efficientdet-d1-tf¶
Use Case and High-Level Description¶
The efficientdet-d1-tf
model is one of the EfficientDet models designed to perform object detection. This model was pre-trained in TensorFlow*. All the EfficientDet models have been pre-trained on the Common Objects in Context (COCO) image database. For details about this family of models, check out the Google AutoML repository.
Specification¶
Metric |
Value |
---|---|
Type |
Object detection |
GFLOPs |
6.1 |
MParams |
6.6 |
Source framework |
TensorFlow* |
Accuracy¶
Metric |
Converted model |
---|---|
37.54% |
Input¶
Original Model¶
Image, name: image_arrays
, shape: 1, 640, 640, 3
, format is B, H, W, C
, where:
B
- batch sizeH
- heightW
- widthC
- channel
Channel order is RGB
.
Converted Model¶
Image, name: image_arrays/placeholder_port_0
, shape: 1, 3, 640, 640
, format is B, C, H, W
, where:
B
- batch sizeC
- channelH
- heightW
- width
Channel order is BGR
.
Output¶
Original Model¶
The array of summary detection information, name: detections
, shape: 1, 100, 7
in the format 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:
image_id
- ID of the image in the batch(
x_min
,y_min
) - coordinates of the top left bounding box corner(
x_max
,y_max
) - coordinates of the bottom right bounding box cornerconfidence
- confidence for the predicted classlabel
- predicted class ID, in range<omz_dir>/data/dataset_classes/coco_91cl.txt
Converted Model¶
The array of summary detection information, name: detections
, shape: 1, 1, 100, 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, in range<omz_dir>/data/dataset_classes/coco_91cl.txt
conf
- confidence for the predicted class(
x_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])
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 Apache License, Version 2.0. A copy of the license is provided in <omz_dir>/models/public/licenses/APACHE-2.0-TF-AutoML.txt
.