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This is a clear, extensible yolo v3 framework
Testing in ubuntu 18.04, Python 3.7.1
, Others in requirements.txt
.
first use yolo scripts:
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:
python3 make_voc_list.py xxxx/train.txt data/voc_img_ann.npy
Load the annotations generate anchors (LOW
and HIGH
depending on the distribution of dataset):
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
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 | weiyun |
yolo_mobilev1 | 0.75 | google drive | weiyun |
yolo_mobilev1 | 1.0 | google drive | weiyun |
yolo_mobilev2 | 0.5 | google drive | weiyun |
yolo_mobilev2 | 0.75 | google drive | weiyun |
yolo_mobilev2 | 1.0 | google drive | weiyun |
tiny_yolo | google drive | weiyun | |
yolo | google drive | weiyun |
NOTE: The mobilenet is not original, I have modified it to fit k210
When you use mobilenet, you need to specify the DEPTHMUL
parameter. You don't need set DEPTHMUL
to use tiny yolo
or yolo
.
Set MODEL
and DEPTHMUL
to start training:
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.
Set CKPT
to continue training:
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
Set IAA
to enable data augment:
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
Use tensorboard:
tensorboard --logdir log
NOTE: The more options please see with python3 ./keras_train.py -h
make inference MODEL=xxxx DEPTHMUL=xx CLSNUM=xx CKPT=log/xxxxxx/yolo_model.h5 IMG=data/people.jpg
You can try with my model :
make inference MODEL=yolo_mobilev1 DEPTHMUL=0.75 CKPT=asset/yolo_model.h5 IMG=data/people.jpg
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
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
.
toco --output_file mobile_yolo.tflite --keras_model_file log/xxxxxx/yolo_model.h5
Now you have mobile_yolo.tflite
Please refer nncase v0.1.0-RC5 example
Use kendryte-standalone-sdk v0.5.6
Use Kflash.py
kflash yolo3_frame_test_public/kfpkg/kpu_yolov3.kfpkg -B kd233 -p /dev/ttyUSB0 -b 2000000 -t
Use Kflash.py
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 to prove the validity of the model.
If you need standard yolov3 region layer code
, you can buy with me.
Makefile
OBJWEIGHT
,NOOBJWEIGHT
,WHWEIGHT
used to balance precision and recallOUTSIZE
IMGSIZE
and OUTSIZE
in the Makefile to the original yolo parameters