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训练自己的数据集 #83
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Hello, and thank you for your interest in our work! For customizing the dataset, you might find the discussion in this previous issue helpful: #57. |
我查看了#57 的建议。由于我是新手,我的数据集转换成COCO格式或者YOLO格式我都能理解并且做到,但在 #57 中,您的回答是将数据集转换成OVIS再通过您提供的管道convert_ovis_coco转换成 coco标注。我是否可以理解为我只需要将我的数据集按coco数据集格式组织(通过自己的代码),还是说必须通过您提供的函数?我的工作是使用这个模型对视频进行目标检测训练。你能解答一下吗? |
The purpose of the dataloader is to organize the dataset and send sequences of frames from a video to the model. This differs from the typical image detection pipeline, because all images in a batch should come from the same video. You can download the OVIS dataset annotations and organize your dataset in a similar format, then load it using the function provided in this repository. Alternatively, you may need to rewrite the dataloader pipeline to meet the specific requirements mentioned above. |
我的数据集是多个视频片段组成的(来源于不同的视频),因此我认为应该是可以使用该模型来进行训练的吧?
Download our COCO-style annotations for training, FGFA version training annotation and video sequences. Then, put them in these two directories:
我知道ILSVRC2015 DET和 ILSVRC2015 VID的文件结构是不一样的,鉴于我所需要对视频序列进行目标检测这个任务,这意味着我是否可以按照ILSVRC2015 VID数据集的结构重新组织我的数据集结构,然后生成vid_train_coco.json和train_seq.npy两个文件,之后可以进行训练?还是说您仍建议我以OVIS 数据集格式重新组织我的数据集结构,然后使用此存储库中提供的函数加载它? |
Both of them work! |
好的,我去尝试一下 |
我已经组织好了结构,并寻求如何训练。在查看相关的issue之后,我是不是可以这样理解:按照ILSVRC2015_VID数据集格式重构自己的数据集,然后生成一个train.json文件和train.npy文件,先使用train.json文件训练YOLOX的模型,得到一个YOLOX的预训练模型,然后把得到的这个YOLOX的模型作为YOLOV的预训练模型进行添加,YOLOV会使用train.npy文件进行训练。 |
Yep. As a reminder, this repo contains the code to train the base detector. |
我如果想将您的结构搬迁于yolov8项目上,是不是要进行以下工作: |
想请问一下,你实现了吗? |
没有,我放弃了。需要修改很多文件。 |
请问我如果想使用您的项目训练自己的模型,在创建数据集部分,我在readme.md里看到有coco.json和val_seq.npy,但是代码中又有xml文件,这让我很迷惑,您能给个指导吗?我该如何构建数据集结构,以此来训练自己的模型
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