FFNet-150S: Semantic Segmentation
FFNets are families of Simple and Efficient Architectures, which demonstrate the effectiveness of for the task of Semantic Image Segmentation. The model definitions and pre-trained weights for the models introduced in the paper Simple and Efficient Architectures for Semantic Segmentation, published at the Efficient Deep Learning for Computer Vision Workshop at CVPR 2022. FFNet stands for "Fuss-Free Networks", and utilize a simple ResNet-like backbone, and a tiny convolution-only head to produce multi-scale features that are useful for various tasks.
Source model
- Input shape: 1x3x512x1024
- Number of parameters: 48M
- Model size: 184MB
- Output shape: 1x19x128x256
Source model repository: FFNet-150S
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License
Source Model: BSD-3-CLAUSE
Deployable Model: APLUX-MODEL-FARM-LICENSE