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https://github.com/zhen8838/K210_Yolo_framework
Yolo v3 framework base on tensorflow, support multiple models, multiple datasets, any number of output layers, any number of anchors, model prune, and portable model to K210 !
https://github.com/zhen8838/K210_Yolo_framework
Last synced: 3 months ago
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Yolo v3 framework base on tensorflow, support multiple models, multiple datasets, any number of output layers, any number of anchors, model prune, and portable model to K210 !
- Host: GitHub
- URL: https://github.com/zhen8838/K210_Yolo_framework
- Owner: zhen8838
- License: mit
- Created: 2019-07-09T13:34:28.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-11-22T04:32:09.000Z (about 2 years ago)
- Last Synced: 2024-08-01T03:17:15.527Z (6 months ago)
- Language: C
- Homepage:
- Size: 50.2 MB
- Stars: 245
- Watchers: 12
- Forks: 73
- Open Issues: 28
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-yolo-object-detection - zhen8838/K210_Yolo_framework
- awesome-yolo-object-detection - zhen8838/K210_Yolo_framework
README
[toc]
# K210 YOLO V3 framework
This is a clear, extensible yolo v3 framework
- [x] Real-time display recall and precision
- [x] Easy to use with other datasets
- [x] Support multiple model backbones and expand more
- [x] Support n number of output layers and m anchors
- [x] Support model weight pruning
- [x] Portable model to kendryte [K210](https://kendryte.com/) chip
# Training on Voc
## Set Environment
Testing in ubuntu 18.04, `Python 3.7.1`, Others in `requirements.txt`.
## Prepare dataset
first use [yolo](https://pjreddie.com/darknet/yolo/) scripts:
```sh
wget https://pjreddie.com/media/files/VOCtrainval_11-May-2012.tar
wget https://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar
wget https://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar
tar xf VOCtrainval_11-May-2012.tar
tar xf VOCtrainval_06-Nov-2007.tar
tar xf VOCtest_06-Nov-2007.tar
wget https://pjreddie.com/media/files/voc_label.py
python3 voc_label.py
cat 2007_train.txt 2007_val.txt 2012_*.txt > train.txt
```
now you have `train.txt`, then merge img path and annotation to one npy file:
```sh
python3 make_voc_list.py xxxx/train.txt data/voc_img_ann.npy
```
## Make anchors
Load the annotations generate anchors (`LOW` and `HIGH` depending on the distribution of dataset):
```sh
make anchors DATASET=voc ANCNUM=3 LOW='.0 .0' HIGH='1. 1.'
```
When success you will see figure like this:
**NOTE:** the kmeans result is random. when you get error , just rerun it.
If you want to use custom dataset, just write script and generate `data/{dataset_name}_img_ann.npy`, Then use `make anchors DATASET=dataset_name`. The more options please see with `python3 ./make_anchor_list.py -h`
If you want to change number of output layer, you should modify `OUTSIZE` in Makefile
## Download pre-trian model
You **must** download the model weights you want to train because I load the pre-train weights by default. And put the files into `K210_Yolo_framework/data` directory.
**My Demo use `yolo_mobilev1 0.75`**
| `MODEL` | `DEPTHMUL` | Url | Url |
| ------------- | ---------- | ---------------------------------------------------------------------------------- | ------------------------------------------ |
| yolo_mobilev1 | 0.5 | [google drive](https://drive.google.com/open?id=1SmuqIU1uCLRgaePve9HgCj-SvXJB7U-I) | [weiyun](https://share.weiyun.com/59nnvtW) |
| yolo_mobilev1 | 0.75 | [google drive](https://drive.google.com/open?id=1BlH6va_plAEUnWBER6vij_Q_Gp8TFFaP) | [weiyun](https://share.weiyun.com/5FgNE0b) |
| yolo_mobilev1 | 1.0 | [google drive](https://drive.google.com/open?id=1vIuylSVshJ47aJV3gmoYyqxQ5Rz9FAkA) | [weiyun](https://share.weiyun.com/516LqR7) |
| yolo_mobilev2 | 0.5 | [google drive](https://drive.google.com/open?id=1qjpexl4dZLMtd0dX3QtoIHxXtidj993N) | [weiyun](https://share.weiyun.com/5BwaRTu) |
| yolo_mobilev2 | 0.75 | [google drive](https://drive.google.com/open?id=1qSM5iQDicscSg0MYfZfiIEFGkc3Xtlt1) | [weiyun](https://share.weiyun.com/5RRMwob) |
| yolo_mobilev2 | 1.0 | [google drive](https://drive.google.com/open?id=1Qms1BMVtT8DcXvBUFBTgTBtVxQc9r4BQ) | [weiyun](https://share.weiyun.com/5dUelqn) |
| tiny_yolo | | [google drive](https://drive.google.com/open?id=1M1ZUAFJ93WzDaHOtaa8MX015HdoE85LM) | [weiyun](https://share.weiyun.com/5413QWx) |
| yolo | | [google drive](https://drive.google.com/open?id=17eGV6DCaFQhVoxOuTUiwi7-v22DAwbXf) | [weiyun](https://share.weiyun.com/55g6zHl) |
**NOTE:** The mobilenet is not original, I have **modified it** to fit k210
## Train
When you use mobilenet, you need to specify the `DEPTHMUL` parameter. You don't need set `DEPTHMUL` to use `tiny yolo` or `yolo`.
