Object Detection (Maskrcnn)¶
The benchmark reference for Object Detection (Maskrcnn) can be found in this link, and here is the PR for the minified benchmark implementation: link.
Project setup¶
# Create Python environment and install MLCube Docker runner
virtualenv -p python3 ./env && source ./env/bin/activate && pip install pip==24.0 && pip install mlcube-docker
# Fetch the Object Detection workload
git clone https://github.com/mlcommons/training && cd ./training
git fetch origin pull/501/head:feature/object_detection && git checkout feature/object_detection
cd ./object_detection/mlcube
Dataset¶
The COCO dataset will be downloaded and extracted. Sizes of the dataset in each step:
| Dataset Step | MLCube Task | Format | Size |
|---|---|---|---|
| Download (Compressed dataset) | download_data | Tar/Zip files | ~20.5 GB |
| Extract (Uncompressed dataset) | download_data | Jpg/Json files | ~21.2 GB |
| Total | (After all tasks) | All | ~41.7 GB |
Tasks execution¶
Parameters are defined at these files:
- MLCube user parameters: mlcube/workspace/parameters.yaml
- Project user parameters: pytorch/configs/e2e_mask_rcnn_R_50_FPN_1x.yaml
- Project default parameters: pytorch/maskrcnn_benchmark/config/defaults.py
# Download COCO dataset. Default path = /workspace/data
mlcube run --task=download_data -Pdocker.build_strategy=always
# Run benchmark. Default paths = ./workspace/data
mlcube run --task=train -Pdocker.build_strategy=always
Demo execution¶
These tasks will use a demo dataset (39M) to execute a faster training workload for a quick demo (~12 min):
# Download subsampled dataset. Default path = /workspace/demo
mlcube run --task=download_demo -Pdocker.build_strategy=always
# Run benchmark. Default paths = ./workspace/demo and ./workspace/demo_output
mlcube run --task=demo -Pdocker.build_strategy=always
It's also possible to execute the two tasks in one single instruction:
mlcube run --task=download_demo,demo -Pdocker.build_strategy=always
Additional options¶
Parameters defined at mculbe/mlcube.yaml could be overridden using: --param=input
We are targeting pull-type installation, so MLCube images should be available on docker hub. If not, try this:
mlcube run ... -Pdocker.build_strategy=always