• Transfer learning
• YoloV4 configuration
• YoloV4 training
• YoloV4 loss function adjustments.
• Live plot losses
• Command line options
• YoloV3 tiny
• Rasberry Pi support

• Getting Started

  • Installation

• Description

• Features

  • Command line options
    • General
    • Training
    • Detection
  • Conversion from DarkNet .cfg files to keras models
  • Support for all yolo versions
  • Tensorflow-2.x
  • Random weights and DarkNet weights support
  • Multiple input options
    • .csv
    • .xml
    • .tfrecord
  • Anchor generation
  • Visualization of training stages
  • mAP evaluation in pandas
  • labelpix support
  • Photo & video detection

• Usage

  • Training
    • Code example
    • Command line example
  • Detection
    • Code example
    • Command line example
  • Evaluation

• Contributing

• Issues

  • What will be addressed
  • What will not be adressed

• License

• Show your support

pip install git+https://github.com/unsignedrant/yolo-tf2

Verify installation

% yolotf2
Yolo-tf2 1.0

Usage:
	yolotf2 <command> [options] [args]

Available commands:
	train      Create new or use existing dataset and train a model
	detect     Detect a folder of images or a video

Use yolotf2 <command> -h to see more info about a command

Use yolotf2 -h to display all command line options

yolo-tf2 was initially an implementation of yolov3 (you only look once)(training & inference) and support for all yolo versions was added in db2f889. Yolo is a state-of-the-art, real-time object detection system that is extremely fast and accurate. The official repo is here. There are many implementations that support tensorflow, only a few that support tensorflow v2 and as I did not find versions that suit my needs so, I decided to create this version which is very flexible and customizable. It requires python 3.10+, is not platform specific and is MIT licensed.

This feature was introduced to replace the old hard-coded model. Models are loaded directly from DarkNet .cfg files for convenience.

As of db2f889 DarkNet .cfg files are automatically converted to keras models.

The current code leverages features that were introduced in tensorflow 2.x including keras models, tfrecord datasets, etc...

Both options are available, and Note in case of using DarkNet weights you must maintain the same number of COCO classes (80 classes) as transfer learning to models with different classes is not currently supported.

There are 3 input options accepted by the api:

A .csv file similar to the one below is supported. Note that x0, y0, x1, y1 are x and y coordinates relative to their corresponding image width and height. For example:

image width = 1000
image height = 500 
x0, y0 = 100, 300
x1, y1 = 120, 320
x0, y0, x1, y1 = 0.1, 0.6, 0.12, 0.64 respectively. 

<annotation>
	<folder>VOC2012</folder>
	<filename>2007_000033.jpg</filename>
	<source>
		<database>The VOC2007 Database</database>
		<annotation>PASCAL VOC2007</annotation>
		<image>flickr</image>
	</source>
	<size>
		<width>500</width>
		<height>366</height>
		<depth>3</depth>
	</size>
	<segmented>1</segmented>
	<object>
		<name>aeroplane</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>9</xmin>
			<ymin>107</ymin>
			<xmax>499</xmax>
			<ymax>263</ymax>
		</bndbox>
	</object>
	<object>
		<name>aeroplane</name>
		<pose>Left</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>421</xmin>
			<ymin>200</ymin>
			<xmax>482</xmax>
			<ymax>226</ymax>
		</bndbox>
	</object>
	<object>
		<name>aeroplane</name>
		<pose>Left</pose>
		<truncated>1</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>325</xmin>
			<ymin>188</ymin>
			<xmax>411</xmax>
			<ymax>223</ymax>
		</bndbox>
	</object>
</annotation>

.tfrecord files previously generated by the code can be reused. A typical feature map looks like:

{
    'image': tf.io.FixedLenFeature([], tf.string),
    'x0': tf.io.VarLenFeature(tf.float32),
    'y0': tf.io.VarLenFeature(tf.float32),
    'x1': tf.io.VarLenFeature(tf.float32),
    'y1': tf.io.VarLenFeature(tf.float32),
    'object_name': tf.io.VarLenFeature(tf.string),
    'object_index': tf.io.VarLenFeature(tf.int64),
}

A k-means algorithm finds the optimal sizes and generates anchors with process visualization.

Including:

• k-means visualization:

• Generated anchors:

• Precision and recall curves:

• Evaluation bar charts:

• Actual vs. detections:

You can always visualize different stages of the program using my other repo labelpix which is a tool for drawing bounding boxes, but can also be used to visualize bounding boxes over images using csv files in the format mentioned here.

Evaluation is available through the detection api which supports mAP score calculation. A typical evaluation result looks like:

You can check my other repo labelpix which is a labeling tool that you can use produce small datasets for experimentation. It supports .csv files in the format mentioned here and/or .xml files as here

Detections can be performed on photos or videos using the detection api.

