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Detectron2

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## Getting Started with Detectron2
This document provides a brief intro of the usage of builtin command-line tools in detectron2.
For a tutorial that involves actual coding with the API,
see our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
which covers how to run inference with an
existing model, and how to train a builtin model on a custom dataset.
For more advanced tutorials, refer to our [documentation](https://detectron2.readthedocs.io/tutorials/extend.html).
### Inference Demo with Pre-trained Models
1. Pick a model and its config file from
[model zoo](MODEL_ZOO.md),
for example, `mask_rcnn_R_50_FPN_3x.yaml`.
2. We provide `demo.py` that is able to demo builtin configs. Run it with:
```
cd demo/
python demo.py --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \
--input input1.jpg input2.jpg \
[--other-options]
--opts MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl
```
The configs are made for training, therefore we need to specify `MODEL.WEIGHTS` to a model from model zoo for evaluation.
This command will run the inference and show visualizations in an OpenCV window.
For details of the command line arguments, see `demo.py -h` or look at its source code
to understand its behavior. Some common arguments are:
* To run __on your webcam__, replace `--input files` with `--webcam`.
* To run __on a video__, replace `--input files` with `--video-input video.mp4`.
* To run __on cpu__, add `MODEL.DEVICE cpu` after `--opts`.
* To save outputs to a directory (for images) or a file (for webcam or video), use `--output`.
### Training & Evaluation in Command Line
We provide two scripts in "tools/plain_train_net.py" and "tools/train_net.py",
that are made to train all the configs provided in detectron2. You may want to
use it as a reference to write your own training script.
Compared to "train_net.py", "plain_train_net.py" supports fewer default
features. It also includes fewer abstraction, therefore is easier to add custom
logic.
To train a model with "train_net.py", first
setup the corresponding datasets following
[datasets/README.md](./datasets/README.md),
then run:
```
cd tools/
./train_net.py --num-gpus 8 \
--config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml
```
The configs are made for 8-GPU training.
To train on 1 GPU, you may need to [change some parameters](https://arxiv.org/abs/1706.02677), e.g.:
```
./train_net.py \
--config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \
--num-gpus 1 SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025
```
For most models, CPU training is not supported.
To evaluate a model's performance, use
```
./train_net.py \
--config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \
--eval-only MODEL.WEIGHTS /path/to/checkpoint_file
```
For more options, see `./train_net.py -h`.
### Use Detectron2 APIs in Your Code
See our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
to learn how to use detectron2 APIs to:
1. run inference with an existing model
2. train a builtin model on a custom dataset
See [detectron2/projects](https://github.com/facebookresearch/detectron2/tree/master/projects)
for more ways to build your project on detectron2.

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## Installation
Our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
has step-by-step instructions that install detectron2.
The [Dockerfile](docker)
also installs detectron2 with a few simple commands.
### Requirements
- Linux or macOS with Python ≥ 3.6
- PyTorch ≥ 1.4 and [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation.
You can install them together at [pytorch.org](https://pytorch.org) to make sure of this
- OpenCV is optional and needed by demo and visualization
### Build Detectron2 from Source
gcc & g++ ≥ 5 are required. [ninja](https://ninja-build.org/) is recommended for faster build.
After having them, run:
```
python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
# (add --user if you don't have permission)
# Or, to install it from a local clone:
git clone https://github.com/facebookresearch/detectron2.git
python -m pip install -e detectron2
# Or if you are on macOS
CC=clang CXX=clang++ python -m pip install ......
```
To __rebuild__ detectron2 that's built from a local clone, use `rm -rf build/ **/*.so` to clean the
old build first. You often need to rebuild detectron2 after reinstalling PyTorch.
