https://github.com/super-dainiu/yolov3
Midterm of DATA130051 Computer Vision
https://github.com/super-dainiu/yolov3
darknet object-detection pytorch yolo yolov3
Last synced: 3 months ago
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Midterm of DATA130051 Computer Vision
- Host: GitHub
- URL: https://github.com/super-dainiu/yolov3
- Owner: super-dainiu
- License: apache-2.0
- Created: 2022-04-18T19:05:20.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2022-07-13T07:02:34.000Z (almost 3 years ago)
- Last Synced: 2025-01-29T16:43:14.640Z (5 months ago)
- Topics: darknet, object-detection, pytorch, yolo, yolov3
- Language: Python
- Homepage:
- Size: 33.5 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# YOLOv3 from scratch
This is the mid-term project for DATA130051 Computer Vision. ðŪðŪðŪ
Full report <[here](https://github.com/super-dainiu/cv-midterm)>.
YOLOv3 ð is an open-source object detection architecture first proposed by Joseph Redmon[[1](https://pjreddie.com/)]. I managed to reproduce the network from scratch with the help of Aladdin Persson[[2](https://github.com/aladdinpersson/)] and his excellent video tutorials[[3](https://www.youtube.com/watch?v=Grir6TZbc1M)]. ðĪģ Basically, I reimplemented YOLOv3 model as well as the utils and metrics for detections. Also, a detector class was written to provide a shortcut for video detections. Again, thanks a lot for those who contribute to this open-source society! ðĪ
### Quick Start ð
#### Install ð
[Python>=3.6.0](https://www.python.org/) is required with all [requirements.txt](./requirements.txt) installed including [PyTorch>=1.9.0](https://pytorch.org/get-started/locally/):
```bash
git clone https://github.com/super-dainiu/yolov3
cd yolov3
pip install -r requirements.txt
```
#### Download weights ðĪŠ
Please follow the guide in [./pretrained](./pretrained) directory.
#### Download dataset ðĨĩ
Please follow the guide in [./PASCAL_VOC](./PASCAL_VOC) directory. Again credit to Aladdin Persson[[2](https://github.com/aladdinpersson/)] for his excellent tutorial. ð
#### Inference ðū
After downloading the weights, you can do detection for images, gifs, or mp4 using [main.py](main.py).
>usage: main.py [-h] --weights WEIGHTS [--save SAVE] [--save_dir SAVE_DIR] [--conf_thres CONF_THRES] [--iou_thres IOU_THRES] [--max_det MAX_DET] [--target TARGET [TARGET ...]]
> [--camera CAMERA] [--samples SAMPLES [SAMPLES ...]]
>
>Use YOLOv3
>
>optional arguments:
>
>-h, --help show this help message and exit
>
>--weights WEIGHTS Weight directory
>
>--save SAVE Save results
>
>--save_dir SAVE_DIR Save directory
>
>--conf_thres CONF_THRES
> Confidence threshold
>
>--iou_thres IOU_THRES
> IOU threshold
>
>--max_det MAX_DET Maximum detection per frame
>
>--target TARGET [TARGET ...]
> Targets (i.e. person), * for all classes
>
>--camera CAMERA Use your camera
>
>--samples SAMPLES [SAMPLES ...]
> Sample images (ends with .jpg, .png, .gif, .mp4)
You might do a simple detection with the following bash command.
```bash
python main.py --weights pretrained/yolov3.pth.tar --sample samples/video.mp4 samples/image_1.jpg samples/image_2.png samples/gif_1.gif --save True --save-dir outputs --target person car --conf_thres 0.7 --iou_thres 0.3 --max_det 10
```
Or activate your camera for detection.
```bash
python main.py --weights pretrained/yolov3.pth.tar --camera True
```
### Results ð
#### PASCAL VOC metrics (mAP at .5 IOU) ð
| Class | mAP |
| ------------- | ------ |
| aeroplane | 58.018 |
| bicycle | 60.933 |
| bird | 34.835 |
| boat | 32.412 |
| bottle | 17.423 |
| bus | 63.038 |
| car | 69.853 |
| cat | 57.547 |
| chair | 29.547 |
| cow | 44.667 |
| diningtable | 48.446 |
| dog | 48.934 |
| horse | 59.299 |
| motorbike | 63.281 |
| person | 52.751 |
| pottedplant | 19.121 |
| sheep | 48.951 |
| sofa | 51.492 |
| train | 56.918 |
| tvmonitor | 55.288 |
| **TOTAL mAP** | 48.638 |
#### Example Outputs ðĐ
Transformer
CR7 Celebration
Maguire and Lingod
The dog
The cat
Car
