Compared with the fp32 SSD, SSD-int8's mAP drop ratio is 0.32% and performance improvement is 3.49x.
Note
The performance depends on the test hardware. Performance data here is collected with Intel® Xeon® Platinum 8280 Processor, 1s 4c per instance, CentOS Linux 8.3, data batch size is 1.
Inference
Input to model
Image shape (1x3x1200x1200)
Preprocessing steps
The images have to be loaded in to a range of [0, 1], resized to (1200, 1200) with bilinear interpolation and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. The transformation should preferrably happen at preprocessing.
The following code shows how to preprocess a NCHW tensor:
The model has 3 outputs.
boxes: (1x'nbox'x4)
labels: (1x'nbox')
scores: (1x'nbox')
Dataset (Train and validation)
The SSD model was trained on 2017 COCO train data set - using mlperf/training/single_stage_detector repo , compute mAP on 2017 COCO val data set.
Validation accuracy
Metric is COCO box mAP (averaged over IoU of 0.5:0.95), computed over 2017 COCO val data.
mAP of 0.195
Publication/Attribution
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg. SSD: Single Shot MultiBox Detector. In the Proceedings of the European Conference on Computer Vision (ECCV), 2016.
Backbone is ResNet34 pretrained on ILSVRC 2012 (from torchvision). Modifications to the backbone networks: remove conv_5x residual blocks, change the first 3x3 convolution of the conv_4x block from stride 2 to stride1 (this increases the resolution of the feature map to which detector heads are attached), attach all 6 detector heads to the output of the last conv_4x residual block. Thus detections are attached to 38x38, 19x19, 10x10, 5x5, 3x3, and 1x1 feature maps. Convolutions in the detector layers are followed by batch normalization layers.
Quantization
SSD-int8 and SSD-qdq are obtained by quantizing fp32 SSD model. We use Intel® Neural Compressor with onnxruntime backend to perform quantization. View the instructions to understand how to use Intel® Neural Compressor for quantization.
