Test implementation of Tiny-YOLO-v3.
Based on MXNet and Gluon-cv.
This repo is in active development. Issues are welcomed.
- Morden-day tricks, including multi-scale training and mix-up
- Pretrained weights and logs provided
- EXTREMELY FAST (See below)
- python 3.7
- mxnet 1.5.1
- numpy < 1.18
- matplotlib
- tqdm
- opencv
- pycocotools
Note that numpy 1.18 will cause problem for pycocotools.
See more.
I'd suggest creating a new conda environment.
conda create -n tinyyolo python=3.7 numpy=1.17 matplotlib tqdm opencv Cython
conda activate tinyyolo
pip install mxnet-cu101mkl pycocotools
Other versions like mxnet-cu92 and mxnet-cu92mkl are all acceptable.
git clone [email protected]:EletronicElephant/tiny_yolov3.git
cd tiny_yolov3/gluon-cv
python setup.py develop --user
Up to now, only MS COCO-formatted dataset is supported.
cd ./.. # return to the root
mkdir data
cd data
ln -s /disk1/data/coco
weights/best.params
Evaluation results on COCO val2017 are listed below.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.139
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.297
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.114
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.047
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.137
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.224
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.159
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.248
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.262
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.100
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.270
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.406
You can either edit the parameters by changing the default values in trian.py or specify it.
Personally, I would recommend creating a new file named train.sh and adds
python train.py \
--batch-size 64 \
--gpus 4,5 \
--num-workers 16 \
--warmup-epochs 2 \
--lr 0.001 \
--epochs 200 \
--lr-mode step \
--save-prefix ./results/1/ \
--save-interval 1 \
--log-interval 100 \
--start-epoch 0 \
--optimizer sgd \
--label-smooth
Make sure you have 24GB gMemory for training with batch-size=64 and random-shape.
In other words, training with bs=64 and data-shape=640 will use 24GB gMemory.
For a more commonly-used shape 416*416, 12GB gMemory will be used for bs=64 at 200 Samples/second on a Titan Xp.
I believe online-evaluating is stupid, for it can waste valuable training time.
Instead, I would suggest a bash trick.
for epoch in {0000..0199..1}
do
while [ ! -f ./results/yolo3_tiny_darknet_coco_${epoch}.params ]
do
echo -n "."
sleep 60
done
python eval.py --data-shape 416 \
--save-prefix ./results/ \
--gpus 3 --batch-size 4 --num-workers 4 --start-epoch ${epoch}
done
TODO
python eval.py --resume weights/best.params --benchmark
Here is the test result on Titan Xp
| data-shape | fps (bs=1) | fps (bs=8) |
|---|---|---|
| 320 | 218 | 633 |
| 416 | 204 | 638 |
| 608 | 156 | 327 |
I got a lot of code from gluon-cv. Thanks.
If you encountered with high CPU-usage while training (especially on some machines that have more than 40 cores), you can set these environmental variables
export MKL_NUM_THREADS="1"
export MKL_DOMAIN_NUM_THREADS="MKL_BLAS=1"
export OMP_NUM_THREADS="1"
export MKL_DYNAMIC="FALSE"
export OMP_DYNAMIC="FALSE"
MXNet currently doesn't provide any high-performance image-data-argumentation method. The whole training speed is largely infected by the transformer.
- [UNTESTED] Mixup will not work.
- [UNTESTED]
Adamoptimizer runs slowly.