# YOLO3

**Repository Path**: shenmu/yolo3

## Basic Information

- **Project Name**: YOLO3
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-12-01
- **Last Updated**: 2021-11-03

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# TensorFlow-2.x-YOLOv3 and YOLOv4 tutorials

YOLOv3 and YOLOv4 implementation in TensorFlow 2.x, with support for training, transfer training, object tracking mAP and so on...
Code was tested with following specs:
- i7-7700k CPU and Nvidia 1080TI GPU
- OS Ubuntu 18.04
- CUDA 10.1
- cuDNN v7.6.5
- TensorRT-6.0.1.5
- Tensorflow-GPU 2.3.1
- Code was tested on Ubuntu and Windows 10 (TensorRT not supported officially)

## Installation
First, clone or download this GitHub repository.
Install requirements and download pretrained weights:
```
pip install -r ./requirements.txt

# yolov3
wget -P model_data https://pjreddie.com/media/files/yolov3.weights

# yolov3-tiny
wget -P model_data https://pjreddie.com/media/files/yolov3-tiny.weights

# yolov4
wget -P model_data https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights

# yolov4-tiny
wget -P model_data https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights
```

## Quick start
Start with using pretrained weights to test predictions on both image and video:
```
python detection_demo.py
```

<p align="center">
    <img width="100%" src="IMAGES/city_pred.jpg" style="max-width:100%;"></a>
</p>

## Quick training for custom mnist dataset
mnist folder contains mnist images, create training data:
```
python mnist/make_data.py
```
`./yolov3/configs.py` file is already configured for mnist training.

Now, you can train it and then evaluate your model
```
python train.py
tensorboard --logdir=log
```
Track training progress in Tensorboard and go to http://localhost:6006/:
<p align="center">
    <img width="100%" src="IMAGES/tensorboard.png" style="max-width:100%;"></a>
</p>

Test detection with `detect_mnist.py` script:
```
python detect_mnist.py
```
Results:
<p align="center">
    <img width="40%" src="IMAGES/mnist_test.jpg" style="max-width:40%;"></a>
</p>

## Custom YOLOv3 & YOLOv4 object detection training
Custom training required to prepare dataset first, how to prepare dataset and train custom model you can read in following link:<br>
https://pylessons.com/YOLOv3-TF2-custrom-train/<br>
More about YOLOv4 training you can read [on this link](https://pylessons.com/YOLOv4-TF2-training/). I didn’t have time to implement all YOLOv4 Bag-Of-Freebies to improve the training process… Maybe later I’ll find time to do that, but now I leave it as it is. I recommended to use [Alex's Darknet](https://github.com/AlexeyAB/darknet) to train your custom model, if you need maximum performance, otherwise, you can use my implementation.

## Google Colab Custom Yolo v3 training
To learn more about Google Colab Free gpu training, visit my [text version tutorial](https://pylessons.com/YOLOv3-TF2-GoogleColab/)

## Yolo v3 Tiny train and detection
To get detailed instructions how to use Yolov3-Tiny, follow my text version tutorial [YOLOv3-Tiny support](https://pylessons.com/YOLOv3-TF2-Tiny/). Short instructions:
- Get YOLOv3-Tiny weights: ```wget -P model_data https://pjreddie.com/media/files/yolov3-tiny.weights```
- From `yolov3/configs.py` change `TRAIN_YOLO_TINY` from `False` to `True`
- Run `detection_demo.py` script.

## Yolo v3 Object tracking
To learn more about Object tracking with Deep SORT, visit [Following link](https://pylessons.com/YOLOv3-TF2-DeepSort/).
Quick test:
- Clone this repository;
- Make sure object detection works for you;
- Run object_tracking.py script
<p align="center">
    <img src="IMAGES/tracking_results.gif"></a>
</p>

## YOLOv3 vs YOLOv4 comparison on 1080TI:

YOLO FPS on COCO 2017 Dataset:
| Detection    | 320x320 | 416x416 | 512x512 |
|--------------|---------|---------|---------|
| YoloV3 FPS   | 24.38   | 20.94   | 18.57   |
| YoloV4 FPS   | 22.15   | 18.69   | 16.50   |

TensorRT FPS on COCO 2017 Dataset:
| Detection       | 320x320 | 416x416 | 512x512 | 608x608 |
|-----------------|---------|---------|---------|---------|
| YoloV4 FP32 FPS | 31.23   | 27.30   | 22.63   | 18.17   |
| YoloV4 FP16 FPS | 30.33   | 25.44   | 21.94   | 17.99   |
| YoloV4 INT8 FPS | 85.18   | 62.02   | 47.50   | 37.32   |
| YoloV3 INT8 FPS | 84.65   | 52.72   | 38.22   | 28.75   |

mAP on COCO 2017 Dataset:
| Detection        | 320x320 | 416x416 | 512x512 |
|------------------|---------|---------|---------|
| YoloV3 mAP50     | 49.85   | 55.31   | 57.48   |         
| YoloV4 mAP50     | 48.58   | 56.92   | 61.71   |         

