LiteSeg 是实轻量级语义分割算法，使用的框架是pytorch。

[{"title":"（ 39 个子文件 21.09MB ） LiteSeg 是实轻量级语义分割算法，使用的框架是pytorch。","children":[{"title":"LiteSeg","children":[{"title":"models","children":[{"title":"liteseg_mobilenet.py <span style='color:#111;'> 3.33KB </span>","children":null,"spread":false},{"title":"backbone_networks","children":[{"title":"ShuffleNet.py <span style='color:#111;'> 9.00KB </span>","children":null,"spread":false},{"title":"darknet.py <span style='color:#111;'> 6.76KB </span>","children":null,"spread":false},{"title":"MobileNetV2.py <span style='color:#111;'> 4.42KB </span>","children":null,"spread":false}],"spread":true},{"title":"liteseg_shufflenet.py <span style='color:#111;'> 4.05KB </span>","children":null,"spread":false},{"title":"aspp.py <span style='color:#111;'> 2.02KB </span>","children":null,"spread":false},{"title":"liteseg.py <span style='color:#111;'> 1.13KB </span>","children":null,"spread":false},{"title":"liteseg_darknet.py <span style='color:#111;'> 4.06KB </span>","children":null,"spread":false},{"title":"separableconv.py <span style='color:#111;'> 649B </span>","children":null,"spread":false}],"spread":true},{"title":"convert_to_onnx.py <span style='color:#111;'> 1009B </span>","children":null,"spread":false},{"title":"eval.py <span style='color:#111;'> 2.87KB </span>","children":null,"spread":false},{"title":"运行说明.docx <span style='color:#111;'> 317.62KB </span>","children":null,"spread":false},{"title":"config","children":[{"title":"training.yaml <span style='color:#111;'> 504B </span>","children":null,"spread":false}],"spread":true},{"title":"environment.yml <span style='color:#111;'> 3.16KB </span>","children":null,"spread":false},{"title":"dataloaders","children":[{"title":"augmentation.py <span style='color:#111;'> 10.48KB </span>","children":null,"spread":false},{"title":"utils.py <span style='color:#111;'> 5.90KB </span>","children":null,"spread":false},{"title":"card.py <span style='color:#111;'> 1.10KB </span>","children":null,"spread":false}],"spread":true},{"title":"train.py <span style='color:#111;'> 8.96KB </span>","children":null,"spread":false},{"title":"_config.yml <span style='color:#111;'> 106B </span>","children":null,"spread":false},{"title":"demo.py <span style='color:#111;'> 2.79KB </span>","children":null,"spread":false},{"title":"samples","children":[{"title":"images","children":[{"title":"400001.jpg <span style='color:#111;'> 83.12KB </span>","children":null,"spread":false},{"title":"20034.jpg <span style='color:#111;'> 350.36KB </span>","children":null,"spread":false},{"title":"400034.jpg <span style='color:#111;'> 97.01KB </span>","children":null,"spread":false},{"title":"20005.jpg <span style='color:#111;'> 363.24KB </span>","children":null,"spread":false},{"title":"20001.jpg <span style='color:#111;'> 185.88KB </span>","children":null,"spread":false}],"spread":true},{"title":"predictions","children":[{"title":"400001_liteseg-mobilenet.png <span style='color:#111;'> 338.91KB </span>","children":null,"spread":false},{"title":"20001_liteseg-mobilenet.png <span style='color:#111;'> 811.28KB </span>","children":null,"spread":false},{"title":"400034_liteseg-mobilenet.png <span style='color:#111;'> 408.01KB </span>","children":null,"spread":false},{"title":"20034_liteseg-mobilenet.png <span style='color:#111;'> 1.27MB </span>","children":null,"spread":false},{"title":"20005_liteseg-mobilenet.png <span style='color:#111;'> 1.37MB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"checkpoints","children":[{"title":"LiteSeg-mobilenet-card_epoch-99.pth <span style='color:#111;'> 16.99MB </span>","children":null,"spread":false}],"spread":true},{"title":".gitignore <span style='color:#111;'> 46B </span>","children":null,"spread":false},{"title":"generate_label.py <span style='color:#111;'> 4.10KB </span>","children":null,"spread":false},{"title":"utils","children":[{"title":"loss.py <span style='color:#111;'> 1.07KB </span>","children":null,"spread":false},{"title":"flops_counter.py <span style='color:#111;'> 9.24KB </span>","children":null,"spread":false},{"title":"iou_eval.py <span style='color:#111;'> 2.95KB </span>","children":null,"spread":false}],"spread":false},{"title":"parse_classes.py <span style='color:#111;'> 481B </span>","children":null,"spread":false},{"title":"datasets","children":[{"title":"val.txt <span style='color:#111;'> 165.89KB </span>","children":null,"spread":false},{"title":"train.txt <span style='color:#111;'> 1.46MB </span>","children":null,"spread":false}],"spread":false}],"spread":false}],"spread":true}]