We lifted 5 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| xyonglu/sbcformer | canonical | 5 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| conv_1x1_bn | Ran | xyonglu/sbcformer/models/mobilenetv2.py code served (permissive licence) · get_code("a0131fb70c267a9e") |
| conv_3x3_bn | Ran | xyonglu/sbcformer/models/mobilenetv2.py code served (permissive licence) · get_code("408e747e0425a594") |
| mixup_target | Ran | xyonglu/sbcformer/mixup.py code served (permissive licence) · get_code("8733e0d87f232d11") |
| one_hot | Ran | xyonglu/sbcformer/mixup.py code served (permissive licence) · get_code("4728dc2025227bba") |
| rand_bbox | Ran | xyonglu/sbcformer/mixup.py code served (permissive licence) · get_code("3913c620f6926f42") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Computer vision has become increasingly prevalent in solving real-world problems across diverse domains, including smart agriculture, fishery, and livestock management. These applications may not require processing many image frames per second, leading practitioners to use single board computers (SBCs). Although many lightweight networks have been developed for mobile/edge devices, they primarily target smartphones with more powerful processors and not SBCs with the low-end CPUs. This paper introduces a CNN-ViT hybrid network called SBCFormer, which achieves high accuracy and fast computation on such low-end CPUs. The hardware constraints of these CPUs make the Transformer's attention mechanism preferable to convolution. However, using attention on low-end CPUs presents a challenge: high-resolution internal feature maps demand excessive computational resources, but reducing their resolution results in the loss of local image details. SBCFormer introduces an architectural design to address this issue. As a result, SBCFormer achieves the highest trade-off between accuracy and speed on a Raspberry Pi 4 Model B with an ARM-Cortex A72 CPU. For the first time, it achieves an ImageNet-1K top-1 accuracy of around 80% at a speed of 1.0 frame/sec on the SBC. Code is available at https://github.com/xyongLu/SBCFormer.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2311.03747")
get_code_for_paper("2311.03747")
have("2311.03747")
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