The ssd_mobilenet_v1_coco
model is a Single-Shot multibox Detection (SSD) network intended to perform object detection. The difference bewteen this model and the mobilenet-ssd
is that there the mobilenet-ssd
can only detect face, the ssd_mobilenet_v1_coco
model can detect objects.
Metric | Value |
---|---|
Type | Detection |
GFLOPs | 2.494 |
MParams | 6.807 |
Source framework | TensorFlow* |
Metric | Value |
---|---|
coco_precision | 23.3212% |
Image, name - image_tensor
, shape - [1x300x300x3], format [BxHxWxC], where:
Expected color order - RGB.
Image, name - image_tensor
, shape - [1x3x300x300], format [BxCxHxW], where:
Expected color order: BGR.
detection_classes
, contains predicted bounding boxes classes in range [1, 91]. The model was trained on Microsoft* COCO dataset version with 90 categories of object.detection_scores
, contains probability of detected bounding boxes.detection_boxes
, contains detection boxes coordinates in format [y_min, x_min, y_max, x_max]
, where (x_min
, y_min
) are coordinates top left corner, (x_max
, y_max
) are coordinates right bottom corner. Coordinates are rescaled to input image size.num_detections
, contains the number of predicted detection boxes.The array of summary detection information, name - DetectionOutput
, shape - [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 IDconf
- confidence for the predicted classx_min
, y_min
) - coordinates of the top left bounding box corner (coordinates stored in normalized format, in range [0, 1])x_max
, y_max
) - coordinates of the bottom right bounding box corner (coordinates stored in normalized format, in range [0, 1])The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0-TensorFlow.txt.