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License: Apache License 2.0
mmdetection源码注释
License: Apache License 2.0
@ming71
i saw your work with the oriented bounding box, very nice work!.(ultralytics/yolov3#345)
how you implemented the oriented bounding box to yolov3?Which format for the label you use and can i use a webcam to detect the objects?
Thank you!
显示KeyError: 'PAFPN is not in the neck registry'
你好,你的模型可视化用的什么工具?能否分享下?谢谢~
1、configs中batchsize是在哪里说明的?
2、config中的img_scale 是什么意思?
3、您在本地训练是训练的什么数据,用几个gpu?如果单gpu,你的lr是多少?能提供一下实际的本地训练配置文件吗?谢谢
您好,我用的是cuda10.0,因此它这里报错,无libcudart.so.9.0,请问如何解决呢?
看到博客中有car.json,但我在git工程中没有找到这个数据。
How did you create the visualization structure file like maskrcnn-model-inference.png?
That will be very helpful for the study.
Thanks
I am Vansin, the technical operator of OpenMMLab. In September of last year, we announced the release of OpenMMLab 2.0 at the World Artificial Intelligence Conference in Shanghai. We invite you to upgrade your algorithm library to OpenMMLab 2.0 using MMEngine, which can be used for both research and commercial purposes. If you have any questions, please feel free to join us on the OpenMMLab Discord at https://discord.gg/amFNsyUBvm or add me on WeChat (van-sin) and I will invite you to the OpenMMLab WeChat group.
Here are the OpenMMLab 2.0 repos branches:
OpenMMLab 1.0 branch | OpenMMLab 2.0 branch | |
---|---|---|
MMEngine | 0.x | |
MMCV | 1.x | 2.x |
MMDetection | 0.x 、1.x、2.x | 3.x |
MMAction2 | 0.x | 1.x |
MMClassification | 0.x | 1.x |
MMSegmentation | 0.x | 1.x |
MMDetection3D | 0.x | 1.x |
MMEditing | 0.x | 1.x |
MMPose | 0.x | 1.x |
MMDeploy | 0.x | 1.x |
MMTracking | 0.x | 1.x |
MMOCR | 0.x | 1.x |
MMRazor | 0.x | 1.x |
MMSelfSup | 0.x | 1.x |
MMRotate | 1.x | 1.x |
MMYOLO | 0.x |
Attention: please create a new virtual environment for OpenMMLab 2.0.
您好,想请教一下,我在训练的过程中我只用了三张数据集,为什么会出现no prediction found,不应该是过拟合吗?
关于测试显示结果后,程序出现故障无法停止的问题
这个是什么原因呢?
运行
~/mmdetection$ python tools/train.py configs/cascade_rcnn_x101_32x4d_fpn_1x.py --validate
时
2019-07-25 10:13:28,296 - INFO - Distributed training: False
2019-07-25 10:13:30,073 - INFO - load model from: open-mmlab://resnext101_32x4d
Downloading: "https://s3.ap-northeast-2.amazonaws.com/open-mmlab/pretrain/third_party/resnext101_32x4d-a5af3160.pth" to /home/yaping/.cache/torch/checkpoints/resnext101_32x4d-a5af3160.pth
Traceback (most recent call last):
File "/home/yaping/anaconda3/envs/mmd/lib/python3.7/urllib/request.py", line 1317, in do_open
encode_chunked=req.has_header('Transfer-encoding'))
......(省略)
File "/home/yaping/anaconda3/envs/mmd/lib/python3.7/urllib/request.py", line 1319, in do_open
raise URLError(err)
urllib.error.URLError: <urlopen error [Errno 110] Connection timed out>
显示下载resnext101_32x4d-a5af3160.pth,但是超时,是不是从国外网站下载不下来resnext101_32x4d-a5af3160.pth这个文件啊? 请问有相关的文件么?谢谢啦!
请问只要改变了类别数量就一定不能加载预训练模型吗,我把类别改为五类,加载他原来的预训练模型最后效果很差,不知道是不是这个原因.
谢谢~
在训练的时候遇到这个问题,请问这个pkl预训练文件需要自己训练吗?怎么训练呢?谢谢~
首先感谢你的注释,中文的看起来舒服多了。另外是这样的,我现在在做疾病分类,但是有的病灶只占了图像很小一部分,所以我现在需要用目标检测来提高图像分类准确率。我目前的想法是比如说一张图片预测出了很多box,那么我就按照置信度选择前10个box来得出最终这张图片的类别,比如看10个box觉得多数预测称了A类,那么就预测这张图片属于A类。
我现在用的是faster_rcnn_x101_64x4d这个模型,然后问题是我不太清楚模型输出是个什么东西。比如我在tools/test.py
里
def single_gpu_test(model, data_loader, show=False):
model.eval()
results = []
dataset = data_loader.dataset
prog_bar = mmcv.ProgressBar(len(dataset))
for i, data in enumerate(data_loader):
with torch.no_grad():
result = model(return_loss=False, rescale=not show, **data)
results.append(result)
if show:
model.module.show_result(data, result, dataset.img_norm_cfg)
batch_size = data['img'][0].size(0)
for _ in range(batch_size):
prog_bar.update()
return results
那个result我调试发现输出是个list,好像每次长度都为10,每个元素是一个tensor,维度是 n*5, 这个n不固定,可能是0,1,也可能是8。就想请问一下这里输出的是什么?或者有什么简单的办法可以计算最终的分类准确率?谢谢
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