ssd-12

<!--- SPDX-License-Identifier: Apache-2.0 -->

Single Stage Detector

Description

This model is a real-time neural network for object detection that detects 80 different classes.

Model

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.

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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:

import numpy as np
from PIL import Image

def preprocess(img_path):
input_shape = (1, 3, 1200, 1200)
img = Image.open(img_path)
img = img.resize((1200, 1200), Image.BILINEAR)
img_data = np.array(img)
img_data = np.transpose(img_data, [2, 0, 1])
img_data = np.expand_dims(img_data, 0)
mean_vec = np.array([0.485, 0.456, 0.406])
stddev_vec = np.array([0.229, 0.224, 0.225])
norm_img_data = np.zeros(img_data.shape).astype('float32')
for i in range(img_data.shape[1]):
norm_img_data[:,i,:,:] = (img_data[:,i,:,:]/255 - mean_vec[i]) / stddev_vec[i]
return norm_img_data

Output of model

The model has 3 outputs. boxes: (1x'nbox'x4) labels: (1x'nbox') scores: (1x'nbox')

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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.

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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

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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.

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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.

Environment

onnx: 1.9.0 onnxruntime: 1.8.0

Prepare model

wget https://github.com/onnx/models/raw/main/vision/object_detection_segmentation/ssd/model/ssd-12.onnx

Model quantize

Make sure to specify the appropriate dataset path in the configuration file.

bash run_tuning.sh --input_model=path/to/model \  # model path as *.onnx
--config=ssd.yaml \
--output_model=path/to/save

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References

• This model is converted from mlperf/inference repository with modifications in repository.

• Intel® Neural Compressor

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Contributors

• mengniwang95 (Intel)
• yuwenzho (Intel)
• airMeng (Intel)
• ftian1 (Intel)
• hshen14 (Intel)

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License

Apache License 2.0

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