Winning Solution (1st out of 735) in Kaggle: Carvana Image Masking Challenge

Sik-Ho Tsang

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Sep 10, 2023

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TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation
TernausNet, by Lyft Inc., and Massachusetts Institute of Technology
2018 arXiv v1, Over 650 Citations (Sik-Ho Tsang @ Medium)

Image Segmentation
2014 … 2022 [YOLACT++] 2023 [Segment Anthing Model (SAM)]

• Classic U-Net is trained from scratch. In this paper, VGG11 pretrained encoder is used as U-Net encoder.
• This network architecture was a part of the winning solution (1st out of 735) in the Kaggle: Carvana Image Masking Challenge.

Outline

1. TernausNet
2. Results

1. TernausNet

• VGG11 is used as U-Net encoder backbone.
• Three weight initialization schemes are compared: LeCun uniform, the encoder with weights from VGG11 and full network trained on the Carvana dataset.

2. Results

• Jaccard index (Intersection Over Union) is used as evaluation metric.

Validation learning curves in Fig. 3 show benefits of the pretraining approach.

• After 100 epochs, for validation subset:

1. LeCun uniform initializer: IoU = 0.593
2. The Encoder is pre-trained on ImageNet: IoU = 0.686
3. Fully pre-trained U-Net on Carvana: IoU = 0.687

Pretrained models obtain better segmentation results.