xiaohu2015 / ssd_pytorch

SSD_PyTorch

This is a clean implementation of SSD from torchvision.

To execute the example commands below you must install the following:

cython
pycocotools
matplotlib

You must modify the following flags:

--data-path=/path/to/coco/dataset

--nproc_per_node=<number_of_gpus_available>

Except otherwise noted, all models have been trained on 8x V100 GPUs.

SSD300 VGG16

torchrun --nproc_per_node=8 train.py\
    --dataset coco --model ssd300_vgg16 --epochs 120\
    --lr-steps 80 110 --aspect-ratio-group-factor 3 --lr 0.002 --batch-size 4\
    --weight-decay 0.0005 --data-augmentation ssd

IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.251
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.415
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.262
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.055
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.268
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.435
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.239
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.344
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.365
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.088
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.406
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.602

SSDlite320 MobileNetV3-Large

torchrun --nproc_per_node=8 train.py\
    --dataset coco --model ssdlite320_mobilenet_v3_large --epochs 660\
    --aspect-ratio-group-factor 3 --lr-scheduler cosineannealinglr --lr 0.15 --batch-size 24\
    --weight-decay 0.00004 --data-augmentation ssdlite

IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.213
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.343
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.221
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.011
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.202
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.444
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.208
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.307
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.334
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.042
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.344
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.643

Inference

See inference_demo.