Update README.md

README.md

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 # PSConv
-[ECCV 2020] PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer
+Official implementation of our PSConv operator as described in [PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer]() (ECCV'20) by  [Duo Li](https://github.com/d-li14), [Anbang Yao](https://github.com/YaoAnbang) and [Qifeng Chen](https://github.com/CQFIO) on the [MS COCO 2017](https://cocodataset.org) benchmark.
+
+<p align="center"><img src="fig/psconv.png" width="600" /></p>
+
+## Getting Started
+
+### Installation
+
+Following the instructions from [INSTALL.md](INSTALL.md) for installation. More detailed guidance can be found from [MMDetection](https://github.com/open-mmlab/mmdetection).
+
+### Dataset
+
+It is recommended to symlink the dataset root to `$MMDETECTION/data`.
+If your folder structure is different, you may need to change the corresponding paths in config files.
+
+```
+mmdetection
+├── mmdet
+├── tools
+├── configs
+├── data
+│   ├── coco
+│   │   ├── annotations
+│   │   ├── train2017
+│   │   ├── val2017
+│   │   ├── test2017
+```
+
+
+### Download ImageNet pre-trained checkpoints
+
+Fetch pre-trained [PS-ResNet-50](https://drive.google.com/file/d/155pB1vci62zQV6AmY4MsCw5zLL0qYmG4/view?usp=sharing), [PS-ResNet-101](https://drive.google.com/file/d/15kgctzliEguQHihJoNeG65Vld9DGEITV/view?usp=sharing) and [PS-ResNeXt-101 (32x4d)](https://drive.google.com/file/d/1dbttT3PvILRQvA9f0nKCLvQNg1a2QsVD/view?usp=sharing) and put them to your local path. Set the `pretrained` path of config file.
+
+### Training
+
+The default learning rate in config files is for 8 GPUs and 2 img/GPU (batch size = 8*2 = 16).
+According to the [Linear Scaling Rule](https://arxiv.org/abs/1706.02677), you need to set the learning rate proportional to the batch size if you use different GPUs or images per GPU, e.g., lr=0.01 for 4 GPUs * 2 img/gpu and lr=0.08 for 16 GPUs * 4 img/GPU.
+
+```shell
+# single-gpu training
+python tools/train.py ${CONFIG_FILE}
+
+# multi-gpu training
+./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]
+```
+
+Optional arguments are:
+
+- `--validate` (**strongly recommended**): Perform evaluation at every k (default value is 1) epochs during the training.
+- `--work_dir ${WORK_DIR}`: Override the working directory specified in the config file.
+- `--resume_from ${CHECKPOINT_FILE}`: Resume from a previous checkpoint file.
+
+### Test
+
+```shell
+# single-gpu testing
+python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] [--show]
+
+# multi-gpu testing
+./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}]
+```
+
+Optional arguments:
+- `RESULT_FILE`: Filename of the output results in pickle format. If not specified, the results will not be saved to a file.
+- `EVAL_METRICS`: Items to be evaluated on the results. Allowed values are: `proposal_fast`, `proposal`, `bbox`, `segm`, `keypoints`.
+- `--show`: If specified, detection results will be ploted on the images and shown in a new window. (Only applicable for single GPU testing.)
+
+
+## Model Zoo
+
+### Faster R-CNN
+
+| Backbone  |  Style  | Lr schd | box AP |                           Download                           |
+| :-------: | :-----: | :-----: | :----: | :----------------------------------------------------------: |
+| R-50-FPN  | pytorch |   1x    |  38.4  | [model](https://drive.google.com/file/d/1sXHMMCWD9PS73gBRw-C5KoorjzFCfKkA/view?usp=sharing) &#124; [log](https://drive.google.com/file/d/1n1a_XB9XclQk3uoJJ2R3mojytqH3ccTx/view?usp=sharing) |
+| R-101-FPN | pytorch |   1x    |  40.9  | [model](https://drive.google.com/file/d/1NV2dPYBb8lnDS75we0u-qHnPP_pfI6DG/view?usp=sharing) &#124; [log](https://drive.google.com/file/d/17yvoUtn9KqEPTwOUfT1Ucwyqt2ZraD60/view?usp=sharing) |
