nanodet-plus-m-1.5x-416#
Use Case and High-Level Description#
The nanodet-plus-m-1.5x-416
model is one from NanoDet models family, which is a FCOS-style one-stage anchor-free object detection model which using Generalized Focal Loss as classification and regression loss. A novel label assignment strategy with a simple assign guidance module (AGM) and a dynamic soft label assigner (DSLA) is used in NanoDet-Plus to solve the optimal label assignment problem in lightweight model training. Also a light feature pyramid called Ghost-PAN is introduced in Plus models to enhance multi-layer feature fusion. The model is a super fast and high accuracy lightweight model with ShuffleNetV2 1.5x backbone. This model was pre-trained on Common Objects in Context (COCO) dataset.
More details provided in the repository.
Specification#
Metric |
Value |
---|---|
Type |
Object detection |
GFLOPs |
3.0147 |
MParams |
2.4614 |
Source framework |
PyTorch* |
Accuracy#
Accuracy metrics obtained on Common Objects in Context (COCO) validation dataset for converted model. Label map with 80 public available object categories are used.
Metric |
Value |
---|---|
coco_orig_precision |
33.77% |
coco_precision |
34.53% |
Input#
Original model#
Image, name - data
, shape - 1, 3, 416, 416
, format B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- image width
Expected color order is BGR
.
Mean values - [103.53, 116.28, 123.675]. Scale values - [57.375, 57.12, 58.395].
Converted model#
Image, name - data
, shape - 1, 3, 416, 416
, format B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- image width
Expected color order is BGR
.
Output#
Original model#
The array of detection summary info, name - output
, shape - 1, 3598, 112
, format is B, N, 112
, where:
B
- batch sizeN
- number of detection boxes
Detection box has the following format:
80 probability distribution over the classes in logits format for 80 public available Common Objects in Context (COCO) object classes, listed in file
<omz_dir>/data/dataset_classes/coco_80cl.txt
.8 * 4 raw coordinates in format
A
* 4, whereA
- max value of integral set.
Converted model#
The array of detection summary info, name - output
, shape - 1, 3598, 112
, format is B, N, 112
, where:
B
- batch sizeN
- number of detection boxes
Detection box has the following format:
80 probability distribution over the classes in logits format for 80 public available Common Objects in Context (COCO) object classes, listed in file
<omz_dir>/data/dataset_classes/coco_80cl.txt
.8 * 4 raw coordinates in format
A
* 4, whereA
- max value of integral set.
Download a Model and Convert it into OpenVINO™ IR Format#
You can download models and if necessary convert them into OpenVINO™ IR format using the Model Downloader and other automation tools as shown in the examples below.
An example of using the Model Downloader:
omz_downloader --name <model_name>
An example of using the Model Converter:
omz_converter --name <model_name>
Demo usage#
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
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-PyTorch-NanoDet.txt
.