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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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Darknet classification #1974

Open dexception opened 5 years ago

dexception commented 5 years ago

Hi @AlexeyAB ,

I want to do just classification of images using darknet. I have looked everywhere but could find the steps. Can you please help me in this regard ..

Thanks

Jacob-Stevens-Haas commented 5 years ago

@dexception did you try the section https://github.com/AlexeyAB/darknet#how-to-train-to-detect-your-custom-objects? Yes, there's stuff to figure out, but it's mostly worked for me and if you run into a question, I'll try to answer it.

vinjn commented 5 years ago

I've prepared starter template for image classification in https://github.com/jing-vision/lightnet/tree/master/__template-darknet19_448

dexception commented 5 years ago

@Jacob-Stevens-Haas I am not talking about object detection.

This is simple and pure image classification without any coordinates.

vdalv commented 5 years ago

There is info on this, on pjreddie's site.

ImageNet Classification Train a Classifier on CIFAR-10

deimsdeutsch commented 5 years ago

For those that are just doing classification this is the command:

./darknet classifier train cfg/your.data cfg/your.cfg

@AlexeyAB

  1. Can you tell me what would be the format of the .txt file for each image ?
  2. I am trying to do classification using resnet18.cfg for 4 categories. Can you suggest what changes in need to make according the number of categories in the cfg file ?
  3. How can i invoke the same via python wrapper ?
deimsdeutsch commented 5 years ago

@AlexeyAB I have been stuck on this issue for a long time. Can you plz reply. Thanks.

AlexeyAB commented 5 years ago

@deimsdeutsch

  1. Can you tell me what would be the format of the .txt file for each image ?

There shouldn't be txt-file for each image. Each jpg-file should contain label in its filename.

You should set here 1000 for 1000 classes: https://github.com/AlexeyAB/darknet/blob/3e2ef94130a8b4cf8c4b8d3c7a6f345d92531f65/cfg/darknet53.cfg#L558

You should have cfg/obj.data file

classes=1000
train  = train.txt
valid  = valid.txt
backup = backup
labels = labels.list
names  = shortnames.list
top=5
deimsdeutsch commented 5 years ago

@AlexeyAB Thanks for replying.

I am assuming from your reply that right now there is not way to fetch the classification categories in python3.

root@dimsdeutsch-GL553VE:/opt/project1/darknet# ./darknet classifier predict cfg/imagenet1k.data cfg/darknet19.cfg darknet19.weights data/dog.jpg layer filters size input output 0 conv 32 3 x 3 / 1 224 x 224 x 3 -> 224 x 224 x 32 0.087 BF 1 max 2 x 2 / 2 224 x 224 x 32 -> 112 x 112 x 32 0.002 BF 2 conv 64 3 x 3 / 1 112 x 112 x 32 -> 112 x 112 x 64 0.462 BF 3 max 2 x 2 / 2 112 x 112 x 64 -> 56 x 56 x 64 0.001 BF 4 conv 128 3 x 3 / 1 56 x 56 x 64 -> 56 x 56 x 128 0.462 BF 5 conv 64 1 x 1 / 1 56 x 56 x 128 -> 56 x 56 x 64 0.051 BF 6 conv 128 3 x 3 / 1 56 x 56 x 64 -> 56 x 56 x 128 0.462 BF 7 max 2 x 2 / 2 56 x 56 x 128 -> 28 x 28 x 128 0.000 BF 8 conv 256 3 x 3 / 1 28 x 28 x 128 -> 28 x 28 x 256 0.462 BF 9 conv 128 1 x 1 / 1 28 x 28 x 256 -> 28 x 28 x 128 0.051 BF 10 conv 256 3 x 3 / 1 28 x 28 x 128 -> 28 x 28 x 256 0.462 BF 11 max 2 x 2 / 2 28 x 28 x 256 -> 14 x 14 x 256 0.000 BF 12 conv 512 3 x 3 / 1 14 x 14 x 256 -> 14 x 14 x 512 0.462 BF 13 conv 256 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 256 0.051 BF 14 conv 512 3 x 3 / 1 14 x 14 x 256 -> 14 x 14 x 512 0.462 BF 15 conv 256 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 256 0.051 BF 16 conv 512 3 x 3 / 1 14 x 14 x 256 -> 14 x 14 x 512 0.462 BF 17 max 2 x 2 / 2 14 x 14 x 512 -> 7 x 7 x 512 0.000 BF 18 conv 1024 3 x 3 / 1 7 x 7 x 512 -> 7 x 7 x1024 0.462 BF 19 conv 512 1 x 1 / 1 7 x 7 x1024 -> 7 x 7 x 512 0.051 BF 20 conv 1024 3 x 3 / 1 7 x 7 x 512 -> 7 x 7 x1024 0.462 BF 21 conv 512 1 x 1 / 1 7 x 7 x1024 -> 7 x 7 x 512 0.051 BF 22 conv 1024 3 x 3 / 1 7 x 7 x 512 -> 7 x 7 x1024 0.462 BF 23 conv 1000 1 x 1 / 1 7 x 7 x1024 -> 7 x 7 x1000 0.100 BF 24 avg 7 x 7 x1000 -> 1000 25 softmax 1000 26 cost 1000 Total BFLOPS 5.585 Allocate additional workspace_size = 52.43 MB Loading weights from darknet19.weights... seen 32 Done!

try to allocate additional workspace_size = 52.43 MB CUDA allocate done! 224 224 data/dog.jpg: Predicted in 0.001283 seconds. Eskimo dog: 0.277704 malamute: 0.153123 Siberian husky: 0.138571 German shepherd: 0.046391 miniature schnauzer: 0.038783

But not getting the same output from darknet.py file.

root@dimsdeutsch-GL553VE:/opt/project1/darknet# python3 darknet.py Try to load cfg: ./cfg/darknet19.cfg, weights: darknet19.weights, clear = 0 layer filters size input output 0 conv 32 3 x 3 / 1 224 x 224 x 3 -> 224 x 224 x 32 0.087 BF 1 max 2 x 2 / 2 224 x 224 x 32 -> 112 x 112 x 32 0.002 BF 2 conv 64 3 x 3 / 1 112 x 112 x 32 -> 112 x 112 x 64 0.462 BF 3 max 2 x 2 / 2 112 x 112 x 64 -> 56 x 56 x 64 0.001 BF 4 conv 128 3 x 3 / 1 56 x 56 x 64 -> 56 x 56 x 128 0.462 BF 5 conv 64 1 x 1 / 1 56 x 56 x 128 -> 56 x 56 x 64 0.051 BF 6 conv 128 3 x 3 / 1 56 x 56 x 64 -> 56 x 56 x 128 0.462 BF 7 max 2 x 2 / 2 56 x 56 x 128 -> 28 x 28 x 128 0.000 BF 8 conv 256 3 x 3 / 1 28 x 28 x 128 -> 28 x 28 x 256 0.462 BF 9 conv 128 1 x 1 / 1 28 x 28 x 256 -> 28 x 28 x 128 0.051 BF 10 conv 256 3 x 3 / 1 28 x 28 x 128 -> 28 x 28 x 256 0.462 BF 11 max 2 x 2 / 2 28 x 28 x 256 -> 14 x 14 x 256 0.000 BF 12 conv 512 3 x 3 / 1 14 x 14 x 256 -> 14 x 14 x 512 0.462 BF 13 conv 256 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 256 0.051 BF 14 conv 512 3 x 3 / 1 14 x 14 x 256 -> 14 x 14 x 512 0.462 BF 15 conv 256 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 256 0.051 BF 16 conv 512 3 x 3 / 1 14 x 14 x 256 -> 14 x 14 x 512 0.462 BF 17 max 2 x 2 / 2 14 x 14 x 512 -> 7 x 7 x 512 0.000 BF 18 conv 1024 3 x 3 / 1 7 x 7 x 512 -> 7 x 7 x1024 0.462 BF 19 conv 512 1 x 1 / 1 7 x 7 x1024 -> 7 x 7 x 512 0.051 BF 20 conv 1024 3 x 3 / 1 7 x 7 x 512 -> 7 x 7 x1024 0.462 BF 21 conv 512 1 x 1 / 1 7 x 7 x1024 -> 7 x 7 x 512 0.051 BF 22 conv 1024 3 x 3 / 1 7 x 7 x 512 -> 7 x 7 x1024 0.462 BF 23 conv 1000 1 x 1 / 1 7 x 7 x1024 -> 7 x 7 x1000 0.100 BF 24 avg 7 x 7 x1000 -> 1000 25 softmax 1000 26 cost 1000 Total BFLOPS 5.585 Allocate additional workspace_size = 52.43 MB Loading weights from darknet19.weights... seen 32 Done! Loaded - names_list: data/imagenet.shortnames.list, classes = 1000 []

AlexeyAB commented 5 years ago

@deimsdeutsch Yes, darknet.py is just a simple example for Yolo detection.

deimsdeutsch commented 5 years ago

@AlexeyAB I am looking forward for the python wrapper for classification. It would be a good addition to the project.

wting861006 commented 5 years ago

@deimsdeutsch

  1. Can you tell me what would be the format of the .txt file for each image ?

There shouldn't be txt-file for each image. Each jpg-file should contain label in its filename.

You should set here 1000 for 1000 classes:

https://github.com/AlexeyAB/darknet/blob/3e2ef94130a8b4cf8c4b8d3c7a6f345d92531f65/cfg/darknet53.cfg#L558

You should have cfg/obj.data file

classes=1000
train  = train.txt
valid  = valid.txt
backup = backup
labels = labels.list
names  = shortnames.list
top=5
  • classes - number of classes, for example = 1000
  • labels.list - file with list of 1000 labels (for example: n1, n2, n3, ... n1000)
  • train.txt - file with list of jpg-files for training, each name should contain one of label, for example image_n1.jpg, img.n2.jpg, n3.jpg, ...
  • shortnames.list - file with list of 1000 names of classes car, dog
  • top=5 - what accuracy will be checked top1, top5, topN....

  • valid.list - file with list of jpg-files for validation

    • or during detection darknet.exe classifier train cfg/obj.data cfg/darknet53.cfg -topk
    • or separate validation darknet.exe classifier valid cfg/obj.data cfg/darknet53.cfg backup/darknet53_last.weights

@AlexeyAB Hi,I have 4000 classes dataset.Can I train my dataset with this method?