https://github.com/tancnle/docker-darknet

YOLO Image Dectection with Darknet
https://github.com/tancnle/docker-darknet

docker object-detection yolo

Last synced: about 1 year ago
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YOLO Image Dectection with Darknet

• Host: GitHub
• URL: https://github.com/tancnle/docker-darknet
• Owner: tancnle
• License: wtfpl
• Created: 2019-04-13T05:26:26.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2020-05-17T13:16:58.000Z (about 6 years ago)
• Last Synced: 2025-03-25T15:02:17.467Z (over 1 year ago)
• Topics: docker, object-detection, yolo
• Language: Dockerfile
• Homepage:
• Size: 427 KB
• Stars: 20
• Watchers: 1
• Forks: 10
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# YOLO Object Detection with Darknet

![Docker Automated build](https://img.shields.io/docker/cloud/automated/tancnle/darknet-yolo.svg?style=popout-square)

![Docker Cloud Build Status](https://img.shields.io/docker/cloud/build/tancnle/darknet-yolo.svg?style=popout-square)

A convenient way to do object detection using YOLOv3/YOLOv4 model via Docker.

## Usage

Run object detection on an image

```bash

# YOLO v3

docker run --volume ${PWD}/output:/output \

           --interactive --rm tancnle/darknet-yolo:3 < interesting.png

# YOLO v4

docker run --volume ${PWD}/output:/output \

           --interactive --rm tancnle/darknet-yolo:4 < interesting.png

```

View the image with polygon-bounding boxes

```bash

open output/prediction.png

```

## Example Output

| Original | YOLO v3 | YOLO v4 |

| :-: | :-: | :-: |

| ![original](https://github.com/tancnle/docker-darknet/raw/master/examples/storefront.jpg) | ![YOLO v3](https://github.com/tancnle/docker-darknet/raw/master/examples/detected-storefront.jpg) | ![YOLO v4](https://github.com/tancnle/docker-darknet/raw/master/examples/detected-storefront-v4.jpg) |

```bash

# YOLO v3

Loading weights from yolov3.weights...Done!

/tmp/image: Predicted in 19.697341 seconds.

pottedplant: 98%

bicycle: 89%

bicycle: 73%

# YOLO v4

Loading weights from yolov4.weights...mini_batch = 1, batch = 8, time_steps = 1, train = 0

nms_kind: greedynms (1), beta = 0.600000

nms_kind: greedynms (1), beta = 0.600000

nms_kind: greedynms (1), beta = 0.600000

Done! Loaded 162 layers from weights-file

Not compiled with OpenCV, saving to predictions.png instead

 seen 64, trained: 32032 K-images (500 Kilo-batches_64)

/tmp/image: Predicted in 22087.479000 milli-seconds.

pottedplant: 98%

bicycle: 38%

bicycle: 47%

bicycle: 53%

person: 55%

person: 67%

```

## References

* YOLO: Real-Time Object Detection (https://pjreddie.com/darknet/yolo/)

* Darknet (https://github.com/pjreddie/darknet)

* Darknet (https://github.com/AlexeyAB/darknet)