The following files are expected:

• Object classes .txt file.

  person
  bicycle
  car
  motorbike
  aeroplane
  bus
  train
  truck
  boat
  traffic light
  fire hydrant
  

• DarkNet model .cfg file

• Anchors .txt file

  10,13
  16,30
  33,23
  30,61
  62,45
  59,119
  116,90
  156,198
  373,326
  

• Masks .txt file

  6,7,8
  3,4,5
  0,1,2
  

• Labeled examples, ONE of:

  • .csv file as shown here
  • .xml labels + image folder as shown here
  • Training + Validation .tfrecord files, having a feature map as shown here

Training is available through yolo_tf2.train api. For more info about other parameters, check the docstrings, available through help()

import yolo_tf2

yolo_tf2.train(
    input_shape=(608, 608, 3),
    classes='/path/to/classes.txt',
    model_cfg='/path/to/darknet/file.cfg',
    anchors='/path/to/anchors.txt',
    masks='/path/to/masks.txt',
    labeled_examples='/path/to/labeled_examples.csv',
    output_dir='/path/to/training-output-dir'
)

yolotf2 train --input-shape 608 608 3 --classes /path/to/classes.txt --model-cfg /path/to/darknet/file.cfg --anchors /path/to/anchors.txt --masks /path/to/masks.txt --labeled-examples /path/to/labeled_examples.csv --output-dir /path/to/training-output-dir  

The following files are expected:

• Object classes .txt file.

  person
  bicycle
  car
  motorbike
  aeroplane
  bus
  train
  truck
  boat
  traffic light
  fire hydrant
  

• DarkNet model .cfg file

• Anchors .txt file

  10,13
  16,30
  33,23
  30,61
  62,45
  59,119
  116,90
  156,198
  373,326
  

• Masks .txt file

  6,7,8
  3,4,5
  0,1,2
  

• Trained .tf or .weights file

• Whatever is to detect: any of:

  • A list of image paths
  • Image dir
  • Video

Note: For yolov4 configuration, v4=True or --v4 should be specified

Detection is available through yolo_tf2.detect api. For more info about other parameters, check the docstrings, available through help()

import yolo_tf2

yolo_tf2.detect(
    input_shape=(608, 608, 3),
    classes='/path/to/classes.txt',
    anchors='/path/to/anchors.txt',
    masks='/path/to/masks.txt',
    model_cfg='/path/to/darknet/file.cfg',
    weights='/path/to/trained_weights.tf',
    images=['/path/to/image1', '/path/to/image2', ...],
    output_dir='detection-output'
)

yolotf2 detect --input-shape 608 608 3 --classes /path/to/classes.txt --model-cfg /path/to/darknet/file.cfg --anchors /path/to/anchors.txt --masks /path/to/masks.txt --weights /path/to/trained_weights.tf --images /path/to/image1 /path/to/image2 --output-dir /path/to/detection-output-dir 

Notes:

• To detect video, video or --video needs to be passed instead
• For yolov4 configuration, v4=True or --v4 should be specified**

Evaluation is available through the very same detection api described in the previous section. The only difference is an additional parameter evaluation_examples or --evaluation-examples for command line which is a .csv file containing the actual labels of the images being detected. The names of the images passed will be looked for in the actual labels, and if any of the filenames were not found, an error is raised, which means:

if you do:

import yolo_tf2

yolo_tf2.detect(
    input_shape=(608, 608, 3),
    classes='/path/to/classes.txt',
    anchors='/path/to/anchors.txt',
    masks='/path/to/masks.txt',
    model_cfg='/path/to/darknet/file.cfg',
    weights='/path/to/trained_weights.tf',
    images=['/path/to/image1', '/path/to/image2', ...],
    output_dir='detection-output',
    evaluation_examples='/path/to/actual/examples'
)

evaluation_examples .csv file should look like:

Because images=['/path/to/image1', '/path/to/image2', ...] were passed, their actual labels must be provided. Same thing applies to the images contained in a directory if image_dir was passed instead.

Contributions are what make the open source community such an amazing place to
learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

There are relevant cases in which the issues will be addressed and irrelevant ones that will be closed.

The following issues will be addressed.

• Bugs.
• Performance issues.
• Installation issues.
• Documentation issues.
• Feature requests.
• Dependency issues that can be solved.

The following issues will not be addressed and will be closed.

• Issues without context / clear and concise explanation.
• Issues without standalone code (minimum reproducible example), or a jupyter notebook link to reproduce errors.
• Issues that are improperly formatted.
• Issues that are dataset / label specific without a dataset sample link.
• Issues that are the result of doing something that is unsupported by the existing features.
• Issues that are not considered as improvement / useful.

Distributed under the MIT License. See LICENSE for more information.

Give a ⭐️ if this project helped you!