DeepLabV3-MobileNetV2 is a lightweight deep learning model for semantic image segmentation based on the DeepLabV3 architecture [2] and a MobileNetV2 backbone [3] with depth multiplier 0.5.
It classifies each pixel of the input image into 20 object classes plus a background class, using the PASCAL VOC label set.
Key Features
Fully quantized int8 model ready for edge deployment
Lightweight MobileNetV2 backbone (depth multiplier 0.5) for fast inference
Outputs per-pixel class scores for 21 classes (20 PASCAL VOC + background)
Compatible with i.MX 8M Plus (CPU), i.MX 93 (Ethos-U65 NPU), i.MX 95 and i.MX 952 (Neutron NPU)
Model Description
Modifications
No modifications were made to the original model weights or quantization. The int8 TFLite model is downloaded directly from TensorFlow Hub and compiled for each target platform.
Model Information
Information
Value
Input shape
RGB image (1, 513, 513, 3)
Input value range
[-1.0, 1.0] (normalized as (pixel - 127.5) / 127.5)
Output shape
(1, 513, 513, 21) - per-pixel scores for 21 classes
Output example
FLOPS
1.76B OPS
File size (int8)
~983 KB
Source framework
TensorFlow / TensorFlow Lite
Target platform
MPUs
Tested Configurations
The int8 model has been tested on the following configurations using benchmark_model:
Platform
BSP / SDK
Accelerator
i.MX 8M Plus
LF6.1.22_2.0.0
CPU, NPU
i.MX 93
lf-6.18.20_2.0.0
CPU, NPU
i.MX 95
eIQ Neutron SDK 3.1.3
CPU, NPU
i.MX 952
eIQ Neutron SDK 3.1.3
CPU, NPU
Training and Evaluation
The model was trained and evaluated on the PASCAL VOC dataset [1], which contains 20 object classes.
It achieved 70.19% mIoU on the test set according to the TensorFlow Model Zoo.
[1] Everingham, Mark, et al. "The pascal visual object classes (voc) challenge." International journal of computer vision 88 (2010): 303-338.
[2] Chen, Liang-Chieh, et al. "Encoder-decoder with atrous separable convolution for semantic image segmentation." Proceedings of the European conference on computer vision (ECCV). 2018.
[3] Sandler, Mark, et al. "Mobilenetv2: Inverted residuals and linear bottlenecks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
