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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
YOLO9000: Better, Faster, Stronger - Real-Time Object Detection. 9000 classes!
| Date | Stars |
|---|---|
| 2026-07-24 | 1196 |
| 2026-07-25 | 1196 |
| 2026-07-28 | 1196 |
| 2026-07-30 | 1194 |
| 2026-08-06 | 1194 |
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# Yolo 9000
YOLO9000: Better, Faster, Stronger - Real-Time Object Detection (State of the art). Official repository of [CVPR17](https://github.com/cvpr17).
<p align="center">
<img src="img/example.gif" width="500"><br/>
<i>Scroll down if you want to make your own video.</i>
</p>
## How to get started?
### Ubuntu/Linux/Mac OS
```bash
git clone --recursive https://github.com/philipperemy/yolo-9000.git
cd yolo-9000
cat yolo9000-weights/x* > yolo9000-weights/yolo9000.weights # it was generated from split -b 95m yolo9000.weights
cd darknet
make # Will run on CPU. For GPU support, scroll down!
./darknet detector test cfg/combine9k.data cfg/yolo9000.cfg ../yolo9000-weights/yolo9000.weights data/horses.jpg
```
### Windows
```
git clone --recursive https://github.com/philipperemy/yolo-9000.git
cd yolo-9000
type yolo9000-weights\xaa yolo9000-weights\xab > yolo9000-weights\yolo9000.weights
certUtil -hashfile yolo9000-weights\yolo9000.weights MD5
cd darknet
git reset --hard b61bcf544e8dbcbd2e978ca6a716fa96b37df767
```
You can use the latest version of `darknet` by running this command in the directory `yolo-9000`:
```bash
git submodule foreach git pull origin master
```
## Names of the 9k classes
Available here:
- https://github.com/pjreddie/darknet/blob/1e729804f61c8627eb257fba8b83f74e04945db7/data/9k.names
## Examples
`./darknet detector test cfg/combine9k.data cfg/yolo9000.cfg ../yolo9000-weights/yolo9000.weights data/horses.jpg`
<div align="center">
<img src="img/predictions_horses.png" width="400"><br><br>
</div>
`./darknet detector test cfg/combine9k.data cfg/yolo9000.cfg ../yolo9000-weights/yolo9000.weights data/person.jpg`
<div align="center">
<img src="img/predictions_person.png" width="400"><br><br>
</div>
The output should be something like:
```
layer filters size input output
0 conv 32 3 x 3 / 1 544 x 544 x 3 -> 544 x 544 x 32
1 max 2 x 2 / 2 544 x 544 x 32 -> 272 x 272 x 32
2 conv 64 3 x 3 / 1 272 x 272 x 32 -> 272 x 272 x 64
3 max 2 x 2 / 2 272 x 272 x 64 -> 136 x 136 x 64
4 conv 128 3 x 3 / 1 136 x 136 x 64 -> 136 x 136 x 128
5 conv 64 1 x 1 / 1 136 x 136 x 128 -> 136 x 136 x 64
6 conv 128 3 x 3 / 1 136 x 136 x 64 -> 136 x 136 x 128
7 max 2 x 2 / 2 136 x 136 x 128 -> 68 x 68 x 128
8 conv 256 3 x 3 / 1 68 x 68 x 128 -> 68 x 68 x 256
9 conv 128 1 x 1 / 1 68 x 68 x 256 -> 68 x 68 x 128
10 conv 256 3 x 3 / 1 68 x 68 x 128 -> 68 x 68 x 256
11 max 2 x 2 / 2 68 x 68 x 256 -> 34 x 34 x 256
12 conv 512 3 x 3 / 1 34 x 34 x 256 -> 34 x 34 x 512
13 conv 256 1 x 1 / 1 34 x 34 x 512 -> 34 x 34 x 256
14 conv 512 3 x 3 / 1 34 x 34 x 256 -> 34 x 34 x 512
15 conv 256 1 x 1 / 1 34 x 34 x 512 -> 34 x 34 x 256
16 conv 512 3 x 3 / 1 34 x 34 x 256 -> 34 x 34 x 512
17 max 2 x 2 / 2 34 x 34 x 512 -> 17 x 17 x 512
18 conv 1024 3 x 3 / 1 17 x 17 x 512 -> 17 x 17 x1024
19 conv 512 1 x 1 / 1 17 x 17 x1024 -> 17 x 17 x 512
20 conv 1024 3 x 3 / 1 17 x 17 x 512 -> 17 x 17 x1024
21 conv 512 1 x 1 / 1 17 x 17 x1024 -> 17 x 17 x 512
22 conv 1024 3 x 3 / 1 17 x 17 x 512 -> 17 x 17 x1024
23 conv 28269 1 x 1 / 1 17 x 17 x1024 -> 17 x 17 x28269
24 detection
Loading weights from ../yolo9000-weights/yolo9000.weights...Done!
data/horses.jpg: Predicted in 7.556429 seconds.
wild horse: 50%
Shetland pony: 84%
Aberdeen Angus: 72%
Not compiled with OpenCV, saving to predictions.png instead
```
The image with the bounding boxes is in `predictions.png`.
Browse on https://pjreddie.com/darknet/yolo/ to find how to compiExcerpt of 8,191 characters
Read on GitHub
Philippe Rémy · Imperial College London
21
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:689f0894d264207d, topic:yolo, desc:object detection, readme:object detection
matched fp:689f0894d264207d, topic:deep-learning