### Install Pre-Built Detectron2 (Linux only)
Choose from this table to install [v0.2.1 (Aug 2020)](https://github.com/facebookresearch/detectron2/releases):
<table class="docutils"><tbody><th width="80"> CUDA </th><th valign="bottom" align="left" width="100">torch 1.6</th><th valign="bottom" align="left" width="100">torch 1.5</th><th valign="bottom" align="left" width="100">torch 1.4</th> <tr><td align="left">10.2</td><td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.6/index.html
</code></pre> </details> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.5/index.html
</code></pre> </details> </td> <td align="left"> </td> </tr> <tr><td align="left">10.1</td><td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.6/index.html
</code></pre> </details> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.5/index.html
</code></pre> </details> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.4/index.html
</code></pre> </details> </td> </tr> <tr><td align="left">10.0</td><td align="left"> </td> <td align="left"> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu100/torch1.4/index.html
</code></pre> </details> </td> </tr> <tr><td align="left">9.2</td><td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu92/torch1.6/index.html
</code></pre> </details> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu92/torch1.5/index.html
</code></pre> </details> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu92/torch1.4/index.html
</code></pre> </details> </td> </tr> <tr><td align="left">cpu</td><td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/torch1.6/index.html
</code></pre> </details> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/torch1.5/index.html
</code></pre> </details> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/torch1.4/index.html
</code></pre> </details> </td> </tr></tbody></table>
Note that:
1. The pre-built package has to be used with corresponding version of CUDA and official PyTorch release.
It will not work with a different version of PyTorch or a non-official build of PyTorch.
2. New packages are released every few months. Therefore, packages may not contain latest features in the master
branch and may not be compatible with the master branch of a research project that uses detectron2
(e.g. those in [projects](projects)).
### Common Installation Issues
Click each issue for its solutions:
<details>
<summary>
Undefined symbols that contains TH,aten,torch,caffe2; Missing torch dynamic libraries; Segmentation fault immediately when using detectron2.
</summary>
<br/>
This usually happens when detectron2 or torchvision is not
compiled with the version of PyTorch you're running.
If the error comes from a pre-built torchvision, uninstall torchvision and pytorch and reinstall them
following [pytorch.org](http://pytorch.org). So the versions will match.
If the error comes from a pre-built detectron2, check [release notes](https://github.com/facebookresearch/detectron2/releases)
to see the corresponding pytorch version required for each pre-built detectron2.
Or uninstall and reinstall the correct pre-built detectron2.
If the error comes from detectron2 or torchvision that you built manually from source,
remove files you built (`build/`, `**/*.so`) and rebuild it so it can pick up the version of pytorch currently in your environment.
If you cannot resolve this problem, please include the output of `gdb -ex "r" -ex "bt" -ex "quit" --args python -m detectron2.utils.collect_env`
in your issue.
</details>
<details>
<summary>
Undefined C++ symbols (e.g. `GLIBCXX`) or C++ symbols not found.
</summary>
<br/>
Usually it's because the library is compiled with a newer C++ compiler but run with an old C++ runtime.
This often happens with old anaconda.
Try `conda update libgcc`. Then rebuild detectron2.
The fundamental solution is to run the code with proper C++ runtime.
One way is to use `LD_PRELOAD=/path/to/libstdc++.so`.
</details>
<details>
<summary>
"nvcc not found" or "Not compiled with GPU support" or "Detectron2 CUDA Compiler: not available".
</summary>
<br/>
CUDA is not found when building detectron2.
You should make sure
```
python -c 'import torch; from torch.utils.cpp_extension import CUDA_HOME; print(torch.cuda.is_available(), CUDA_HOME)'
```
print `(True, a directory with cuda)` at the time you build detectron2.
Most models can run inference (but not training) without GPU support. To use CPUs, set `MODEL.DEVICE='cpu'` in the config.
</details>
<details>
<summary>
"invalid device function" or "no kernel image is available for execution".
</summary>
<br/>
Two possibilities:
* You build detectron2 with one version of CUDA but run it with a different version.
To check whether it is the case,
use `python -m detectron2.utils.collect_env` to find out inconsistent CUDA versions.
In the output of this command, you should expect "Detectron2 CUDA Compiler", "CUDA_HOME", "PyTorch built with - CUDA"
to contain cuda libraries of the same version.
When they are inconsistent,
you need to either install a different build of PyTorch (or build by yourself)
to match your local CUDA installation, or install a different version of CUDA to match PyTorch.
* PyTorch/torchvision/Detectron2 is not built for the correct GPU architecture (aka. compute capability).
The architecture included by PyTorch/detectron2/torchvision is available in the "architecture flags" in
`python -m detectron2.utils.collect_env`. It must include
the architecture of your GPU, which can be found at [developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus).
If you're using pre-built PyTorch/detectron2/torchvision, they have included support for most popular GPUs already.
If not supported, you need to build them from source.
