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diff --git a/CITATION.cff b/CITATION.cff
@@ -0,0 +1,8 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+authors:
+  - name: "UniAD Contributors"
+title: "Planning-oriented Autonomous Driving"
+date-released: 2023-03-26
+url: "https://github.com/OpenDriveLab/UniAD"
+license: Apache-2.0
diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
@@ -0,0 +1,128 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, religion, or sexual identity
+and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the
+  overall community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or
+  advances of any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email
+  address, without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at
+[email protected].
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series
+of actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or
+permanent ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior,  harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within
+the community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.0, available at
+https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
+
+Community Impact Guidelines were inspired by [Mozilla's code of conduct
+enforcement ladder](https://github.com/mozilla/diversity).
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see the FAQ at
+https://www.contributor-covenant.org/faq. Translations are available at
+https://www.contributor-covenant.org/translations.
diff --git a/README.md b/README.md
@@ -3,7 +3,7 @@
 # Planning-oriented Autonomous Driving
 </div>
 
-<p align="center">
+<!-- <p align="center">
  <a href="https://opendrivelab.github.io/UniAD/">
     <img alt="Project Page" src="https://img.shields.io/badge/Project%20Page-Open-yellowgreen.svg" target="_blank" />
   </a>
@@ -13,66 +13,125 @@
   <a href="https://github.com/OpenDriveLab/UniAD/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22">
     <img alt="Good first issue" src="https://img.shields.io/github/issues/OpenDriveLab/UniAD/good%20first%20issue" target="_blank" />
   </a>
-</p>
+</p> -->
 
 <h3 align="center">
-  <a href="https://opendrivelab.github.io/UniAD/">project page</a> |
+  <a href="https://opendrivelab.github.io/UniAD/">Project Page</a> |
   <a href="https://arxiv.org/abs/2212.10156">arXiv</a> |
-  <a href="">video</a> 
+  <a href="https://opendrivelab.com/">OpenDriveLab</a>
+
 </h3>
 
 https://user-images.githubusercontent.com/48089846/202974395-15fe83ac-eebb-4f38-8172-b8ca8c65127e.mp4
 
-This repository will host the code of UniAD.
-
-> Planning-oriented Autonomous Driving
->
-> Yihan Hu*, Jiazhi Yang*, [Li Chen*](https://scholar.google.com/citations?user=ulZxvY0AAAAJ&hl=en&authuser=1), Keyu Li*, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tianwei Lin, Wenhai Wang, Lewei Lu, Xiaosong Jia, Qiang Liu, Jifeng Dai, Yu Qiao, [Hongyang Li](https://lihongyang.info/)
-> - CVPR 2023, award candidate
-> - Primary contact: Li Chen ( [email protected] )
+<br><br>
 
 ![teaser](sources/pipeline.png)
 
-## Highlights
+## Table of Contents:
+1. [Highlights](#high)
+2. [News](#news)
+3. [Getting Started](#start)
+   - [Installation](docs/INSTALL.md)
+   - [Prepare Dataset](docs/DATA_PREP.md)
+   - [Evaluation Example](docs/TRAIN_EVAL.md)
+   - [GPU Requirements](docs/TRAIN_EVAL.md)
+   - [Train/Eval](docs/TRAIN_EVAL.md)
+4. [Results and Models](#models)
+5. [TODO List](#todos)
+6. [License](#license)
+7. [Citation](#citation)
 
-- :oncoming_automobile: **Planning-oriented philosophy**: UniAD is a Unified Autonomous Driving algorithm framework devised following a planning-oriented philosophy. Instead of standalone modular design and multi-task learning, perception, prediciton and planning tasks/components should opt in and be prioritized hierarchically, and we demonstrate the performance can be enhanced to a new level.
-- :trophy: **SOTA performance**: All tasks among UniAD achieve SOTA performance, especially prediction and planning (motion: 0.71m minADE, occ: 63.4% IoU-n., plan: 0.31% avg.Col)
+## Highlights <a name="high"></a>
 
-## News
+- :oncoming_automobile: **Planning-oriented philosophy**: UniAD is a Unified Autonomous Driving algorithm framework following a planning-oriented philosophy. Instead of standalone modular design and multi-task learning, we cast a series of tasks, including perception, prediction and planning tasks hierarchically.
+- :trophy: **SOTA performance**: All tasks within UniAD achieve SOTA performance, especially prediction and planning (motion: 0.71m minADE, occ: 63.4% IoU, planning: 0.31% avg.Col)
 
-- Code & model release: We are actively re-organizing the codebase for better readability. The estimated time is late March. Please stay tuned!
-- About the title: To avoid confusion about the "goal", we change the title from "Goal-oriented" to "Planning-oriented" as suggested by the reviewers. We originally use "goal" to indicate the final safe planning in an AD pipeline, rather than "goal-point" -- the destination of a sequence of actions.
-- [2023/03/21] :rocket::rocket: UniAD paper is accepted by CVPR 2023, as an **award candidate** (12 out of 9155 submissions and 2360 accepted papers)!
-- [2022/12/21] UniAD [paper](https://arxiv.org/abs/2212.10156) is available on arXiv!
+## News <a name="news"></a>
 
