micronet challenge

**MicroNet Challenge** is a **benchmark competition that challenges researchers to design the most efficient neural networks for specific tasks under extreme parameter and computation budgets** — pushing the limits of model compression, efficient architecture design, and neural network efficiency. **Challenge Constraints** - **Parameter Budget**: Strict maximum number of parameters (e.g., <1M parameters for CIFAR-100). - **FLOP Budget**: Strict maximum computation (e.g., <12M multiply-adds for CIFAR-100). - **Scoring**: Models are scored on accuracy relative to a baseline at the given budget — higher is better. - **Tasks**: Typically image classification benchmarks (CIFAR-10, CIFAR-100, ImageNet). **Why It Matters** - **Efficiency Research**: Drives innovation in model efficiency — pruning, quantization, efficient architectures. - **Real-World**: Extremely small models are needed for MCU-class edge devices (kilobyte-scale memory). - **Benchmarking**: Provides a standardized comparison framework for model efficiency techniques. **MicroNet Challenge** is **the efficiency Olympics for neural networks** — competing to build the most accurate models under extreme size and computation constraints.