https://github.com/bm777/object_detection
Implementatoin of object detection using Tensorflow 2.1.0 | this can be use in a car for object detection
https://github.com/bm777/object_detection
absl darknet detection graph-mode object-detection opencv python tensorflow transfer-learning yolov3
Last synced: about 1 year ago
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Implementatoin of object detection using Tensorflow 2.1.0 | this can be use in a car for object detection
- Host: GitHub
- URL: https://github.com/bm777/object_detection
- Owner: bm777
- License: apache-2.0
- Created: 2020-05-25T18:46:55.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2022-04-05T08:27:49.000Z (about 4 years ago)
- Last Synced: 2025-03-18T18:06:31.238Z (over 1 year ago)
- Topics: absl, darknet, detection, graph-mode, object-detection, opencv, python, tensorflow, transfer-learning, yolov3
- Language: Python
- Homepage:
- Size: 331 KB
- Stars: 13
- Watchers: 3
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Average Time Elapsed for process on each frame: 71.6ms
# YoloV3 Implementation in Tensorflow 2.x
See on YouTube: [4K YoloV3 Object detection using Tensorflow 2.1](https://www.youtube.com/watch?v=2Xc9oj2qMRc)
This repo provide a clean implementation of YoloV3 in Tensorflow 2.x using all the best practices.
## Key Features
- [x] TensorFlow 2.1
- [x] `yolov3` with pre-trained Weights
- [ ] `yolov3-tiny` with pre-trained Weights
- [x] Inference example
- [x] Transfer learning example
- [ ] Eager mode training with `tf.GradientTape`
- [ ] Graph mode training with `model.fit`
- [x] Functional model with `tf.keras.layers`
- [x] Input pipeline using `tf.data`
- [x] Tensorflow Serving
- [ ] Vectorized transformations
- [x] GPU accelerated
- [x] Fully integrated with `absl-py` from [abseil.io](https://abseil.io)
- [x] Clean implementation
- [x] Following the best practices
- [x] MIT License
## Usage
### Installation
#### Pip
```bash
# without GPU
pip install -r requirements.txt
#with GPU
pip install -r requirements-gpu.txt
```
### Nvidia Driver (For GPU)
```bash
# Ubuntu 18.04
sudo apt-add-repository -r ppa:graphics-drivers/ppa
sudo apt install nvidia-driver-440
# Windows/Other
https://www.nvidia.com/Download/index.aspx
```
### Convert pre-trainned Darknet weights
```bash
# yolov3
wget https://pjreddie.com/media/files/yolov3.weights -O data/yolov3.weights
python convert.py --weights ./data/yolov3.weights --output ./checkpoints/yolov3.tf
```
### Detection
```bash
# WebCam
python object_detected.py --video 0
# Video file
python object_detected.py --video path_to_file.mp4
# Vidoe file with output
python object_detected.py --video path_to_file.mp4 --output ./output.avi
```
### Training
soon
## Implementation Details
### Loading pre-trained Darknet weights
very hard with pure functional API because the layer ordering is different in
tf.keras and darknet. The clean solution here is creating sub-models in keras.
Keras is not able to save nested model in h5 format properly, TF Checkpoint is
recommended since its offically supported by TensorFlow.
## Command Line Args Reference
```bash
convert.py:
--output: path to output
(default: './checkpoints/yolov3.tf')
--weights: path to weights file
(default: './data/yolov3.weights')
--num_classes: number of classes in the model
(default: '80')
(an integer)
detect_video.py:
--classes: path to classes file
(default: './data/coco.names')
--video: path to input video (use 0 for cam)
(default: './data/video.mp4')
--output: path to output video (remember to set right codec for given format. e.g. XVID for .avi)
(default: None)
--output_format: codec used in VideoWriter when saving video to file
(default: 'XVID')
--weights: path to weights file
(default: './checkpoints/yolov3.tf')
--num_classes: number of classes in the model
(default: '80')
(an integer)
```
# Change Log
#### May 25, 2020
- Updated to Tensorflow to v2.1.0 Release
## References
It is pretty much impossible to implement this from the yolov3 paper alone.
- https://github.com/pjreddie/darknet
- official yolov3 implementation
- https://github.com/AlexeyAB
- explinations of parameters
- https://github.com/qqwweee/keras-yolo3
- models
- loss functions
- https://github.com/YunYang1994/tensorflow-yolov3
- data transformations
- loss functions
- https://github.com/ayooshkathuria/pytorch-yolo-v3
- models
- https://github.com/broadinstitute/keras-resnet
- batch normalization fix
- https://github.com/zzh8829/yolov3-tf2
- tiny