ins_seg分支YOLOv5、v8和PPYOLOE seg目前支持训练了吗

[panp4n]

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请问ins_seg分支添加的YOLOv5、v8和PPYOLOE seg目前支持训练了吗，是否支持Fastdeploy部署 @nemonameless

[nemonameless]

PPYOLOE seg可训，其余暂不可训。暂未支持Fastdeploy。

[panp4n]

PPYOLOE seg可训，其余暂不可训。暂未支持Fastdeploy。

我尝试了PPYOLOE-seg（640640）训练自己的数据集，检测效果良好，但分割掩膜有明显的锯齿，尤其是小目标，且评估segm, mAP异常。 同样的数据集使用MaskRCNN（8001280）检测分割效果要好的多，且segm mAP正常。 以下为PPYOLOE-seg验证Log:

DONE (t=0.01s) creating index... index created! Running per image evaluation... Evaluate annotation type bbox DONE (t=0.23s). Accumulating evaluation results... DONE (t=0.03s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.716 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.909 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.816 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.224 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.664 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.762 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.103 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.548 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.787 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.350 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.748 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.817 [12/15 17:56:40] ppdet.metrics.metrics INFO: The mask result is saved to mask.json. loading annotations into memory... Done (t=0.00s) creating index... index created! [12/15 17:56:40] ppdet.metrics.coco_utils INFO: Start evaluate... Loading and preparing results... DONE (t=0.03s) creating index... index created! Running per image evaluation... Evaluate annotation type segm DONE (t=0.28s). Accumulating evaluation results... DONE (t=0.02s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.014 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.025 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.017 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.006 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.017 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.013 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.053 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.054 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.056 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.049 [12/15 17:56:40] ppdet.engine INFO: Total sample number: 5, average FPS: 0.500295790076311 [12/15 17:56:40] ppdet.engine INFO: Best test bbox ap is 0.718. 看预测的结果也挺正常，得分都挺高。 另外我发现训练PaddleYOLO中这几个实例分割，显存占用异常大，PPYOLOE-seg-s 640输入尺寸，2 batchsize就要占12G显存。其他YOLOv5、6、8 seg 12G显存只能跑1个batchsize。我在使用YOLOv5-seg\YOLOv8-seg官方代码训练时，可以使用更大的batchsize和更大的输入尺寸（1280），也不会爆显存，且检测和分割效果要好很多。

[nemonameless]

显存问题确实存在，后续有空会继续排查，谢谢