Refer to Pytorch-HarDNet for more information
HarDNet68/85:
With enhanced feature extraction on high resolution feature maps, the performance on object detection can be better than models designed for image classification which generally concentrate on global feature extraction.
| Method | COCO mAP on test-dev |
Overall fps (Titan X) |
Overall fps (1080Ti) |
Overall fps (Titan V) |
|---|---|---|---|---|
| SSD512-HarDNet68 | 31.7 | 41 fps | 46.7 fps | 50.4 fps |
| SSD512-HarDNet85 | 35.1 | 32.7 fps | 39.4 fps | 43.4 fps |
| RFBNet512-HarDNet68 | 33.9 | 30 fps | 37.5 fps | 41.5 fps |
| RFBNet512-HarDNet85 | 36.8 | 26 fps | 33.5 fps | 37.1 fps |
12/19 2019 update: Release new overall frame rate measurements after the nms speed improvement*.
*nms speed improvement: 1. employ torchvision nms. 2. filter out bbox with high prob to be background before the nms.
| Method | COCO mAP on test-dev | Inference Time (1080ti, without nms) |
|---|---|---|
| SSD512-VGG16 | 28.8 | 19.7ms |
| SSD513-ResNet101 | 31.2 | - |
| SSD512-HarDNet68 | 31.7 | 13.8ms |
| SSD512-HarDNet85 | 35.1 | 18.5ms |
| RFBNet512-HarDNet68 | 33.9 | 20.0ms |
| RFBNet512-HarDNet85 | 36.8 | 23.2ms |
Note: Inference time and overall fps results were measured with pytorch 1.3 (float32) and cuda 10.1. Please note that HarDNet still suffers from the explicit tensor copy for concatenations. To fully utilize the GPU please increase the batch size or input image size(> 512x512) in the test time. The current results was tested with batch_size=1 and image_size=512x512, which utilize only ~50% of GPU time in average.
SSD512-HarDNet68 detailed results (test-dev):
overall performance
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.317
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.510
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.338
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.125
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.351
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.479
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.277
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.419
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.439
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.184
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.485
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.636
SSD512-HarDNet85 detailed results (test-dev):
overall performance
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.351
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.548
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.376
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.150
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.389
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.515
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.301
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.454
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.475
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.217
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.528
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.673
RFBNet512-HarDNet68 detailed results (test-dev):
overall performance
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.339
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.543
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.362
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.147
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.366
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.505
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.292
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.444
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.468
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.233
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.500
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.664
RFBNet512-HarDNet85 detailed results (test-dev):
overall performance
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.368
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.571
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.395
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.169
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.405
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.529
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.309
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.474
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.498
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.259
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.543
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.688
- Install PyTorch 0.2.0 - 0.4.1 by selecting your environment on the website and running the appropriate command.
- Clone this repository. This repository is forked from PytorchSSD,
- Compile the nms and coco tools: (The nms utilities need to be compiled with an old version of Pytorch. After compile, you can upgrade Pytorch to the newest version)
./make.shNote*: Check you GPU architecture support in utils/build.py, line 131. Default is:
'nvcc': ['-arch=sm_52',
- Then download the dataset by following the instructions below and install opencv.
conda install opencvNote: For training, we currently support VOC and COCO.
To make things easy, we provide simple VOC and COCO dataset loader that inherits torch.utils.data.Dataset making it fully compatible with the torchvision.datasets API.
# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2007.sh # <directory># specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2012.sh # <directory>Install the MS COCO dataset at /path/to/coco from official website, default is ~/data/COCO. Following the instructions to prepare minival2014 and valminusminival2014 annotations. All label files (.json) should be under the COCO/annotations/ folder. It should have this basic structure
$COCO/
$COCO/cache/
$COCO/annotations/
$COCO/images/
$COCO/images/test2015/
$COCO/images/train2014/
$COCO/images/val2014/UPDATE: The current COCO dataset has released new train2017 and val2017 sets which are just new splits of the same image sets.
python train_test.py -v SSD_HarDNet68 -s 512 --test <path_to_pretrained_weight.pth>
- Pretrained backbone models: hardnet68_base_bridge.pth | hardnet85_base.pth
- Pretrained models for COCO dataset: SSD512-HarDNet68 | SSD512-HarDNet85 | RFBNet512-HarDNet68 | RFBNet512-HarDNet85
- Run the follwing to train SSD-HarDNet:
python train_test.py -d VOC -v SSD_HarDNet68 -s 512- Note:
- -d: choose datasets, VOC or COCO.
- -v: choose backbone version, SSD_HarDNet68 or SSD_HarDNet85.
- -s: image size, 300 or 512.
- batch size = 32
- epochs = 150 (COCO) / 300 (VOC)
- initial lr = 4e-3
- lr decay by 0.1 at [60%, 80%, 90%] of total epochs
- weight decay = 1e-4 (COCO) / 5e-4 (VOC)
- we = 0 (no need for warm up)