Comments (2)
Hi,
We used the evaluation scripts published by authors of the datasets. You can find them by visiting the project pages of the datasets.
In all our experiments, 10 datasets mentioned in the paper were used. Therefore, you should follow the same settings mentioned in the paper to get higher accuracy.
from spreid.
Hi,
We used the evaluation scripts published by authors of the datasets. You can find them by visiting the project pages of the datasets.
In all our experiments, 10 datasets mentioned in the paper were used. Therefore, you should follow the same settings mentioned in the paper to get higher accuracy.
Thank you!
from spreid.
Related Issues (20)
- Dataset path HOT 1
- train: path of model HOT 1
- Trying to reproduce human Semantic Parsing in pytorch HOT 4
- cupy.cuda.compiler.CompileException: nvrtc: error: failed to load builtins
- weight shareing HOT 3
- Much Lower Result & GPU Problem HOT 7
- The parts of human semantic parsing HOT 1
- in semantic part,how do you decide the segprob? HOT 1
- how do you get the LIP_iter_30000.chainermodel?
- environment set HOT 2
- The pretrained for human sematic parsing HOT 4
- how to train the model,the details about training process . Reid model and segment model trained sparately?
- is there pytorch version,can you share it HOT 1
- Ask for trained model
- > > Hi, I use only 1 GPU(GXT 1080Ti 11GB) for training this model. Limited by memory of GPU, I set the batchsize=4 instead of 16 in your code. And I trained the model only on Market-1501 dataset instead of 10 datasets. The result is mAP = 0.385818, r1 precision = 0.627969 much lower than your paper said. So there are two problems.
- Testing my own images
- 决定不引用这类论文,不是想黑:想不明白,公开得差不多了,为啥训练好的模型和评估函数都不放进去,方便比较?
- Weight Sharing Setting when training the model
- I am not getting the dataset of this program, can you please send me the dataset particular to this project
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