YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design
Authors: Yuxuan Cai, Hongjia Li, Geng Yuan, Wei Niu, Yanyu Li, Xulong Tang, Bin Ren, Yanzhi Wang955-963
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results indicate that our pruning scheme achieves 14 compression rate of YOLOv4 with 49.0 m AP. Under our YOLObile framework, we achieve 17 FPS inference speed using GPU on Samsung Galaxy S20. By incorporating our proposed GPU-CPU collaborative scheme, the inference speed is increased to 19.1 FPS, and outperforms the original YOLOv4 by 5 speedup. |
| Researcher Affiliation | Academia | Yuxuan Cai1 , Hongjia Li1 , Geng Yuan1 , Wei Niu2, Yanyu Li1, Xulong Tang3, Bin Ren2, Yanzhi Wang1 1Northeastern University 2William & Mary 3University of Pittsburgh |
| Pseudocode | No | The paper includes a section on the 'Reweighted Regularization Pruning Algorithm' which describes the algorithm mathematically, but it does not present it in a structured pseudocode or algorithm block format. |
| Open Source Code | Yes | Source code is at: https://github.com/nightsnack/YOLObile. |
| Open Datasets | Yes | Our YOLObile is derived based on YOLOv4, with 320 320 input size, and train on MS COCO dataset (Lin et al. 2014). |
| Dataset Splits | No | The paper mentions training on the MS COCO dataset but does not specify the validation split used, either by percentage, sample count, or a reference to predefined validation sets. |
| Hardware Specification | Yes | Our models are trained on a server with eight NVIDIA RTX 2080Ti GPUs. ... We evaluate our framework on an off-the-shelf Samsung Galaxy S20 smartphone, which has a Qualcomm Snapdragon 865 Octa-core CPU and a Qualcomm Adreno 650 GPU. |
| Software Dependencies | No | The paper states 'The training methods are implemented using Py Torch API.' but does not provide specific version numbers for PyTorch or any other software dependencies crucial for reproduction. |
| Experiment Setup | Yes | Our YOLObile is derived based on YOLOv4, with 320 320 input size, and train on MS COCO dataset (Lin et al. 2014). ... We adopt 8 4 as our block size, i.e. 4 consecutive channels of 8 consecutive filters. |