TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation
Abstrak
Pixel-wise image segmentation is demanding task in computer vision. Classical U-Net architectures composed of encoders and decoders are very popular for segmentation of medical images, satellite images etc. Typically, neural network initialized with weights from a network pre-trained on a large data set like ImageNet shows better performance than those trained from scratch on a small dataset. In some practical applications, particularly in medicine and traffic safety, the accuracy of the models is of utmost importance. In this paper, we demonstrate how the U-Net type architecture can be improved by the use of the pre-trained encoder. Our code and corresponding pre-trained weights are publicly available at this https URL. We compare three weight initialization schemes: LeCun uniform, the encoder with weights from VGG11 and full network trained on the Carvana dataset. This network architecture was a part of the winning solution (1st out of 735) in the Kaggle: Carvana Image Masking Challenge.
Topik & Kata Kunci
Penulis (2)
V. Iglovikov
Alexey A. Shvets
Akses Cepat
PDF tidak tersedia langsung
Cek di sumber asli →- Tahun Terbit
- 2018
- Bahasa
- en
- Total Sitasi
- 666×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1007/978-3-030-64340-9_15
- Akses
- Open Access ✓