downstream task

**Downstream Task** is the **target task that a pre-trained model is applied to after self-supervised or supervised pre-training** — used to evaluate the quality of learned representations and measure how well the pre-trained features transfer to practical applications. **What Is a Downstream Task?** - **Examples**: Image classification (ImageNet), object detection (COCO), semantic segmentation (ADE20K), action recognition, medical imaging. - **Evaluation Protocol**: Freeze pre-trained backbone -> train a task-specific head (linear probe or fine-tuning). - **Metric**: Performance on the downstream task benchmarks the representation quality. **Why It Matters** - **Representation Benchmark**: Downstream task performance is the ultimate test of self-supervised learning methods. - **Transfer Learning**: Good representations transfer to many downstream tasks, even with limited labeled data. - **Practical Value**: The pre-trained model's usefulness is entirely determined by how well it performs on real downstream tasks. **Downstream Task** is **the final exam for pre-trained models** — the real-world challenge that determines whether the learned representations are actually useful.