YOLOv3: Sparsifying to Improve Object Detection Performance

Neural Magic creates models and recipes that allow anyone to plug in their data and leverage SparseML’s recipe-driven approach on top of Ultralytics’ robust training pipelines for the popular YOLOv3 object detection network. Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and quantization, among others. This sparsification process results in faster inference and smaller file sizes for deployments.

This page walks through the following use cases for trying out the sparsified YOLOv3 models:

• Run the models for inference in deployment or applications

• Train the models on new datasets

• Download the models from the SparseZoo

• Benchmark the models in the DeepSparse Engine

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Run

Example Application

Example Deployment

Train

Applying a Recipe

Download

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The model stubs from the above table can be used with the SparseZoo Python API for downloading:

from sparsezoo.models import Zoo
 
# change out the stub variable from the above table to download the desired model
stub = "zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94"
model = Zoo.download_model_from_stub(stub, override_parent_path="downloads")

# Prints the download path of the model
print(model.dir_path)

Benchmark

Batch size 1 performance comparisons for YOLOv3 on common deployments. Click the chart or here for more detailed information.

Benchmarks within the DeepSparse Engine can be run by using the appropriate stub for each model with the following code:

from sparsezoo.models import Zoo
from deepsparse import compile_model

batch_size = 1
stub = "zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94"

# Download model and compile as optimized executable for your machine
model = Zoo.download_model_from_stub(stub, override_parent_path="downloads")
engine = compile_model(model, batch_size=batch_size)

# Fetch sample input and run a benchmark
inputs = model.data_inputs.sample_batch(batch_size=batch_size)
benchmarks = engine.benchmark(inputs)
print(benchmarks)