TYUST RAICOM2026 CAIP

--- license: mit pipeline_tag: image-classification --- # WeatherNet-RAICOM2026 > **Official solution for the RAICOM 2026 CAIP Weather Classification Challenge** ## 🏆 Competition Achievement 🥉 **Provincial Third Prize — RAICOM Cup 2026 (睿抗杯)** **CAIP AI Algorithm Optimization Track** The proposed **WeatherNet-RAICOM2026** solution achieved **Third Prize (Provincial Level)** in the **RAICOM 2026 Developer Competition – CAIP AI Algorithm Optimization Track**. --- ## Overview WeatherNet-RAICOM2026 is the solution developed for the **RAICOM 2026 Developer Competition – CAIP (AI Algorithm Optimization Track)**. The task aims to classify weather conditions from images into four categories: * ☀️ Sunny * ☁️ Cloudy * 🌧️ Rainy * ❄️ Snowy This repository provides the trained model weights and inference implementation based entirely on **PyTorch** and **Torchvision**. --- ## Model Architecture The final solution adopts a lightweight ensemble framework consisting of two complementary CNN backbones: | Backbone | Input Resolution | | ---------------- | ---------------- | | EfficientNetV2-S | 320 / 352 | | ConvNeXt-Tiny | 256 / 288 | The predictions from both models are fused using weighted soft-voting. --- ## Inference Pipeline The inference pipeline consists of several optimization strategies: * Dual-model ensemble * Multi-scale inference * Horizontal Flip Test-Time Augmentation (TTA) * Weighted probability fusion * Macro-F1 score bias calibration The entire inference pipeline is implemented using only **torchvision**, without additional third-party model libraries. --- ## Project Structure ```text . ├── main.py ├── results │ ├── ev2s.pth │ └── cvt.pth └── README.md

Source context: 0 downloads · 0 likes · Pipeline image-classification · Repo l1ber0/TYUST-RAICOM2026-CAIP