ssd

Single Stage Detector

Description

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

Model

Model	Download	Download (with sample test data)	ONNX version	Opset version	Accuracy
SSD	80.4 MB	78.5 MB	1.5	10	mAP of 0.195
SSD	77.6 MB	86.4 MB	1.9	12	mAP of 0.1898
SSD-int8	20 MB	31 MB	1.9	12	mAP of 0.1892
SSD-qdq	20 MB	26 MB	1.9	12	mAP of 0.1863

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')

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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