+| X-101-FPN | pytorch |   1x    |  41.3  | [model](https://drive.google.com/file/d/1Hct7d_OrVMP2LZmgMu0DncY1NpCpS_E2/view?usp=sharing) &#124; [log](https://drive.google.com/file/d/1FKInWaI15Q-6ra_86gvNjTeIW7TjKE7b/view?usp=sharing) |
+
+### Mask R-CNN
+
+|    Backbone     |  Style  | Lr schd | box AP | mask AP | Download |
+| :-------------: | :-----: | :-----: | :----: | :-----: | :----------------: |
+|    R-50-FPN     | pytorch |   1x    |  39.4  |  35.6  | [model](https://drive.google.com/file/d/1cuXnMNQbv_B3nKEWnstbOJRPdMRu9twn/view?usp=sharing) &#124; [log](https://drive.google.com/file/d/1_9Oho9eRc23eAuZ_bD_MaYhcoRtRJRjP/view?usp=sharing) |
+|    R-101-FPN    | pytorch |   1x    |  41.6  |  37.4  | [model](https://drive.google.com/file/d/1U5G4uyb5DM-1NS06qOEzCVyEAaDxcvRL/view?usp=sharing) &#124; [log](https://drive.google.com/file/d/17piYzxwPmZCTiiJTLdrdd6phgA1f4X22/view?usp=sharing) |
+|    X-101-FPN    | pytorch |   1x    |  42.4  |  38.0  | [model](https://drive.google.com/file/d/1B0GV_PeXDz4_O0iM57GbWSUBXwExZdAs/view?usp=sharing) &#124; [log](https://drive.google.com/file/d/1qD3yW_On8hbcnYyuzKRYTRMNe3QaonRk/view?usp=sharing) |
+
+### Cascade R-CNN
+
+| Backbone  |  Style  | Lr schd | box AP |                           Download                           |
+| :-------: | :-----: | :-----: | :----: | :----------------------------------------------------------: |
+| R-50-FPN  | pytorch |   1x    |  41.9  | [model](https://drive.google.com/file/d/13i8U-QOpFBhIUndqgYjpH6sD5ram6LFP/view?usp=sharing) &#124; [log](https://drive.google.com/file/d/1q_Vk-9XJDlHAd9m8vFLa1GV8Dtc_WbPU/view?usp=sharing) |
+| R-101-FPN | pytorch |   1x    |  43.8  | [model](https://drive.google.com/file/d/1bOWvZfwttuiAjNZhOJin_afji4X1P5Vx/view?usp=sharing) &#124; [log](https://drive.google.com/file/d/1fHAXuuszNsnX9yBaBXP6k_Bni8VrHNFp/view?usp=sharing) |
+| X-101-FPN | pytorch |   1x    |  44.4  | [model](https://drive.google.com/file/d/1yCWeqSEjCawlOxMWb0GQmkjPiVMDBTqL/view?usp=sharing) &#124; [log](https://drive.google.com/file/d/1n2MvM_Uaz64JQJqlPVJ2CZycRzb6eYEq/view?usp=sharing) |
+
+### Cascade Mask R-CNN
+
+|    Backbone     |  Style  | Lr schd | box AP | mask AP | Download |
+| :-------------: | :-----: | :-----: | :----: | :-----: | :----------------: |
+|    R-50-FPN     | pytorch |   1x    |  42.9  |  36.9  | [model](https://drive.google.com/file/d/1L6Ai18CzQ_qcDzSUZEVnOVTQNR_wR9TL/view?usp=sharing) &#124; [log](https://drive.google.com/file/d/1xoKm7Fx3wsShr_LFPNpDtxH0ajHLPyAz/view?usp=sharing) |
+|    R-101-FPN    | pytorch |   1x    |  44.6  |  38.4  | [model](https://drive.google.com/file/d/1oB0p7HkD2MjNjj75xHYbBDNg90PxG09G/view?usp=sharing) &#124; [log](https://drive.google.com/file/d/1XdhV6OOVX4m2vUCKnw4heWhHwwFYGJcK/view?usp=sharing) |
+|    X-101-FPN    | pytorch |   1x    |  45.3  |  38.9  | [model](https://drive.google.com/file/d/1NDaDe2prI_K5Rvgg9g3x3DjWDG8i0N5V/view?usp=sharing) &#124; [log](https://drive.google.com/file/d/1dLssd97Xv0kQIhyzC4BTmSyPKRkn0GN9/view?usp=sharing) |
+
+
+## Acknowledgement
+
+This implementation is built upon [MMDetection](https://github.com/open-mmlab/mmdetection). Thanks [Kai Chen](https://github.com/hellock) for releasing this awesome toolbox and his helpful discussions.
+
+Since this project is finished nearly one year ago, we adapt our code from an early commit [713e98b](https://github.com/open-mmlab/mmdetection/commits/713e98bc1bfb842760df6be73b9520ee775d3c06).
+
+## Citation
+
+```bibtex
+@InProceedings{Li_2020_ECCV,
+author = {Li, Duo and Yao, Anbang and Chen, Qifeng},
+title = {PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer},
+booktitle = {The European Conference on Computer Vision (ECCV)},
+month = {August},
+year = {2020}
+}
+```
+