Voc Seg Efficient

--- language: en license: mit tags: - image-segmentation - semantic-segmentation - pytorch - pascal-voc - mobilenet - lraspp - optuna datasets: - pascal-voc-2012 metrics: - dice - mean_iou --- # VOC Semantic Segmentation — EfficientSegNet (MobileNetV3-Large + LR-ASPP) Semantic segmentation model trained on Pascal VOC 2012. Architecture: **MobileNetV3-Large + LR-ASPP** (pretrained COCO backbone). Optimised with **Optuna** TPE-sampler HPO. ## Metrics | Metric | Value | |--------|-------| | Macro DICE | 0.7645 | | FLOPs/image | 7.3561e+08 | | DICE / GFLOPs | 1.0392 | | Parameters | 3.22M | ## Best Hyperparameters (from Optuna HPO) | Hyperparameter | Value | |---------------|-------| | — | N/A (direct training) | ## Training Details - Dataset: Pascal VOC 2012 (train split, 80/20 train/val) - Image size: 300×300 - Loss: Combined CrossEntropy + Dice Loss - Optimizer: AdamW + CosineAnnealingLR - AMP: Mixed precision (PyTorch 2.x) - Augmentation: HFlip, Rotation, Gaussian noise / blur / salt-pepper / brightness ## VOC Classes `background, aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, diningtable, dog, horse, motorbike, person, pottedplant, sheep, sofa, train, tvmonitor` ## Usage ```python import torch from model import EfficientSegNet from config import Config model = EfficientSegNet(pretrained=False).to(Config.DEVICE) ckpt = torch.load("best_model.pth", map_location=Config.DEVICE, weights_only=True) model.load_state_dict(ckpt["model_state"]) model.eval() ``` --- *Trained: 2026-03-07 19:25*

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