Comments (3)
huanghoujing
commented on August 11, 2024
Hi, thanks for your interest! The common procedure would be
- Instantiate the model and load the trained model weight
- Read your image, resize, subtract mean, divide by variance, transform to Variable
- Feed the image to the model and get the feature
- Compare feature distance between images
In this way, you don't need to use any Dataset interface of this code and can simply skip it.
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Pamulapati13
commented on August 11, 2024
hi,this repo is really good.
Can I get the trained model weights or should i train it locally on my PC
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huanghoujing
commented on August 11, 2024
Hi, you can download the trained models as described in README.
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Related Issues (20)
- How can I refer your model in my paper HOT 1
- the different with open-reid HOT 1
- 关于一个epoch中的step数目 HOT 2
- Performance is worse than yours
- 关于d_ap的值 HOT 5
- Error: when i try to run train.py (no module named tri_loss. dataset ) HOT 1
- except Exception, msg: SyntaxError: invalid syntax
- 为什么triloss用的是MarginRankingLoss而不是TripletMarginLoss
- dist_mat[is_pos].contiguous().view(N, -1), 1, keepdim=True) seems wrong HOT 1
- dist_mat[is_pos].contiguous().view(N, -1), 1, keepdim=True) HOT 2
- 关于实现和paper的区别，请指点一下 HOT 5
- 你好，您的cuda和cudnn选的是什么版本呢 HOT 1
- cannot reproduce from pre-trained model on martket 1501 stride 1 HOT 3
- 关于实验性能请教 HOT 1
- Rank 1, 5, 10 are high, but mAP is low HOT 1
- 损失函数是不是可以更直接呢 HOT 2
- how to save my im_paths and features? HOT 1
- Hi how to train your code with different pre-trained models HOT 4
- 热图
- hard triplet convergence
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