Intro

You can see the original implementation and tutorial for darkflow here

See demo below or see on this imgur

Dependencies

Python3, tensorflow 1.0, numpy, opencv 3.

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 .
   

Run the model 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 yolov2-voc.weights
flow --model cfg/yolov2-voc-1c.cfg --load bin/yolov2-voc.weights

# 2. To completely initialize a model, leave the --load option
flow --model cfg/yolov2-voc-1c.cfg

# 3. It is useful to reuse the first identical layers of tiny for `yolo-new`
flow --model cfg/yolov2-voc-1c.cfg --load bin/yolov2-voc.weights
# this will print out which layers are reused, which are initialized

All input images from default folder test_cards/ are flowed through the net and predictions are put in test_cards/out/. We can always specify more parameters for such forward passes, such as detection threshold, batch size, images folder, etc.

# Forward all images in test_cards/ using yolo and 100% GPU usage
flow --imgdir test_cards/ --model cfg/yolov2-voc-1c.cfg --load bin/yolov2-voc.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 folder by default. An example json array is shown below.

# Forward all images in test_cards/ using yolo and JSON output.
flow --imgdir test_cards/ --model cfg/yolov2-voc-1c.cfg --load bin/yolov2-voc.weights --json

JSON output:

[{"label": "ace", "confidence": 0.32, "topleft": {"x": 46, "y": 114}, "bottomright": {"x": 208, "y": 227}}, 
{"label": "king", "confidence": 0.26, "topleft": {"x": 303, "y": 182}, "bottomright": {"x": 369, "y": 316}}, 
{"label": "queen", "confidence": 0.49, "topleft": {"x": 182, "y": 351}, "bottomright": {"x": 349, "y": 444}}, 
{"label": "nine", "confidence": 0.27, "topleft": {"x": 48, "y": 0}, "bottomright": {"x": 169, "y": 116}}, 
{"label": "ten", "confidence": 0.39, "topleft": {"x": 62, "y": 304}, "bottomright": {"x": 191, "y": 420}}]

• label: self explanatory
• confidence: somewhere between 0 and 1 (how confident yolo is about that detection)
• topleft: pixel coordinate of top left corner of box.
• bottomright: pixel coordinate of bottom right corner of box.

Training the model

Training is simple as you only have to add option --train. Training set and annotation will be parsed if this is the first time a new configuration is trained. To point to training set and annotations, use option --dataset and --annotation. A few examples:

# Initialize yolo from yolov2-voc, then train the net on 100% GPU:
flow --model cfg/yolov2-voc-1c.cfg --load bin/yolov2-voc.weights --train --gpu 1.0

# Completely initialize yolo and train it with ADAM optimizer
flow --model cfg/yolov2-voc-1c.cfg --train --trainer adam

During training, the script will occasionally save intermediate results into Tensorflow checkpoints, stored in ckpt/. To resume to any checkpoint before performing training/testing, use --load [checkpoint_num] option, if checkpoint_num < 0, darkflow will load the most recent save by parsing ckpt/checkpoint.

# Resume the most recent checkpoint for training
flow --train --model cfg/yolov2-voc-1c.cfg --load -1

# Test with checkpoint at step 1500
flow --model cfg/yolov2-voc-1c.cfg --load 1500

# Fine tuning yolo-tiny from the original one
flow --train --model cfg/yolov2-voc-1c.cfg --load bin/yolov2-voc.weights

Training this model 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 yolov2-voc.cfg and rename it according to your preference yolov2-voc-1c.cfg (It is crucial that you leave the original yolov2-voc.cfg file unchanged, see below for explanation).

2. In yolov2-voc-1c.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 yolov2-voc-1c.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 6 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 yolov2-voc-1c.cfg model when you train.

   flow --model cfg/yolov2-voc-1c.cfg --load bin/yolov2-voc.weights --train --annotation train/annotations --dataset train/images

Save the built graph to a protobuf file (.pb)

## Saving the lastest checkpoint to protobuf file
flow --model cfg/yolov2-voc-1c.cfg --load -1 --savepb

## Saving graph and weights to protobuf file
flow --model cfg/yolov2-voc-1c.cfg --load bin/yolov2-voc.weights --savepb

That's all.