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@ -87,18 +87,26 @@ python train_vid_model_xent.py -d mars -a resnet50 --evaluate --resume saved-mod
```
**Note** that `--test-batch` in video reid represents number of tracklets. If you set this argument to 2, and sample 15 images per tracklet, the resulting number of images per batch is 2*15=30. Adjust this argument according to your GPU memory.
## Visualize ranked results
## Visualizing ranked results
Ranked results can be visualized via `--vis-ranked-res`, which works along with `--evaluate`. Ranked images will be saved in `save_dir/ranked_results` where `save_dir` is the directory you specify with `--save-dir`.
<div align="center">
<img src="imgs/ranked_results.jpg" alt="train" width="60%">
<img src="imgs/ranked_results.jpg" alt="train" width="70%">
</div>
## Issue
Before raising an issue, please have a look at the [history issues](https://github.com/KaiyangZhou/deep-person-reid/issues) where you may find answers. If those answers do not solve your problem, raise a new issue (choose an informative title) and include the following details in your question: (1) environmental settings, e.g. python version, torch/torchvision version, etc. (2) command that leads to the errors. (3) screenshot of error logs if available. If you find any errors in the code, please inform me by opening a new issue.
## Citation
Please link this project in your paper.
## References
[1] [He et al. Deep Residual Learning for Image Recognition. CVPR 2016.](https://arxiv.org/abs/1512.03385)<br />
[2] [Yu et al. The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching. arXiv:1711.08106.](https://arxiv.org/abs/1711.08106) <br />