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Object Detection sample

This sample demonstrates DL model compression capabailites for object detection task.

Features:

  • Vanilla SSD300 / SSD512 (+ Batch Normalization), MobileNetSSD-300
  • VOC2007 / VOC2012, COCO datasets
  • Configuration file examples for sparsity and quantization
  • Export to ONNX compatible with OpenVINO (compatible with pre-shipped CPU extensions detection layers)
  • DataParallel and DistributedDataParallel modes
  • Tensorboard output

Quantize FP32 pretrained model

This scenario demonstrates quantization with fine-tuning of SSD300 on VOC dataset.

Dataset preparation

  • Download and extract VOC2007 and VOC2012 train/val and test data + devkit from here and here

Run object detection sample

  • If you did not install the package then add the repository root folder to the PYTHONPATH environment variable
  • Navigate to the examples/object_detection folder
  • Run the following command to start compression with fine-tuning on GPUs: python main.py -m train --config configs/ssd300_vgg_int8_voc.json --data <path_to_dataset> --log-dir=../../results/quantization/ssd300_int8 It may take a few epochs to get the baseline accuracy results.
  • Use --multiprocessing-distributed flag to run in the distributed mode.
  • Use --resume flag with the path to a previously saved model to resume training.

Validate your model checkpoint

To estimate the test scores of your model checkpoint use the following command: python main.py -m test --config=configs/ssd300_vgg_int8_voc.json --data <path_to_dataset> --resume <path_to_trained_model_checkpoint> If you want to validate an FP32 model checkpoint, make sure the compression algorithm settings are empty in the configuration file or pretrained=True is set.

Export compressed model

To export trained model to ONNX format use the following command: python main.py -m test --config configs/ssd300_vgg_int8_voc.json --data <path_to_dataset> --resume <path_to_compressed_model_checkpoint> --to-onnx=../../results/ssd300_int8.onnx

Export to OpenVINO Intermediate Representation (IR)

To export a model to OpenVINO IR and run it using Intel Deep Learning Deployment Toolkit please refer to this tutorial.

Results

Model Compression algorithm Dataset PyTorch compressed accuracy Config path PyTorch checkpoint
SSD300-BN None VOC12+07 78.28 examples/object_detection/configs/ssd300_vgg_voc.json Link
SSD300-BN INT8 VOC12+07 78.08 examples/object_detection/configs/ssd300_vgg_voc_int8.json Link
SSD300-BN INT8 + Sparsity 70% (Magnitude) VOC12+07 77.62 examples/object_detection/configs/ssd300_vgg_voc_magnitude_sparsity_int8.json Link
SSD512-BN None VOC12+07 80.26 examples/object_detection/configs/ssd512_vgg_voc.json Link
SSD512-BN INT8 VOC12+07 80.11 examples/object_detection/configs/ssd512_vgg_voc_int8.json Link
SSD512-BN INT8 + Sparsity 70% (Magnitude) VOC12+07 79.75 examples/object_detection/configs/ssd512_vgg_voc_magnitude_sparsity_int8.json Link