ssd512¶
Use Case and High-Level Description¶
The ssd512 model is the Caffe* framework implementation of Single-Shot multibox Detection (SSD) algorithm with 512x512 input resolution and VGG-16 backbone. The network intended to perform visual object detection. This model is pretrained on VOC2007 + VOC2012 + COCO dataset and is able to detect 20 PASCAL VOC2007 object classes:
Person: person
Animal: bird, cat, cow, dog, horse, sheep
Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train
Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor
Mapping model labels to class names provided in <omz_dir>/data/dataset_classes/voc_20cl_bkgr.txt file.
For details about this model, check out the repository.
Specification¶
Metric |
Value |
|---|---|
Type |
Detection |
GFLOPs |
180.611 |
MParams |
27.189 |
Source framework |
Caffe* |
Accuracy¶
The accuracy results were obtained on test data from VOC2007 dataset.
Metric |
Value |
|---|---|
mAP |
91.07% |
Input¶
Original model¶
Image, name - data, shape - 1, 3, 512, 512, format is B, C, H, W, where:
B- batch sizeC- channelH- heightW- width
Channel order is BGR. Mean values - [104.0, 117.0, 123.0]
Converted model¶
Image, name - data, shape - 1, 3, 512, 512, format is B, C, H, W, where:
B- batch sizeC- channelH- heightW- width
Channel order is BGR.
Output¶
Original model¶
The array of detection summary info, name - detection_out, shape - 1, 1, 200, 7 in the format 1, 1, N, 7, where N is the number of detected bounding boxes. For each detection, the description has the format: [image_id, label, conf, x_min, y_min, x_max, y_max], where:
image_id- ID of the image in the batchlabel- predicted class ID (1..20 - PASCAL VOC defined class ids). Mapping to class names provided by<omz_dir>/data/dataset_classes/voc_20cl_bkgr.txtfile.conf- confidence for the predicted class, in [0, 1] range(
x_min,y_min) - coordinates of the top left bounding box corner (coordinates are in normalized format, in range [0, 1])(
x_max,y_max) - coordinates of the bottom right bounding box corner (coordinates are in normalized format, in range [0, 1])
Converted model¶
The array of detection summary info, name - detection_out, shape - 1, 1, 200, 7 in the format 1, 1, N, 7, where N is the number of detected bounding boxes. For each detection, the description has the format: [image_id, label, conf, x_min, y_min, x_max, y_max], where:
image_id- ID of the image in the batchlabel- predicted class ID (1..20 - PASCAL VOC defined class ids). Mapping to class names provided by<omz_dir>/data/dataset_classes/voc_20cl_bkgr.txtfile.conf- confidence for the predicted class, in [0, 1] range(
x_min,y_min) - coordinates of the top left bounding box corner (coordinates are in normalized format, in range [0, 1])(
x_max,y_max) - coordinates of the bottom right bounding box corner (coordinates are in normalized format, in range [0, 1])
Download a Model and Convert it into Inference Engine Format¶
You can download models and if necessary convert them into Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.
An example of using the Model Downloader:
python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>An example of using the Model Converter:
python3 <omz_dir>/tools/downloader/converter.py --name <model_name>Legal Information¶
The original model is distributed under the following license :
COPYRIGHT
All new contributions compared to the original branch:
Copyright (c) 2015, 2016 Wei Liu (UNC Chapel Hill), Dragomir Anguelov (Zoox),
Dumitru Erhan (Google), Christian Szegedy (Google), Scott Reed (UMich Ann Arbor),
Cheng-Yang Fu (UNC Chapel Hill), Alexander C. Berg (UNC Chapel Hill).
All rights reserved.
All contributions by the University of California:
Copyright (c) 2014, 2015, The Regents of the University of California (Regents)
All rights reserved.
All other contributions:
Copyright (c) 2014, 2015, the respective contributors
All rights reserved.
Caffe uses a shared copyright model: each contributor holds copyright over
their contributions to Caffe. The project versioning records all such
contribution and copyright details. If a contributor wants to further mark
their specific copyright on a particular contribution, they should indicate
their copyright solely in the commit message of the change when it is
committed.
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