MobileNetV2-SSD-lite.md

PyTorch MobileNetV2-SSD-lite

Environment Setup

Setup AI Model Efficiency Toolkit (AIMET)

Please install and setup AIMET before proceeding further. This model was tested with the torch_gpu variant of AIMET 1.23.

Install dependencies

   python -m pip install pycocotools

Append the repo location to your PYTHONPATH with the following:

export PYTHONPATH=$PYTHONPATH:<path to parent of aimet_model_zoo>

Dataset

Pascal VOC2007 dataset can be downloaded from here:

• http://host.robots.ox.ac.uk/pascal/VOC/voc2007/index.html

---

Usage

To run evaluation with QuantSim in AIMET, use the following

python3  aimet_zoo_torch/ssd_mobilenetv2/evaluators/ssd_mobilenetv2_quanteval.py \
                --model-config <configuration to be tested> \
                --dataset-path <path to the downloaded Pascal dataset, should end in VOCdevkit/VOC2007> 

Available model configurations are:

• ssd_mobilenetv2_w8a8

---

Obtaining model checkpoint

• The original MobileNetV2-SSD-lite checkpoint can be downloaded from here:
  • https://storage.googleapis.com/models-hao/mb2-ssd-lite-mp-0_686.pth
• Optimized checkpoint can be downloaded from the Releases.

---

Quantization Configuration

• Weight quantization: 8 bits, per tensor asymmetric quantization
• Bias parameters are not quantized
• Activation quantization: 8 bits, asymmetric quantization
• Model inputs are quantized
• TF_enhanced was used as quantization scheme
• Cross-layer-Equalization and Adaround have been applied on optimized checkpoint