master/src/init.c#L215 to match your CPU architecture if you're on ARM, as the cache size detection code only works on x86.
Since none of the ARM CPUs have a L3, it's recommended to set L3 = L2 and set inclusive=false. This should lead to the L2 size being set equal to the L3 size.
Ironically, after some trial and error, I've found that setting L3 to an arbitrary 2MB seems to work pretty well.
If the convolution-inference-smoketest fails, you've probably hit a compiler bug and will have to change to Clang or an older version of GCC. You can skip the qmkl/qasm/qbin2hex steps if you aren't targeting the QPU.
At this point, you can build darknet-nnpack using make. Be sure to edit the Makefile before compiling.
The weight files can be downloaded from the YOLO homepage.
All NNPACK=1 results use march=native, pthreadpool is initialized for one thread for the single core Pi Zero, and mcpu=cortex-a53 for the Pi 3.
For non-implicit-GEMM convolution computation, it is possible to precompute the kernel to accelerate subsequent inferences. The first inference is slower than later ones, but the speedup is significant (40%+). This optimization is a classic time-memory tradeoff; YOLOv2 won't fit in the Raspberry Pi 3's memory with this code.
1.1 (first frame), 0.73 (subsequent frames)
1.4 (first frame), 0.82 (subsequent frames)
1.7 (first frame), 0.77 (subsequent frames)
1.8 (first frame), 0.87 (subsequent frames)
5.3 (first frame), 2.7 (subsequent frames)
5.8 (first frame), 3.1 (subsequent frames)
0.27 (first frame), 0.17 (subsequent frames)
0.98 (first frame), 0.69 (subsequent frames)
Apparently cfg files have changed with yolov3 update, so benchmarks may be out of date, ie. classifier network input size. This has been updated for the classifier networks Darknet and Darknet19 only.
On the Intel chip, using transformed GEMM is always faster, even with precomputation on the first frame, than implicit-GEMM. On the Pi 3, implicit-GEMM is faster on the first frame. This suggests that memory bandwidth may be a limiting factor on the Pi 3.
I used these NNPACK cache tunings for the Pi 3:
Even though the Pi Zero's L2 is attached to the QPU and almost as slow as main memory, it does seem to have a small benefit.
On the Pi 3, the QPU is slower than NEON-NNPACK. qmkl is just unable to match the performance NNPACK's extremely well tuned NEON implicit GEMM.
On the Pi Zero, the QPU is faster than scalar-NNPACK. I have yet to investigate why enabling NNPACK gives a very slight speedup on the Pi Zero.
Using the QPU requires memory set aside for the GPU. Using the command sudo vcdbg reloc you can see how much memory is free on the GPU - it's roughly 20MB less than what is specified by gpu_mem.
I recommend no less than gpu_mem=80 if you want to run Tiny-YOLO/Darknet19/Darknet. The code I've used tries to keep GPU allocations to a minimum, but if Darknet crashes before GPU memory is freed, it will be gone until a reboot.

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