Open solution to the Google AI Object Detection Challenge :maple_leaf:
This is an open solution to the Google AI Open Images - Object Detection Track :smiley:
Check collection of public projects :gift:, where you can find multiple Kaggle competitions with code, experiments and outputs.
We are building entirely open solution to this competition. Specifically:
UNet training monitor :bar_chart: | Predicted bounding boxes :bar_chart: |
---|---|
In this open source solution you will find references to the neptune.ml. It is free platform for community Users, which we use daily to keep track of our experiments. Please note that using neptune.ml is not necessary to proceed with this solution. You may run it as plain Python script :snake:.
link to code | link to description |
---|---|
solution-1 | palm-tree :palm_tree: |
This competition is special, because it used Open Images Dataset V4, which is quite large: >1.8M
images and >0.5TB
:astonished: To make it more approachable, we are hosting entire dataset in the neptune's public directory :sunglasses:. You can use this dataset in neptune.ml with no additional setup :+1:.
You can jump start your participation in the competition by using our starter pack. Installation instruction below will guide you through the setup.
pip3 install -r requirements.txt
:hamster:
neptune send --worker m-4p100 \
--environment pytorch-0.3.1-gpu-py3 \
--config configs/neptune.yaml \
main.py train --pipeline_name retinanet
:trident:
neptune run main.py train --pipeline_name retinanet
:snake:
python main.py -- train --pipeline_name retinanet
Note in case of memory trouble go to neptune.yaml
and change batch_size_inference: 1
:hamster:
With cloud environment you need to change the experiment directory to the one that you have just trained. Let's assume that your experiment id was GAI-14
. You should go to neptune.yaml
and change:
experiment_dir: /output/experiment
clone_experiment_dir_from: /input/GAI-14/output/experiment
neptune send --worker m-4p100 \
--environment pytorch-0.3.1-gpu-py3 \
--config configs/neptune.yaml \
--input /GAI-14 \
main.py evaluate_predict --pipeline_name retinanet --chunk_size 100
:trident:
neptune run main.py train --pipeline_name retinanet --chunk_size 100
:snake:
python main.py -- train --pipeline_name retinanet --chunk_size 100
You are welcome to contribute your code and ideas to this open solution. To get started:
There are several ways to seek help: