metadata
library_name: pytorch
DeepLabV3+ extends atrous convolution–based semantic segmentation with an encoder–decoder structure that refines object boundaries while preserving rich contextual information.
Original paper: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation (DeepLabV3+)
DeepLabV3Plus-ResNet50
This model uses DeepLabV3+ with a ResNet-50 backbone, combining multi-scale context aggregation from atrous spatial pyramid pooling (ASPP) with a lightweight decoder for sharper segmentation outputs. It is well suited for semantic segmentation tasks in applications such as autonomous driving, robotics, and scene understanding, where accuracy and robustness are critical.
Model Configuration:
- Reference implementation: DeepLabV3Plus_ResNet50_v1
- Original Weight: DeepLabV3Plus_ResNet50_Weights.Pascal_VOC2012_Aug
- Resolution: 3x513x513
- Support Cooper version:
- Cooper SDK: [2.5.3]
- Cooper Foundry: [2.2]
| Model | Device | compression | Model Link |
|---|---|---|---|
| DeepLabV3Plus-ResNet50 | N1-655 | Amba_optimized | Model_Link |
| DeepLabV3Plus-ResNet50 | N1-655 | Activation_fp16 | Model_Link |
| DeepLabV3Plus-ResNet50 | CV7 | Amba_optimized | Model_Link |
| DeepLabV3Plus-ResNet50 | CV7 | Activation_fp16 | Model_Link |
| DeepLabV3Plus-ResNet50 | CV72 | Amba_optimized | Model_Link |
| DeepLabV3Plus-ResNet50 | CV72 | Activation_fp16 | Model_Link |
| DeepLabV3Plus-ResNet50 | CV75 | Amba_optimized | Model_Link |
| DeepLabV3Plus-ResNet50 | CV75 | Activation_fp16 | Model_Link |