The "efficientdet-d1-tf" model is one of the EfficientDet models designed to perform object detection. This model was pretrained in TensorFlow*. All the EfficientDet models have been pretrained on the MSCOCO* image database. For details about this family of models, check out the Google AutoML repository.
Metric | Value |
---|---|
Type | Object detection |
GFLOPs | 6.1 |
MParams | 6.6 |
Source framework | TensorFlow* |
Metric | Converted model |
---|---|
COCO* mAP (0.5:0.05:0.95) | 37.54% |
Image, name - image_arrays
, shape - [1x640x640x3]
, format is [BxHxWxC]
, where:
B
- batch sizeH
- heightW
- widthC
- channelChannel order is RGB
.
Image, name - image_arrays/placeholder_port_0
, shape - [1x3x640x640]
, format is [BxCxHxW]
, where:
B
- batch sizeC
- channelH
- heightW
- widthChannel order is BGR
.
The array of summary detection information, name: detections
, shape: [1, N, 7], where N is the number of detected bounding boxes. For each detection, the description has the format: [image_id
, y_min
, x_min
, y_max
, x_max
, confidence
, label
], where: where:
image_id
- ID of the image in the batchx_min
, y_min
) - coordinates of the top left bounding box cornerx_max
, y_max
) - coordinates of the bottom right bounding box cornerconfidence
- confidence for the predicted classlabel
- predicted class ID, in range [1, 91] across following labelsThe array of summary detection information, name: detections
, 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 ID, in range [0, 90] across following labelsconf
- 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])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:
An example of using the Model Converter:
The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0-TF-AutoML.txt.