When building detectron2/torchvision from source, they detect the GPU device and build for only the device.
This means the compiled code may not work on a different GPU device.
To recompile them for the correct architecture, remove all installed/compiled files,
and rebuild them with the `TORCH_CUDA_ARCH_LIST` environment variable set properly.
For example, `export TORCH_CUDA_ARCH_LIST=6.0,7.0` makes it compile for both P100s and V100s.
</details>
<details>
<summary>
Undefined CUDA symbols; Cannot open libcudart.so
</summary>
<br/>
The version of NVCC you use to build detectron2 or torchvision does
not match the version of CUDA you are running with.
This often happens when using anaconda's CUDA runtime.
Use `python -m detectron2.utils.collect_env` to find out inconsistent CUDA versions.
In the output of this command, you should expect "Detectron2 CUDA Compiler", "CUDA_HOME", "PyTorch built with - CUDA"
to contain cuda libraries of the same version.
When they are inconsistent,
you need to either install a different build of PyTorch (or build by yourself)
to match your local CUDA installation, or install a different version of CUDA to match PyTorch.
</details>
<details>
<summary>
C++ compilation errors from NVCC
</summary>
1. NVCC version has to match the CUDA version of your PyTorch.
2. The combination of NVCC and GCC you use is incompatible. You need to change one of their versions.
See [here](https://gist.github.com/ax3l/9489132) for some valid combinations.
The CUDA/GCC version used by PyTorch can be found by `print(torch.__config__.show())`.
</details>
<details>
<summary>
"ImportError: cannot import name '_C'".
</summary>
<br/>
Please build and install detectron2 following the instructions above.
Or, if you are running code from detectron2's root directory, `cd` to a different one.
Otherwise you may not import the code that you installed.
</details>
<details>
<summary>
Any issue on windows.
</summary>
<br/>
Detectron2 is continuously built on windows with [CircleCI](https://app.circleci.com/pipelines/github/facebookresearch/detectron2?branch=master).
However we do not provide official support for it.
PRs that improves code compatibility on windows are welcome.
</details>
<details>
<summary>
ONNX conversion segfault after some "TraceWarning".
</summary>
<br/>
The ONNX package is compiled with a too old compiler.
Please build and install ONNX from its source code using a compiler
whose version is closer to what's used by PyTorch (available in `torch.__config__.show()`).
</details>

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# Detectron2 Model Zoo and Baselines
## Introduction
This file documents a large collection of baselines trained
with detectron2 in Sep-Oct, 2019.
All numbers were obtained on [Big Basin](https://engineering.fb.com/data-center-engineering/introducing-big-basin-our-next-generation-ai-hardware/)
servers with 8 NVIDIA V100 GPUs & NVLink. The software in use were PyTorch 1.3, CUDA 9.2, cuDNN 7.4.2 or 7.6.3.
You can access these models from code using [detectron2.model_zoo](https://detectron2.readthedocs.io/modules/model_zoo.html) APIs.
In addition to these official baseline models, you can find more models in [projects/](projects/).
#### How to Read the Tables
* The "Name" column contains a link to the config file. Running `tools/train_net.py --num-gpus 8` with this config file
will reproduce the model.
* Training speed is averaged across the entire training.
We keep updating the speed with latest version of detectron2/pytorch/etc.,
so they might be different from the `metrics` file.
Training speed for multi-machine jobs is not provided.
* Inference speed is measured by `tools/train_net.py --eval-only`, or [inference_on_dataset()](https://detectron2.readthedocs.io/modules/evaluation.html#detectron2.evaluation.inference_on_dataset),
with batch size 1 in detectron2 directly.
Measuring it with custom code may introduce other overhead.
Actual deployment in production should in general be faster than the given inference
speed due to more optimizations.
* The *model id* column is provided for ease of reference.
To check downloaded file integrity, any model on this page contains its md5 prefix in its file name.
* Training curves and other statistics can be found in `metrics` for each model.
#### Common Settings for COCO Models
* All COCO models were trained on `train2017` and evaluated on `val2017`.
* The default settings are __not directly comparable__ with Detectron's standard settings.
For example, our default training data augmentation uses scale jittering in addition to horizontal flipping.