-<!-- 
-## Getting started
+- **`Paper Title Change`**: To avoid confusion with the "goal-point" navigation in Robotics, we change the title from "Goal-oriented" to "Planning-oriented" suggested by Reviewers. Thank you!
+- [2023/04] **_Estimated_**. Model checkpoints release `v2.0`
+
+
+- [2023/03/29] Code & model initial release `v1.0`
+- [2023/03/21] :rocket::rocket: UniAD is accepted by CVPR 2023, as an **Award Candidate** (12 out of 2360 accepted papers)!
+- [2022/12/21] UniAD [paper](https://arxiv.org/abs/2212.10156) is available on arXiv.
+
+
+
+<!-- ## Table of Contents:
+1. [Installation](docs/INSTALL.md)
+2. [Prepare Data](docs/DATA_PREP.md)
+3. [Evaluation Example](docs/TRAIN_EVAL.md#example)
+4. [UniAD Training](docs/TRAIN_EVAL.md#train)
+5. [UniAD Evaluation](docs/TRAIN_EVAL.md#eval)
+6. [Results and Models](#models)
+7. [TODO List](#todos)
+7. [License](#license)
+8. [Citing](#citation) -->
+
+
+## Getting Started <a name="start"></a>
+- [Installation](docs/INSTALL.md)
+- [Prepare Dataset](docs/DATA_PREP.md)
+- [Evaluation Example](docs/TRAIN_EVAL.md)
+- [GPU Requirements](docs/TRAIN_EVAL.md)
+- [Train/Eval](docs/TRAIN_EVAL.md)
+
+## Results and Pre-trained Models <a name="models"></a>
+UniAD is trained in two stages. Pretrained checkpoints of both stages will be released and the results of each model are listed in the following tables.
+
+### Stage-one: Perception training
+> We first train the perception modules (i.e., track and map) to obtain a stable initlization for the next stage.
 
-- [Installation]()
-- [Dataset preparation]()
-- [Train and eval]()
--->
+| Method | Encoder | Tracking<br>AMOTA | Mapping<br>IoU-lane | config | Download |
+| :---: | :---: | :---: | :---: | :---:|:---:| 
+| UniAD-S | R50 | -  | - | TBA | TBA |
+| UniAD-B | R101 | 0.390 | 0.297 |  [base-stage1](projects/configs/track_map/base_stage1.py) | [base-stage1](https://github.com/OpenDriveLab/UniAD/releases/download/untagged-d7e1d5e20eded789eee9/uniad_base_track_map.pth) |
+| UniAD-L | V2-99 | - | - | TBA | TBA |
 
-## Main results
 
-Pre-trained models and results under main metrics are provided below. We refer you to the [paper](https://arxiv.org/abs/2212.10156) for more details.
+
+### Stage-two: End-to-end training
+> We optimize all task modules together, including track, map, motion, occupancy and planning.
+
+<!-- 
+Pre-trained models and results under main metrics are provided below. We refer you to the [paper](https://arxiv.org/abs/2212.10156) for more details. -->
 
 | Method | Encoder | Tracking<br>AMOTA | Mapping<br>IoU-lane | Motion<br>minADE |Occupancy<br>IoU-n. | Planning<br>avg.Col. | config | Download |
 | :---: | :---: | :---: | :---: | :---:|:---:| :---: | :---: | :---: |
 | UniAD-S | R50 | 0.241  | 0.315 | 0.788 | 59.4  | 0.32 | TBA | TBA |
-| UniAD-M | R101 | 0.359 | 0.313 | 0.708 | 63.4 | 0.31 |  TBA | TBA |
+| UniAD-B | R101 | 0.359 | 0.313 | 0.708 | 63.4 | 0.31 |  TBA | TBA |
 | UniAD-L | V2-99 | 0.409 | 0.323 | 0.723 | 64.1 | 0.29 | TBA | TBA |
 
-## License
+### Checkpoint Usage
+* Download the checkpoints you need into `UniAD/ckpts/` directory.
+* You can evaluate these checkpoints to reproduce the results, following the `evaluation` section in [TRAIN_EVAL.md](docs/TRAIN_EVAL.md).
+* You can also initialize your own model with the provided weights. Change the `load_from` field to `path/of/ckpt` in the config and follow the `train` section in [TRAIN_EVAL.md](docs/TRAIN_EVAL.md) to start training.
+
+
+### Model Structure
+The overall pipeline of UniAD is controlled by [uniad_e2e.py](projects/mmdet3d_plugin/uniad/detectors/uniad_e2e.py) which coordinates all the task modules in `UniAD/projects/mmdet3d_plugin/uniad/dense_heads`. If you are interested in the implementation of a specific task module, please refer to its corresponding file, e.g., [motion_head](projects/mmdet3d_plugin/uniad/dense_heads/motion_head.py).
+
+## TODO List <a name="todos"></a>
+- [ ] Base-model configs & checkpoints [Est. 2023/04]
+- [ ] Separating BEV encoder and tracking module [Est. 2023/04]
+- [ ] Support larger batch size [Est. 2023/04]
+- [ ] (Long-term) Improve flexibility for future extensions
+- [ ] All configs & checkpoints
+- [x] Code initialization
 
