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## Prepare pretrained model
If you use origin ResNet, you do not need to do anything. But if you want to use ResNet_ibn, you need to download pretrain model in [here](https://drive.google.com/open?id=1thS2B8UOSBi_cJX6zRy6YYRwz_nVFI_S). And then you can put it in `~/.cache/torch/checkpoints` or anywhere you like.
If you use origin ResNet, you do not need to do anything. But if you want to use ResNet-ibn or ResNeSt, you need to download pretrain model in [here](https://drive.google.com/open?id=1thS2B8UOSBi_cJX6zRy6YYRwz_nVFI_S).
And then you need to put it in `~/.cache/torch/checkpoints` or anywhere you like.
Then you should set the pretrain model path in `configs/Base-bagtricks.yml`.

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INSTALL.md 100644
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# Installation
## Requirements
- Linux or macOS with python ≥ 3.6
- PyTorch ≥ 1.0
- torchvision that matches the Pytorch installation. You can install them together at [pytorch.org]() to make sure of this.
- [yacs](https://github.com/rbgirshick/yacs)
- Cython (optional to compile evaluation)

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# FastReID
FastReID is a research platform that implements state-of-the-art re-identification algorithms.
FastReID is a research platform that implements state-of-the-art re-identification algorithms. It is a groud-up rewrite of the previous verson, [reid strong baseline](https://github.com/michuanhaohao/reid-strong-baseline).
## What's New
- Remove [ignite](https://github.com/pytorch/ignite)(a high-level library) dependency and powered by [PyTorch](https://pytorch.org/).
- Includes more features such as circle loss, visualizing ranklist and label, SoTA results on intra-domain, cross-domain and partial reid, testing on multi-datasets at the same time, etc.
- Can be used as a library to support [different projects](https://github.com/JDAI-CV/fast-reid/tree/master/projects) on top of it. We'll open source more research projects in this way.
- It trains much faster.
See our [zhihu blog]() to learn more about fastreid.
## Installation
See [INSTALL.md](https://github.com/JDAI-CV/fast-reid/blob/master/INSTALL.md).
## Quick Start
The designed architecture follows this guide [PyTorch-Project-Template](https://github.com/L1aoXingyu/PyTorch-Project-Template), you can check each folder's purpose by yourself.
See GETTING_STARTED.md.
See [GETTING_STARTED.md](https://github.com/JDAI-CV/fast-reid/blob/master/GETTING_STARTED.md).
Learn more at out documentation. And see projects/ for some projects that are build on top of fastreid.
Install dependencies:
- [pytorch 1.0.0+](https://pytorch.org/)
- torchvision
- [yacs](https://github.com/rbgirshick/yacs)
Learn more at out [documentation](). And see [projects/](https://github.com/JDAI-CV/fast-reid/tree/master/projects) for some projects that are build on top of fastreid.
## Model Zoo and Baselines