1. Set `MODEL` and `DEPTHMUL` to start training:
```sh
make train MODEL=xxxx DEPTHMUL=xx MAXEP=10 ILR=0.001 DATASET=voc CLSNUM=20 IAA=False BATCH=16
```
**You can use `Ctrl+C` to stop training** , it will auto save weights and model in log dir.
2. Set `CKPT` to continue training:
```sh
make train MODEL=xxxx DEPTHMUL=xx MAXEP=10 ILR=0.0005 DATASET=voc CLSNUM=20 IAA=False BATCH=16 CKPT=log/xxxxxxxxx/yolo_model.h5
```
3. Set `IAA` to enable data augment:
```sh
make train MODEL=xxxx DEPTHMUL=xx MAXEP=10 ILR=0.0001 DATASET=voc CLSNUM=20 IAA=True BATCH=16 CKPT=log/xxxxxxxxx/yolo_model.h5
```
4. Use tensorboard:
```sh
tensorboard --logdir log
```
**NOTE:** The more options please see with `python3 ./keras_train.py -h`
## Inference
```sh
make inference MODEL=xxxx DEPTHMUL=xx CLSNUM=xx CKPT=log/xxxxxx/yolo_model.h5 IMG=data/people.jpg
```
You can try with my model :
```sh
make inference MODEL=yolo_mobilev1 DEPTHMUL=0.75 CKPT=asset/yolo_model.h5 IMG=data/people.jpg
```
```sh
make inference MODEL=yolo_mobilev1 DEPTHMUL=0.75 CKPT=asset/yolo_model.h5 IMG=data/dog.jpg
```
**NOTE:** Since the anchor is randomly generated, your results will be **different from the above image**.You just need to load this model and continue training for a while.
The more options please see with `python3 ./keras_inference.py -h`
## Prune Model
```sh
make train MODEL=xxxx MAXEP=1 ILR=0.0003 DATASET=voc CLSNUM=20 BATCH=16 PRUNE=True CKPT=log/xxxxxx/yolo_model.h5 END_EPOCH=1
```
When training finish, will save model as `log/xxxxxx/yolo_prune_model.h5`.
## Freeze
```sh
toco --output_file mobile_yolo.tflite --keras_model_file log/xxxxxx/yolo_model.h5
```
Now you have `mobile_yolo.tflite`
## Convert Kmodel
Please refer [nncase v0.1.0-RC5](https://github.com/kendryte/nncase/tree/v0.1.0-rc5) [example](https://github.com/kendryte/nncase/tree/v0.1.0-rc5/examples/20classes_yolo)
## Demo
Use [kendryte-standalone-sdk v0.5.6](https://github.com/kendryte/kendryte-standalone-sdk/tree/V0.5.6)
* [KD233](https://kendryte.com/)
Use [Kflash.py](https://github.com/kendryte/kflash.py)
```sh
kflash yolo3_frame_test_public/kfpkg/kpu_yolov3.kfpkg -B kd233 -p /dev/ttyUSB0 -b 2000000 -t
```
* [MAIXPY GO](https://wiki.sipeed.com/en/maix/board/go.html)
Use [Kflash.py](https://github.com/kendryte/kflash.py)
```sh
kflash yolo3_frame_test_public_maixpy/kfpkg/kpu_yolov3.kfpkg -B goE -p /dev/ttyUSB1 -b 2000000 -t
```
**NOTE:** I just use [kendryte yolov2 demo code](https://github.com/kendryte/nncase/tree/master/examples/20classes_yolo/k210/kpu_20classes_example) to prove the validity of the model.
If you need `standard yolov3 region layer code`, you can buy with me.
# Caution
1. Default parameter in `Makefile`
2. `OBJWEIGHT`,`NOOBJWEIGHT`,`WHWEIGHT` used to balance precision and recall
3. Default output two layers,if you want more output layers can modify `OUTSIZE`
4. If you want to use the **full yolo**, you need to modify the `IMGSIZE` and `OUTSIZE` in the Makefile to the original yolo parameters