-All assets and code are under the [Apache 2.0 license](https://github.com/OpenDriveLab/UniAD/blob/master/LICENSE) unless specified otherwise.
 
-## Citation
+## License <a name="license"></a>
+
+All assets and code are under the [Apache 2.0 license](./LICENSE) unless specified otherwise.
+
+## Citation <a name="citation"></a>
 
 Please consider citing our paper if the project helps your research with the following BibTex:
 
 ```bibtex
-@inproceedings{uniad,
+@inproceedings{hu2023_uniad,
  title={Planning-oriented Autonomous Driving}, 
  author={Yihan Hu and Jiazhi Yang and Li Chen and Keyu Li and Chonghao Sima and Xizhou Zhu and Siqi Chai and Senyao Du and Tianwei Lin and Wenhai Wang and Lewei Lu and Xiaosong Jia and Qiang Liu and Jifeng Dai and Yu Qiao and Hongyang Li},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
@@ -82,10 +141,9 @@ Please consider citing our paper if the project helps your research with the fol
 ## Related resources
 
 [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)
-
-- [mmdet3d](https://github.com/open-mmlab/mmdetection3d)
 - [BEVFormer](https://github.com/fundamentalvision/BEVFormer) (:rocket:Ours!)
 - [ST-P3](https://github.com/OpenPerceptionX/ST-P3) (:rocket:Ours!)
+- [mmdet3d](https://github.com/open-mmlab/mmdetection3d)
 - [FIERY](https://github.com/wayveai/fiery)
 - [MOTR](https://github.com/megvii-research/MOTR)
-- [BEVerse](https://github.com/zhangyp15/BEVerse)
+- [BEVerse](https://github.com/zhangyp15/BEVerse)
diff --git a/docs/DATA_PREP.md b/docs/DATA_PREP.md
@@ -1 +1,66 @@
-# Dataset Preparation
+
+
+# NuScenes
+Download nuScenes V1.0 full dataset data, CAN bus and map(v1.3) extensions [HERE](https://www.nuscenes.org/download), following the steps below to prepare the data.
+
+
+**Download nuScenes, CAN_bus and Map extensions**
+```shell
+cd UniAD
+mkdir data
+# Download nuScenes V1.0 full dataset data directly to (or soft link to) UniAD/data/
+# Download CAN_bus and Map(v1.3) extensions directly to (or soft link to) UniAD/data/nuscenes/
+```
+
+**Prepare UniAD data infos**
+
+*Option1: We have already prepared the off-the-shelf data infos for you:*
+```shell
+cd UniAD/data
+mkdir infos
+cd infos
+wget https://github.com/OpenDriveLab/UniAD/releases/download/untagged-d7e1d5e20eded789eee9/nuscenes_infos_temporal_train.pkl  # train_infos
+wget https://github.com/OpenDriveLab/UniAD/releases/download/untagged-d7e1d5e20eded789eee9/nuscenes_infos_temporal_val.pkl  # val_infos
+```
+
+
+*Option2: You can also generate the data infos by yourself:*
+```shell
+cd UniAD/data
+mkdir infos
+./tools/uniad_create_data.sh
+# This will generate nuscenes_infos_temporal_{train,val}.pkl
+```
+
+**Prepare Motion Anchors**
+```shell
+cd UniAD/data
+mkdir others
+cd others
+wget https://github.com/OpenDriveLab/UniAD/releases/download/untagged-d7e1d5e20eded789eee9/motion_anchor_infos_mode6.pkl
+```
+
+**The Overall Structure**
+
+*Please make sure the data structure in UniAD/data/ is as follows:*
+```
+UniAD
+├── projects/
+├── tools/
+├── configs/
+├── ckpts/
+│   ├── uniad_base_track_map.pth
+├── data/
+│   ├── nuscenes/
+│   │   ├── can_bus/
+│   │   ├── maps/
+│   │   ├── samples/
+│   │   ├── sweeps/
+│   │   ├── v1.0-test/
+│   │   ├── v1.0-trainval/
+│   ├── infos/
+│   │   ├── nuscenes_infos_temporal_train.pkl
+│   │   ├── nuscenes_infos_temporal_val.pkl
+│   ├── others/
+│   │   ├── motion_anchor_infos_mode6.pkl
+```