Mobilenetv2 deeplabv3, Model Garden contains a collection of state-of-the-art models, implemented with TensorFlow's high-level APIs. Apr 24, 2025 · This page focuses on the two backbone options implemented in this codebase - MobileNetV2 and Xception - including their architectures, key features, and implementation details. You can use the raw model for semantic segmentation. The model in this repo adds a DeepLabV3+ head to the MobileNetV2 backbone for semantic segmentation. The model is another Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation (Deeplab-V3+) implementation base on MobilenetV2 / MobilenetV3 on TensorFlow. This model combines the efficiency of MobileNetV2 with the powerful semantic segmentation capabilities of DeepLabV3+. MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature. . MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature. The encoder uses MobileNetV2 with a combination of CBAM for highlighting important spatial and channel features. Oct 14, 2025 · This tutorial trains a DeepLabV3 with Mobilenet V2 as backbone model from the TensorFlow Model Garden package (tensorflow-models). The backbone of MobileNetv2 comes from paper: And the segment head of DeepLabv3 comes from paper: Dec 27, 2025 · The framework is designed on the basis of DeepLabv3 + with attention mechanisms for efficient feature learning in challenging underwater environments. This is a PyTorch implementation of MobileNet v2 network with DeepLab v3 structure used for semantic segmentation. It's specifically designed for mobile and edge devices, offering a careful balance between computational efficiency and segmentation accuracy.


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