Skip to content

Latest commit

 

History

History
140 lines (127 loc) · 4.16 KB

RetinaNet.md

File metadata and controls

140 lines (127 loc) · 4.16 KB
Raw

TensorFlow RetinaNet

Setup AI Model Efficiency Toolkit (AIMET)

Please install and setup AIMET before proceeding further. This evaluation was run using AIMET 1.22.2 for TensorFlow 1.15 i.e. please set release_tag="1.22.2" and AIMET_VARIANT="tf_gpu_tf115" in the above instructions.

Environment Requirements

This model requires the following python package versions:

pip install tensorflow-gpu==1.15.0  
pip install keras==2.2.4
pip install progressbar2>=4.0.0
pip install --user git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI

It also required libGL1:

sudo apt update
sudo apt-get install libgl1 -y

Note that this model is expected not to work with GPUs at or after NVIDIA 30-series (e.g. RTX 3050), as those bring a new architecture not fully compatible with TF 1.X

Setup RetinaNet Repo

  • Clone the RetinaNet repository from github: https://github.com/fizyr/keras-retinanet
    git clone https://github.com/fizyr/keras-retinanet.git
    cd keras-retinanet

  • Within the cloned repository, checkout the commit corresponding to pre-tf2.0. The included example scripts only works for TF 1.x.
    git checkout 08af308d01a8f22dc286d62bc26c8496e1ff6539

  • Install keras-retinanet and dependencies using by running:
    pip install . --user

Add AIMET Model Zoo to the Python Path

export PYTHONPATH=$PYTHONPATH:<path to parent>/aimet-model-zoo

Dataset

Model Weights

Usage

The evaluation script supports 4 actions: evaluating the original model on GPU ("original_fp32"); evaluating the original model on a simulated device ("original_int8"); evaluating the optimized model on GPU ("optimized_fp32"); evaluating the optimized model on a simulated device ("optimized_int8").

python3 retinanet_quanteval.py \
        --dataset-Path <path to location of coco dataset> \
        --action <one of: original_fp32, original_int8, optimized_fp32, optimized_int8>

Quantization Configuration

  • Weight quantization: 8 bits, per tensor asymmetric quantization
  • Bias parameters are quantized
  • Activation quantization: 8 bits, asymmetric quantization

Results

(COCO dataset)

Average Precision/Recall @[ IoU | area | maxDets] FP32 INT8
Average Precision @[ 0.50:0.95 | all | 100 ] 0.350 0.349
Average Precision @[ 0.50 | all | 100 ] 0.537 0.536
Average Precision @[ 0.75 | all | 100 ] 0.374 0.372
Average Precision @[ 0.50:0.95 | small | 100 ] 0.191 0.187
Average Precision @[ 0.50:0.95 | medium | 100 ] 0.383 0.381
Average Precision @[ 0.50:0.95 | large | 100 ] 0.472 0.472
Average Recall @[ 0.50:0.95 | all | 1 ] 0.306 0.305
Average Recall @[0.50:0.95 | all | 10 ] 0.491 0.490
Average Recall @[ 0.50:0.95 | all |100 ] 0.533 0.532
Average Recall @[ 0.50:0.95 | small | 100 ] 0.345 0.341
Average Recall @[ 0.50:0.95 | medium | 100 ] 0.577 0.577
Average Recall @[ 0.50:0.95 | large | 100 ] 0.681 0.679