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README.md
Exploring DINO: Self-Supervised Transformers for Road Segmentation with ResNet50 and U-Net
This folder contains the Jupyter Notebooks and scripts for the LearnOpenCV article - Exploring DINO: Self-Supervised Transformers for Road Segmentation with ResNet50 and U-Net.
We have provided:
- code to prepare dataset (
data_prep.ipynb) - Model Training Script (
training.ipynb) - Video inference code (
inference.ipynb)
Model Download:
- Download trained model from here and place it under
models/base_line_e50_v2/
Recommended File Structure:
├── data
│ ├── csv
│ ├── idd-segmentation
│ │ └── IDD_Segmentation
│ │ ├── csv
│ │ ├── gtFine
│ │ │ ├── train
│ │ │ └── val
│ │ └── leftImg8bit
│ │ ├── test
│ │ ├── train
│ │ └── val
├── public-code
│ ├── domain_adaptation
│ │ ├── source
│ │ │ └── core
│ │ │ └── csvs
│ │ └── target
│ │ ├── semi-supervised
│ │ └── weakly-supervised
│ ├── evaluation
│ ├── helpers
│ ├── preperation
│ └── viewer
├── models
│ └── base_line_e50_v2
│ └── model_epoch_24.pth
├── training.ipynb
├── data_prep.ipynb
├── inference.ipynb
├── README.md
You can download the trained weights and code files as well from the below link.
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