【免费】mmdetection-2.10.0.zip资源-CSDN文库

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《mmdetection-2.10.0：深度学习目标检测框架详解》 mmdetection是基于PyTorch的一个开源目标检测库，它为研究人员和开发者提供了丰富的目标检测算法实现，便于快速实验和开发新的检测模型。在mmdetection-2.10.0版本中，我们能看到该框架的持续优化和新功能的加入，使得目标检测的研究和应用更加便捷。一、mmdetection框架概述 mmdetection是阿里云M6团队维护的项目，它涵盖了多种经典和最新的目标检测算法，如Faster R-CNN、Mask R-CNN、YOLO系列、Deformable DETR等，涵盖了两阶段、单阶段、关键点检测等多种任务。其设计思想是模块化，允许用户灵活组合不同的backbone、neck、head等组件，以实现各种复杂的检测网络结构。二、核心组件解析 1. **Backbone**：通常选用预训练的卷积神经网络，如ResNet、ResNeXt、DenseNet等，用于提取图像特征。 2. **Neck**：包括FPN（Feature Pyramid Network）和NAS-FPN等，目的是将不同层的特征融合，提升检测性能。 3. **Head**：针对不同检测任务，如分类、定位和分割，有不同的设计，如RPN（Region Proposal Network）用于生成候选框，FCOS、ATSS等用于单阶段检测。三、训练与评估 mmdetection提供了一套完整的训练和评估流程，支持多GPU并行训练，可以方便地调整学习率策略、数据增强策略等超参数。同时，内置了COCO、LVIS等数据集的加载器，便于对模型进行验证和测试。四、模型微调与迁移学习 mmdetection支持模型的微调和迁移学习，用户可以利用预训练模型在特定任务上进行再训练，以提高检测效果。此外，框架还提供了模型保存和加载的接口，方便模型的复用和对比实验。五、新特性与优化在2.10.0版本中，可能包含以下更新： - **新算法支持**：可能新增了一些最新的目标检测算法，以保持与研究前沿同步。 - **性能优化**：可能对训练速度、内存占用等方面进行了优化，提升用户体验。 - **API改进**：可能对代码结构和API进行了调整，使代码更易读、易用。 - **文档更新**：可能提供了更详尽的使用指南和教程，帮助用户更好地理解和使用框架。六、应用场景 mmdetection广泛应用于图像分析、视频理解、自动驾驶等领域，对于开发者和研究人员来说，它是快速验证新想法、比较不同模型性能的理想平台。总结，mmdetection-2.10.0作为一款强大的目标检测框架，通过其模块化的设计、丰富的模型库和高效的训练机制，为深度学习领域的研究者和实践者提供了强大工具。无论是学术研究还是工业应用，mmdetection都是值得信赖的选择。

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mmdetection-2.10.0.zip （935个子文件）

make.bat 760B

setup.cfg 462B

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# High-resolution networks (HRNets) for object detection ## Introduction [ALGORITHM] ```latex @inproceedings{SunXLW19, title={Deep High-Resolution Representation Learning for Human Pose Estimation}, author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang}, booktitle={CVPR}, year={2019} } @article{SunZJCXLMWLW19, title={High-Resolution Representations for Labeling Pixels and Regions}, author={Ke Sun and Yang Zhao and Borui Jiang and Tianheng Cheng and Bin Xiao and Dong Liu and Yadong Mu and Xinggang Wang and Wenyu Liu and Jingdong Wang}, journal = {CoRR}, volume = {abs/1904.04514}, year={2019} } ``` ## Results and Models ### Faster R-CNN | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :-------------:|:------:| :------:| :--------:| | HRNetV2p-W18 | pytorch | 1x | 6.6 | 13.4 | 36.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco/faster_rcnn_hrnetv2p_w18_1x_coco_20200130-56651a6d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco/faster_rcnn_hrnetv2p_w18_1x_coco_20200130_211246.log.json) | | HRNetV2p-W18 | pytorch | 2x | 6.6 | | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco/faster_rcnn_hrnetv2p_w18_2x_coco_20200702_085731-a4ec0611.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco/faster_rcnn_hrnetv2p_w18_2x_coco_20200702_085731.log.json) | | HRNetV2p-W32 | pytorch | 1x | 9.0 | 12.4 | 40.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco/faster_rcnn_hrnetv2p_w32_1x_coco_20200130-6e286425.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco/faster_rcnn_hrnetv2p_w32_1x_coco_20200130_204442.log.json) | | HRNetV2p-W32 | pytorch | 2x | 9.0 | | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco/faster_rcnn_hrnetv2p_w32_2x_coco_20200529_015927-976a9c15.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco/faster_rcnn_hrnetv2p_w32_2x_coco_20200529_015927.log.json) | | HRNetV2p-W40 | pytorch | 1x | 10.4 | 10.5 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco/faster_rcnn_hrnetv2p_w40_1x_coco_20200210-95c1f5ce.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco/faster_rcnn_hrnetv2p_w40_1x_coco_20200210_125315.log.json) | | HRNetV2p-W40 | pytorch | 2x | 10.4 | | 42.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco/faster_rcnn_hrnetv2p_w40_2x_coco_20200512_161033-0f236ef4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco/faster_rcnn_hrnetv2p_w40_2x_coco_20200512_161033.log.json) | ### Mask R-CNN | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :-------------:|:------:| :------:|:------:|:--------:| | HRNetV2p-W18 | pytorch | 1x | 7.0 | 11.7 | 37.7 | 34.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco/mask_rcnn_hrnetv2p_w18_1x_coco_20200205-1c3d78ed.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco/mask_rcnn_hrnetv2p_w18_1x_coco_20200205_232523.log.json) | | HRNetV2p-W18 | pytorch | 2x | 7.0 | - | 39.8 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco/mask_rcnn_hrnetv2p_w18_2x_coco_20200212-b3c825b1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco/mask_rcnn_hrnetv2p_w18_2x_coco_20200212_134222.log.json) | | HRNetV2p-W32 | pytorch | 1x | 9.4 | 11.3 | 41.2 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco/mask_rcnn_hrnetv2p_w32_1x_coco_20200207-b29f616e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco/mask_rcnn_hrnetv2p_w32_1x_coco_20200207_055017.log.json) | | HRNetV2p-W32 | pytorch | 2x | 9.4 | - | 42.5 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco/mask_rcnn_hrnetv2p_w32_2x_coco_20200213-45b75b4d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco/mask_rcnn_hrnetv2p_w32_2x_coco_20200213_150518.log.json) | | HRNetV2p-W40 | pytorch | 1x | 10.9 | | 42.1 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco/mask_rcnn_hrnetv2p_w40_1x_coco_20200511_015646-66738b35.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco/mask_rcnn_hrnetv2p_w40_1x_coco_20200511_015646.log.json) | | HRNetV2p-W40 | pytorch | 2x | 10.9 | | 42.8 | 38.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco/mask_rcnn_hrnetv2p_w40_2x_coco_20200512_163732-aed5e4ab.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco/mask_rcnn_hrnetv2p_w40_2x_coco_20200512_163732.log.json) | ### Cascade R-CNN | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :-------------:|:------:| :------: | :--------: | | HRNetV2p-W18 | pytorch | 20e | 7.0 | 11.0 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco/cascade_rcnn_hrnetv2p_w18_20e_coco_20200210-434be9d7.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco/cascade_rcnn_hrnetv2p_w18_20e_coco_20200210_105632.log.json) | | HRNetV2p-W32 | pytorch | 20e | 9.4 | 11.0 | 43.3 | [config]