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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, quantization, filter pruning and quantization with sparsity
  • Export to ONNX compatible with OpenVINO (compatible with pre-shipped CPU extensions detection layers)
  • DataParallel and DistributedDataParallel modes
  • Tensorboard output

Installation

At this point it is assumed that you have already installed nncf. You can find information on downloading nncf here.

To work with the sample you should install the corresponding Python package dependencies:

pip install -r examples/torch/requirements.txt

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

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/torch/object_detection folder
  • (Optional) Before compressing a model, it is highly recommended checking the accuracy of the pretrained model, use the following command: 
    python main.py \
--mode=test \
--config=configs/ssd300_vgg_voc_int8.json \
--data=<path_to_dataset> \
--disable-compression
  • Run the following command to start compression with fine-tuning on GPUs: python main.py -m train --config configs/ssd300_vgg_voc_int8.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.
  • Use the --weights flag with the path to a compatible PyTorch checkpoint in order to load all matching weights from the checkpoint into the model - useful if you need to start compression-aware training from a previously trained uncompressed (FP32) checkpoint instead of performing compression-aware training fr om scratch.

Validate your model checkpoint

To estimate the test scores of your trained model checkpoint use the following command: python main.py -m test --config=configs/ssd300_vgg_voc_int8.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.

WARNING: The samples use torch.load functionality for checkpoint loading which, in turn, uses pickle facilities by default which are known to be vulnerable to arbitrary code execution attacks. Only load the data you trust

Export compressed model

To export trained model to ONNX format use the following command: python main.py -m export --config configs/ssd300_vgg_voc_int8.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 mAP (drop) % NNCF config file PyTorch checkpoint
SSD300-MobileNet None VOC12+07 train, VOC07 eval 62.23 ssd300_mobilenet_voc.json Link
SSD300-MobileNet INT8 + Sparsity 70% (Magnitude) VOC12+07 train, VOC07 eval 62.94 (-0.71) ssd300_mobilenet_voc_magnitude_int8.json Link
SSD300-VGG-BN None VOC12+07 train, VOC07 eval 78.28 ssd300_vgg_voc.json Link
SSD300-VGG-BN INT8 VOC12+07 train, VOC07 eval 77.96 (0.32) ssd300_vgg_voc_int8.json Link
SSD300-VGG-BN INT8 + Sparsity 70% (Magnitude) VOC12+07 train, VOC07 eval 77.59 (0.69) ssd300_vgg_voc_magnitude_sparsity_int8.json Link
SSD512-VGG-BN None VOC12+07 train, VOC07 eval 80.26 ssd512_vgg_voc.json Link
SSD512-VGG-BN INT8 VOC12+07 train, VOC07 eval 80.12 (0.14) ssd512_vgg_voc_int8.json Link
SSD512-VGG-BN INT8 + Sparsity 70% (Magnitude) VOC12+07 train, VOC07 eval 79.67 (0.59) ssd512_vgg_voc_magnitude_sparsity_int8.json Link

Results for filter pruning

Model Compression algorithm Dataset mAP (drop) % GFLOPS MParams NNCF config file PyTorch checkpoint
SSD300-VGG-BN None VOC12+07 train, VOC07 eval 78.28 61.1 (100%) 26.3 (100%) Link Link
SSD300-VGG-BN Filter pruning 40%,
geometric median criterion		 VOC12+07 train, VOC07 eval 77.72 (0.56) 25.8 (42.23%) 11.4 (43.35%) Link Link