To make fair comparisons with Detectron's settings, see
[Detectron1-Comparisons](configs/Detectron1-Comparisons/) for accuracy comparison,
and [benchmarks](https://detectron2.readthedocs.io/notes/benchmarks.html)
for speed comparison.
* For Faster/Mask R-CNN, we provide baselines based on __3 different backbone combinations__:
* __FPN__: Use a ResNet+FPN backbone with standard conv and FC heads for mask and box prediction,
respectively. It obtains the best
speed/accuracy tradeoff, but the other two are still useful for research.
* __C4__: Use a ResNet conv4 backbone with conv5 head. The original baseline in the Faster R-CNN paper.
* __DC5__ (Dilated-C5): Use a ResNet conv5 backbone with dilations in conv5, and standard conv and FC heads
for mask and box prediction, respectively.
This is used by the Deformable ConvNet paper.
* Most models are trained with the 3x schedule (~37 COCO epochs).
Although 1x models are heavily under-trained, we provide some ResNet-50 models with the 1x (~12 COCO epochs)
training schedule for comparison when doing quick research iteration.
#### ImageNet Pretrained Models
It's common to initialize from backbone models pre-trained on ImageNet classification tasks. The following backbone models are available:
* [R-50.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-50.pkl): converted copy of [MSRA's original ResNet-50](https://github.com/KaimingHe/deep-residual-networks) model.
* [R-101.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-101.pkl): converted copy of [MSRA's original ResNet-101](https://github.com/KaimingHe/deep-residual-networks) model.
* [X-101-32x8d.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/FAIR/X-101-32x8d.pkl): ResNeXt-101-32x8d model trained with Caffe2 at FB.
* [R-50.pkl (torchvision)](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/torchvision/R-50.pkl): converted copy of [torchvision's ResNet-50](https://pytorch.org/docs/stable/torchvision/models.html#torchvision.models.resnet50) model.
More details can be found in [the conversion script](tools/convert-torchvision-to-d2.py).
Note that the above models have __different__ format from those provided in Detectron: we do not fuse BatchNorm into an affine layer.
Pretrained models in Detectron's format can still be used. For example:
* [X-152-32x8d-IN5k.pkl](https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/25093814/X-152-32x8d-IN5k.pkl):
ResNeXt-152-32x8d model trained on ImageNet-5k with Caffe2 at FB (see ResNeXt paper for details on ImageNet-5k).
* [R-50-GN.pkl](https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/47261647/R-50-GN.pkl):
ResNet-50 with Group Normalization.
* [R-101-GN.pkl](https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/47592356/R-101-GN.pkl):
ResNet-101 with Group Normalization.
#### License
All models available for download through this document are licensed under the
[Creative Commons Attribution-ShareAlike 3.0 license](https://creativecommons.org/licenses/by-sa/3.0/).
### COCO Object Detection Baselines
#### Faster R-CNN:
<!--
(fb only) To update the table in vim:
1. Remove the old table: d}
2. Copy the below command to the place of the table
3. :.!bash
./gen_html_table.py --config 'COCO-Detection/faster*50*'{1x,3x}'*' 'COCO-Detection/faster*101*' --name R50-C4 R50-DC5 R50-FPN R50-C4 R50-DC5 R50-FPN R101-C4 R101-DC5 R101-FPN X101-FPN --fields lr_sched train_speed inference_speed mem box_AP
-->
<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom">Name</th>
<th valign="bottom">lr<br/>sched</th>
<th valign="bottom">train<br/>time<br/>(s/iter)</th>
<th valign="bottom">inference<br/>time<br/>(s/im)</th>
<th valign="bottom">train<br/>mem<br/>(GB)</th>
<th valign="bottom">box<br/>AP</th>
<th valign="bottom">model id</th>
<th valign="bottom">download</th>
<!-- TABLE BODY -->
<!-- ROW: faster_rcnn_R_50_C4_1x -->
<tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml">R50-C4</a></td>
<td align="center">1x</td>
<td align="center">0.551</td>
<td align="center">0.102</td>
<td align="center">4.8</td>
<td align="center">35.7</td>
<td align="center">137257644</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_C4_1x/137257644/model_final_721ade.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_C4_1x/137257644/metrics.json">metrics</a></td>
</tr>
<!-- ROW: faster_rcnn_R_50_DC5_1x -->
<tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml">R50-DC5</a></td>
<td align="center">1x</td>
<td align="center">0.380</td>
<td align="center">0.068</td>
<td align="center">5.0</td>
<td align="center">37.3</td>
