Intro

Real-time object detection and classification. Paper: version 1, version 2.

Read more about YOLO (in darknet) and download weight files here. In case the weight file cannot be found, I uploaded some of mine here, which include

yolo-full

yolo-tiny

tiny-yolo-v1.1

yolo

tiny-yolo-voc

See demo below or see on this imgur

Dependencies

Python3, tensorflow 1.0, numpy, opencv 3.

Citation

@article{trieu2018darkflow,
  title={Darkflow},
  author={Trieu, Trinh Hoang},
  journal={GitHub Repository. Available online: https://github. com/thtrieu/darkflow (accessed on 14 February 2019)},
  year={2018}
}

Getting started

You can choose one of the following three ways to get started with darkflow.

1. Just build the Cython extensions in place. NOTE: If installing this way you will have to use

   ./flow

   in the cloned darkflow directory instead of

   flow

   as darkflow is not installed globally.

   python3 setup.py build_ext --inplace
   

2. Let pip install darkflow globally in dev mode (still globally accessible, but changes to the code immediately take effect)

   pip install -e .
   

3. Install with pip globally

   pip install .
   

Update

Android demo on Tensorflow's here

I am looking for help: -

help wanted

Parsing the annotations

Skip this if you are not training or fine-tuning anything (you simply want to forward flow a trained net)

For example, if you want to work with only 3 classes

tvmonitor

person

pottedplant

labels.txt

tvmonitor
person
pottedplant

And that's it.

darkflow

--labels

--labels myOtherLabelsFile.txt

labels.txt

.cfg

Design the net

Skip this if you are working with one of the original configurations since they are already there. Otherwise, see the following example:

...

[convolutional]
batch_normalize = 1
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
[connected]
output = 4096
activation = linear
...

Flowing the graph using

flow

# Have a look at its options
flow --h

First, let's take a closer look at one of a very useful option

--load

# 1. Load tiny-yolo.weights
flow --model cfg/tiny-yolo.cfg --load bin/tiny-yolo.weights

2. To completely initialize a model, leave the --load option
flow --model cfg/yolo-new.cfg
3. It is useful to reuse the first identical layers of tiny for yolo-new
flow --model cfg/yolo-new.cfg --load bin/tiny-yolo.weights
this will print out which layers are reused, which are initialized

All input images from default folder

sample_img/

sample_img/out/

# Forward all images in sample_img/ using tiny yolo and 100% GPU usage
flow --imgdir sample_img/ --model cfg/tiny-yolo.cfg --load bin/tiny-yolo.weights --gpu 1.0

json output can be generated with descriptions of the pixel location of each bounding box and the pixel location. Each prediction is stored in the

sample_img/out

Forward all images in sample_img/ using tiny yolo and JSON output.

flow --imgdir sample_img/ --model cfg/tiny-yolo.cfg --load bin/tiny-yolo.weights --json

JSON output:

Training new model

Training is simple as you only have to add option

--train

--dataset

--annotation

# Initialize yolo-new from yolo-tiny, then train the net on 100% GPU:
flow --model cfg/yolo-new.cfg --load bin/tiny-yolo.weights --train --gpu 1.0

Completely initialize yolo-new and train it with ADAM optimizer
flow --model cfg/yolo-new.cfg --train --trainer adam

During training, the script will occasionally save intermediate results into Tensorflow checkpoints, stored in

ckpt/

--load [checkpoint_num]

checkpoint_num < 0

darkflow

ckpt/checkpoint

# Resume the most recent checkpoint for training
flow --train --model cfg/yolo-new.cfg --load -1

Test with checkpoint at step 1500
flow --model cfg/yolo-new.cfg --load 1500
Fine tuning yolo-tiny from the original one
flow --train --model cfg/tiny-yolo.cfg --load bin/tiny-yolo.weights

Example of training on Pascal VOC 2007: ```bash

Download the Pascal VOC dataset:

curl -O https://pjreddie.com/media/files/VOCtest06-Nov-2007.tar tar xf VOCtest06-Nov-2007.tar

