Image Segmentation: Tips and Tricks from 39 Kaggle Competitions
Senior data scientist building experiment tracking tools for ML projects at https://neptune.ai
Imagine if you could get all the tips and tricks you need to hammer a Kaggle competition. I have gone over 39 Kaggle competitions including
- Data Science Bowl 2017 – $1,000,000
- Intel & MobileODT Cervical Cancer Screening – $100,000
- 2018 Data Science Bowl – $100,000
- Airbus Ship Detection Challenge – $60,000
- Planet: Understanding the Amazon from Space – $60,000
- APTOS 2019 Blindness Detection – $50,000
- Human Protein Atlas Image Classification – $37,000
- SIIM-ACR Pneumothorax Segmentation – $30,000
- Inclusive Images Challenge – $25,000
– and extracted that knowledge for you. Dig in.
- External Data Preprocessing
- Data Augmentations
- Hardware Setups
- Loss Functions
- Training Tips
- Evaluation and Cross-validation
- Ensembling Methods
- Post Processing
Data Exploration and Gaining insights
- Use of the AWS GPU instance p2.xlarge with a NVIDIA K80 GPU
- Pascal Titan-X GPU
- Use of 8 TITAN X GPUs
- 6 GPUs: 21080Ti + 41080
- Server with 8×NVIDIA Tesla P40, 256 GB RAM and 28 CPU cores
- Intel Core i7 5930k, 2×1080, 64 GB of RAM, 2x512GB SSD, 3TB HDD
- GCP 1x P100, 8x CPU, 15 GB RAM, SSD or 2x P100, 16x CPU, 30 GB RAM
- NVIDIA Tesla P100 GPU with 16GB of RAM
- Intel Core i7 5930k, 2×1080, 64 GB of RAM,
- 2x512GB SSD, 3TB HDD980Ti GPU, 2600k CPU, and 14GB RAM
Evaluation and cross-validation
Hopefully, this article gave you some background into image segmentation tips and tricks and given you some tools and frameworks that you can use to start competing.
We’ve covered tips on:
- training tricks,
- post processing
- ensemblingtools and frameworks.
If you want to go deeper down the rabbit hole, simply follow the links and see how the best image segmentation models are built.
You can also find me tweeting @Neptune_ai or posting on Linkedin about ML and Data Science stuff.
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