rotation prediction

**Rotation Prediction** is an **early self-supervised pretext task where the model is trained to predict which rotation (0°, 90°, 180°, 270°) was applied to an input image** — requiring the network to learn meaningful visual features (object orientation, shape, semantics) to solve the task. **How Does Rotation Prediction Work?** - **Process**: Randomly rotate each image by 0°, 90°, 180°, or 270°. The network must classify which rotation was applied. - **Labels**: Free (generated by the augmentation, no human annotation needed). - **Architecture**: Standard CNN (e.g., ResNet) + 4-class classification head. - **Paper**: RotNet (Gidaris et al., 2018). **Why It Matters** - **Simplicity**: One of the simplest and most effective early pretext tasks. - **Insight**: To predict rotation, the network must understand "up" vs. "down" and object semantics — non-trivial! - **Legacy**: Largely superseded by contrastive methods (SimCLR, MoCo, DINO) but remains a pedagogical benchmark. **Rotation Prediction** is **the compass test for neural networks** — a deceptively simple pretext task that requires genuine visual understanding to solve.