An example of the Pascal VOC annotation format:

vim VOCdevkit/VOC2007/Annotations/000001.xml

Train the net on the Pascal dataset:

flow --model cfg/yolo-new.cfg --train --dataset "~/VOCdevkit/VOC2007/JPEGImages" --annotation "~/VOCdevkit/VOC2007/Annotations" ```

Training on your own dataset

The steps below assume we want to use tiny YOLO and our dataset has 3 classes

1. Create a copy of the configuration file

   tiny-yolo-voc.cfg

   and rename it according to your preference

   tiny-yolo-voc-3c.cfg

   (It is crucial that you leave the original

   tiny-yolo-voc.cfg

   file unchanged, see below for explanation).

2. In

   tiny-yolo-voc-3c.cfg

   , change classes in the [region] layer (the last layer) to the number of classes you are going to train for. In our case, classes are set to 3.

   ...
   
   [region]
   anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
   bias_match=1
   classes=3
   coords=4
   num=5
   softmax=1
   ...
   

3. In

   tiny-yolo-voc-3c.cfg

   , change filters in the [convolutional] layer (the second to last layer) to num * (classes + 5). In our case, num is 5 and classes are 3 so 5 * (3 + 5) = 40 therefore filters are set to 40.

   ...
   
   [convolutional]
   size=1
   stride=1
   pad=1
   filters=40
   activation=linear
   [region]
   anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
   ...
   

4. Change

   labels.txt

   to include the label(s) you want to train on (number of labels should be the same as the number of classes you set in

   tiny-yolo-voc-3c.cfg

   file). In our case,

   labels.txt

   will contain 3 labels.

   label1
   label2
   label3
   

5. Reference the

   tiny-yolo-voc-3c.cfg

   model when you train.

   flow --model cfg/tiny-yolo-voc-3c.cfg --load bin/tiny-yolo-voc.weights --train --annotation train/Annotations --dataset train/Images

• Why should I leave the original

  tiny-yolo-voc.cfg

  file unchanged?

  When darkflow sees you are loading

  tiny-yolo-voc.weights

  it will look for

  tiny-yolo-voc.cfg

  in your cfg/ folder and compare that configuration file to the new one you have set with

  --model cfg/tiny-yolo-voc-3c.cfg

  . In this case, every layer will have the same exact number of weights except for the last two, so it will load the weights into all layers up to the last two because they now contain different number of weights.

Camera/video file demo

For a demo that entirely runs on the CPU:

flow --model cfg/yolo-new.cfg --load bin/yolo-new.weights --demo videofile.avi

For a demo that runs 100% on the GPU:

flow --model cfg/yolo-new.cfg --load bin/yolo-new.weights --demo videofile.avi --gpu 1.0

To use your webcam/camera, simply replace

videofile.avi

camera

To save a video with predicted bounding box, add

--saveVideo

Using darkflow from another python application

Please note that

return_predict(img)

numpy.ndarray

return_predict(img)

Result from

return_predict(img)

from darkflow.net.build import TFNet
import cv2

options = {"model": "cfg/yolo.cfg", "load": "bin/yolo.weights", "threshold": 0.1}
tfnet = TFNet(options)
imgcv = cv2.imread("./sample_img/sample_dog.jpg")
result = tfnet.return_predict(imgcv)
print(result)

Save the built graph to a protobuf file (

.pb

)

## Saving the lastest checkpoint to protobuf file
flow --model cfg/yolo-new.cfg --load -1 --savepb

Saving graph and weights to protobuf file
flow --model cfg/yolo.cfg --load bin/yolo.weights --savepb

When saving the

.pb

.meta

.meta

meta

anchors

labels

.cfg

The created

.pb

'input'

'output'

Tensorflow

Also, darkflow supports loading from a

.pb

.meta

.cfg

.weights

Forward images in sample_img for predictions based on protobuf file

flow --pbLoad builtgraph/yolo.pb --metaLoad builtgraph/yolo.meta --imgdir sampleimg/ ``

If you'd like to load a

and

file when using

you can set the

and

options in place of the

and

That's all.