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【Hackathon 5th No.70】DET重点模型支持实例分割 #652
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参考已有的`ppdet/modeling/transformers/mask_dino_transformer.py`,复用maskdino中的分割模块实现,少量修改`ppdet/modeling/transformers/rtdetr_transformer.py`,使得`RTDETRTransformer`可以通过`with_mask`参数来控制是否使用分割头。 | ||
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# 六、测试和验收的考量 | ||
PP-YOLO-E+_crn_l、RT-DETR-L模型在COCO数据集的实例分割任务上的精度优于YOLOv8-L。 |
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这里需要增加打通部署功能(动态图和静态图,可保存结果),且给出V100或T4上和其他模型的推理时间对比,增加相关配置、文档等
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好的,已更新
# 六、测试和验收的考量 | ||
1. PP-YOLO-E+_crn_l、RT-DETR-L模型在COCO数据集的实例分割任务上的精度优于YOLOv8-L | ||
2. PP-YOLO-E+_crn_l、RT-DETR-L对应的实例分割模型能够进行动态图和静态图模式下的推理部署 | ||
3. 补全文档,其中包括PP-YOLO-E+_crn_l、RT-DETR-L在V100或T4上和其他模型的推理时间、FLOGS和参数量等的对比, 相关配置文件的使用, 如训练,评估和推理等等。 |
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FLOPs
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已更正
# 八、排期规划 | ||
于2023年10月3日前,完成RT-DETR-L实例分割支持并在COCO上训练完成。 | ||
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于2023年10月7日前,完成PP-YOLO-E+_crn_l实例分割支持并在COCO上训练完成。 |
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最终的完成时间也补充下吧
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好的,已更新
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LGTM
* 【Hackathon 5th No.70】DET重点模型支持实例分割 * 更新 * 更新 * 更新
新增 DET重点模型支持实例分割 设计文档
PaddlePaddle/Paddle#57262