https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
YOLOv3 implementation in TensorFlow 2.3.1
https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
darknet detection pretrained-weights tensorflow2 tensorrt tracking yolo yolov3 yolov4 yolov4-training
Last synced: about 2 months ago
JSON representation
YOLOv3 implementation in TensorFlow 2.3.1
- Host: GitHub
- URL: https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
- Owner: pythonlessons
- License: mit
- Created: 2020-04-21T18:29:55.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2024-05-03T19:54:25.000Z (about 1 year ago)
- Last Synced: 2025-04-12T15:56:53.876Z (2 months ago)
- Topics: darknet, detection, pretrained-weights, tensorflow2, tensorrt, tracking, yolo, yolov3, yolov4, yolov4-training
- Language: Jupyter Notebook
- Homepage: https://pylessons.com/
- Size: 69.1 MB
- Stars: 605
- Watchers: 18
- Forks: 329
- Open Issues: 47
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-yolo-object-detection - pythonlessons/TensorFlow-2.x-YOLOv3 - 2.x-YOLOv3?style=social"/> : YOLOv3 implementation in TensorFlow 2.3.1. (Other Versions of YOLO)
- awesome-yolo-object-detection - pythonlessons/TensorFlow-2.x-YOLOv3 - 2.x-YOLOv3?style=social"/> : YOLOv3 implementation in TensorFlow 2.3.1. (Other Versions of YOLO)
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
```
## 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/:
Test detection with `detect_mnist.py` script:
```
python detect_mnist.py
```
Results:
## 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:
https://pylessons.com/YOLOv3-TF2-custrom-train/
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
## 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.)