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https://github.com/thtrieu/darkflow

Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices
https://github.com/thtrieu/darkflow

convolutional-networks convolutional-neural-networks darknet deep-learning deep-neural-networks graph image-processing machine-learning mobile-development object-detection real-time tensorflow

Last synced: 22 days ago
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Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

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README 

        
## Intro



[![Build Status](https://travis-ci.org/thtrieu/darkflow.svg?branch=master)](https://travis-ci.org/thtrieu/darkflow) [![codecov](https://codecov.io/gh/thtrieu/darkflow/branch/master/graph/badge.svg)](https://codecov.io/gh/thtrieu/darkflow)



Real-time object detection and classification. Paper: [version 1](https://arxiv.org/pdf/1506.02640.pdf), [version 2](https://arxiv.org/pdf/1612.08242.pdf).



Read more about YOLO (in darknet) and download weight files [here](http://pjreddie.com/darknet/yolo/). In case the weight file cannot be found, I uploaded some of mine [here](https://drive.google.com/drive/folders/0B1tW_VtY7onidEwyQ2FtQVplWEU), which include `yolo-full` and `yolo-tiny` of v1.0, `tiny-yolo-v1.1` of v1.1 and `yolo`, `tiny-yolo-voc` of v2.



See demo below or see on [this imgur](http://i.imgur.com/EyZZKAA.gif)



  



## 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](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/android/src/org/tensorflow/demo/TensorFlowYoloDetector.java)



**I am looking for help:**

 - `help wanted` labels in issue track



## 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`; edit `labels.txt` as follows



```

tvmonitor

person

pottedplant

```



And that's it. `darkflow` will take care of the rest. You can also set darkflow to load from a custom labels file with the `--labels` flag (i.e. `--labels myOtherLabelsFile.txt`). This can be helpful when working with multiple models with different sets of output labels. When this flag is not set, darkflow will load from `labels.txt` by default (unless you are using one of the recognized `.cfg` files designed for the COCO or VOC dataset - then the labels file will be ignored and the COCO or VOC labels will be loaded).



## 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:



```python

...



[convolutional]

batch_normalize = 1

size = 3

stride = 1

pad = 1

activation = leaky



[maxpool]



[connected]

output = 4096

activation = linear



...

```



## Flowing the graph using `flow`



```bash

# Have a look at its options

flow --h

```



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



```bash

# 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/` are flowed through the net and predictions are put in `sample_img/out/`. We can always specify more parameters for such forward passes, such as detection threshold, batch size, images folder, etc.



```bash

# 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` folder by default. An example json array is shown below.

```bash

# 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:

```json

[{"label":"person", "confidence": 0.56, "topleft": {"x": 184, "y": 101}, "bottomright": {"x": 274, "y": 382}},

{"label": "dog", "confidence": 0.32, "topleft": {"x": 71, "y": 263}, "bottomright": {"x": 193, "y": 353}},

{"label": "horse", "confidence": 0.76, "topleft": {"x": 412, "y": 109}, "bottomright": {"x": 592,"y": 337}}]

```

 - 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 new 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:



```bash

# 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/`. 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`.



```bash

# 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/VOCtest_06-Nov-2007.tar

tar xf VOCtest_06-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.

    

    ```python

    ...



    [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.

    

    ```python

    ...



    [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:



```bash

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

```



For a demo that runs 100% on the GPU:



```bash

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` with keyword `camera`.



To save a video with predicted bounding box, add `--saveVideo` option.



## Using darkflow from another python application



Please note that `return_predict(img)` must take an `numpy.ndarray`. Your image must be loaded beforehand and passed to `return_predict(img)`. Passing the file path won't work.



Result from `return_predict(img)` will be a list of dictionaries representing each detected object's values in the same format as the JSON output listed above.



```python

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`)



```bash

## 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` file, a `.meta` file will also be generated alongside it. This `.meta` file is a JSON dump of everything in the `meta` dictionary that contains information nessecary for post-processing such as `anchors` and `labels`. This way, everything you need to make predictions from the graph and do post processing is contained in those two files - no need to have the `.cfg` or any labels file tagging along.



The created `.pb` file can be used to migrate the graph to mobile devices (JAVA / C++ / Objective-C++). The name of input tensor and output tensor are respectively `'input'` and `'output'`. For further usage of this protobuf file, please refer to the official documentation of `Tensorflow` on C++ API [_here_](https://www.tensorflow.org/versions/r0.9/api_docs/cc/index.html). To run it on, say, iOS application, simply add the file to Bundle Resources and update the path to this file inside source code.



Also, darkflow supports loading from a `.pb` and `.meta` file for generating predictions (instead of loading from a `.cfg` and checkpoint or `.weights`).

```bash

## Forward images in sample_img for predictions based on protobuf file

flow --pbLoad built_graph/yolo.pb --metaLoad built_graph/yolo.meta --imgdir sample_img/

```

If you'd like to load a `.pb` and `.meta` file when using `return_predict()` you can set the `"pbLoad"` and `"metaLoad"` options in place of the `"model"` and `"load"` options you would normally set.



That's all.