TensorRT mAP on COCO 2017 Dataset:
| Detection         | 320x320 | 416x416 | 512x512 | 608x608 |
|-------------------|---------|---------|---------|---------|
| YoloV4 FP32 mAP50 | 48.58   | 56.92   | 61.71   | 63.92   |
| YoloV4 FP16 mAP50 | 48.57   | 56.92   | 61.69   | 63.92   |
| YoloV4 INT8 mAP50 | 40.61   | 48.36   | 52.84   | 54.53   |
| YoloV3 INT8 mAP50 | 44.19   | 48.64   | 50.10   | 50.69   |

## Converting YOLO to TensorRT
I will give two examples, both will be for YOLOv4 model,quantize_mode=INT8 and model input size will be 608. Detailed tutorial is on this [link](https://pylessons.com/YOLOv4-TF2-TensorRT/).
### Default weights from COCO dataset:
- Download weights from links above;
- In `configs.py` script choose your `YOLO_TYPE`;
- In `configs.py` script set `YOLO_INPUT_SIZE = 608`;
- In `configs.py` script set `YOLO_FRAMEWORK = "trt"`;
- From main directory in terminal type `python tools/Convert_to_pb.py`;
- From main directory in terminal type `python tools/Convert_to_TRT.py`;
- In `configs.py` script set `YOLO_CUSTOM_WEIGHTS = f'checkpoints/{YOLO_TYPE}-trt-{YOLO_TRT_QUANTIZE_MODE}–{YOLO_INPUT_SIZE}'`;
- Now you can run `detection_demo.py`, best to test with `detect_video` function.

### Custom trained YOLO weights:
- Download weights from links above;
- In `configs.py` script choose your `YOLO_TYPE`;
- In `configs.py` script set `YOLO_INPUT_SIZE = 608`;
- Train custom YOLO model with instructions above;
- In `configs.py` script set `YOLO_CUSTOM_WEIGHTS = f"{YOLO_TYPE}_custom"`;
- In `configs.py` script make sure that  `TRAIN_CLASSES` is with your custom classes text file;
- From main directory in terminal type `python tools/Convert_to_pb.py`;
- From main directory in terminal type `python tools/Convert_to_TRT.py`;
- In `configs.py` script set `YOLO_FRAMEWORK = "trt"`;
- In `configs.py` script set `YOLO_CUSTOM_WEIGHTS = f'checkpoints/{YOLO_TYPE}-trt-{YOLO_TRT_QUANTIZE_MODE}–{YOLO_INPUT_SIZE}'`;
- Now you can run `detection_custom.py`, to test custom trained and converted TensorRT model.

What is done:
--------------------
- [x] Detection with original weights [Tutorial link](https://pylessons.com/YOLOv3-TF2-introduction/)
- [x] Mnist detection training [Tutorial link](https://pylessons.com/YOLOv3-TF2-mnist/)
- [x] Custom detection training [Tutorial link1](https://pylessons.com/YOLOv3-TF2-custrom-train/), [link2](https://pylessons.com/YOLOv3-TF2-custrom-images/)
- [x] Google Colab training [Tutorial link](https://pylessons.com/YOLOv3-TF2-GoogleColab/)
- [x] YOLOv3-Tiny support [Tutorial link](https://pylessons.com/YOLOv3-TF2-Tiny/)
- [X] Object tracking [Tutorial link](https://pylessons.com/YOLOv3-TF2-DeepSort/)
- [X] Mean Average Precision (mAP) [Tutorial link](https://pylessons.com/YOLOv3-TF2-mAP/)
- [X] Yolo v3 on Raspberry Pi [Tutorial link](https://pylessons.com/YOLOv3-TF2-RaspberryPi/)
- [X] YOLOv4 and YOLOv4-tiny detection [Tutorial link](https://pylessons.com/YOLOv4-TF2-introduction/)
- [X] YOLOv4 and YOLOv4-tiny detection training (Not fully) [Tutorial link](https://pylessons.com/YOLOv4-TF2-training/)
- [X] Convert to TensorRT model [Tutorial link](https://pylessons.com/YOLOv4-TF2-TensorRT/)
- [X] Add multiprocessing after detection (drawing bbox) [Tutorial link](https://pylessons.com/YOLOv4-TF2-multiprocessing/)
- [X] Generate YOLO Object Detection training data from its own results [Tutorial link](https://pylessons.com/YOLOv4-TF2-CreateXML/)
- [X] Counter-strike Global Offensive realtime YOLOv4 Object Detection aimbot [Tutorial link](https://pylessons.com/YOLOv4-TF2-CSGO-aimbot/)

To be continued... (not anytime soon)
--------------------
- [ ] Converting to TensorFlow Lite
- [ ] YOLO on Android (Leaving it for future, will need to convert everythin to java... not ready for this)
- [ ] Generating anchors
- [ ] YOLACT: Real-time Instance Segmentation
- [ ] Model pruning (Pruning is a technique in deep learning that aids in the development of smaller and more efficient neural networks. It's a model optimization technique that involves eliminating unnecessary values in the weight tensor.)