<td align="center">137847829</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_DC5_1x/137847829/model_final_51d356.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_DC5_1x/137847829/metrics.json">metrics</a></td>
</tr>
<!-- ROW: faster_rcnn_R_50_FPN_1x -->
<tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml">R50-FPN</a></td>
<td align="center">1x</td>
<td align="center">0.210</td>
<td align="center">0.038</td>
<td align="center">3.0</td>
<td align="center">37.9</td>
<td align="center">137257794</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/metrics.json">metrics</a></td>
</tr>
<!-- ROW: faster_rcnn_R_50_C4_3x -->
<tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml">R50-C4</a></td>
<td align="center">3x</td>
<td align="center">0.543</td>
<td align="center">0.104</td>
<td align="center">4.8</td>
<td align="center">38.4</td>
<td align="center">137849393</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_C4_3x/137849393/model_final_f97cb7.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_C4_3x/137849393/metrics.json">metrics</a></td>
</tr>
<!-- ROW: faster_rcnn_R_50_DC5_3x -->
<tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml">R50-DC5</a></td>
<td align="center">3x</td>
<td align="center">0.378</td>
<td align="center">0.070</td>
<td align="center">5.0</td>
<td align="center">39.0</td>
<td align="center">137849425</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_DC5_3x/137849425/model_final_68d202.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_DC5_3x/137849425/metrics.json">metrics</a></td>
</tr>
<!-- ROW: faster_rcnn_R_50_FPN_3x -->
<tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml">R50-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.209</td>
<td align="center">0.038</td>
<td align="center">3.0</td>
<td align="center">40.2</td>
<td align="center">137849458</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/metrics.json">metrics</a></td>
</tr>
<!-- ROW: faster_rcnn_R_101_C4_3x -->
<tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml">R101-C4</a></td>
<td align="center">3x</td>
<td align="center">0.619</td>
<td align="center">0.139</td>
<td align="center">5.9</td>
<td align="center">41.1</td>
<td align="center">138204752</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_C4_3x/138204752/model_final_298dad.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_C4_3x/138204752/metrics.json">metrics</a></td>
</tr>
<!-- ROW: faster_rcnn_R_101_DC5_3x -->
<tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml">R101-DC5</a></td>
<td align="center">3x</td>
<td align="center">0.452</td>
<td align="center">0.086</td>
<td align="center">6.1</td>
<td align="center">40.6</td>
<td align="center">138204841</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_DC5_3x/138204841/model_final_3e0943.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_DC5_3x/138204841/metrics.json">metrics</a></td>
</tr>
<!-- ROW: faster_rcnn_R_101_FPN_3x -->
<tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml">R101-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.286</td>
<td align="center">0.051</td>
<td align="center">4.1</td>
<td align="center">42.0</td>
<td align="center">137851257</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_FPN_3x/137851257/model_final_f6e8b1.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_FPN_3x/137851257/metrics.json">metrics</a></td>
</tr>
<!-- ROW: faster_rcnn_X_101_32x8d_FPN_3x -->
<tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml">X101-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.638</td>
<td align="center">0.098</td>
<td align="center">6.7</td>
<td align="center">43.0</td>
<td align="center">139173657</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x/139173657/model_final_68b088.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x/139173657/metrics.json">metrics</a></td>
</tr>
</tbody></table>
#### RetinaNet:
<!--
./gen_html_table.py --config 'COCO-Detection/retina*50*' 'COCO-Detection/retina*101*' --name R50 R50 R101 --fields lr_sched train_speed inference_speed mem box_AP
-->
<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom">Name</th>
<th valign="bottom">lr<br/>sched</th>
<th valign="bottom">train<br/>time<br/>(s/iter)</th>
<th valign="bottom">inference<br/>time<br/>(s/im)</th>
<th valign="bottom">train<br/>mem<br/>(GB)</th>
<th valign="bottom">box<br/>AP</th>
<th valign="bottom">model id</th>
<th valign="bottom">download</th>
<!-- TABLE BODY -->
<!-- ROW: retinanet_R_50_FPN_1x -->
<tr><td align="left"><a href="configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml">R50</a></td>
<td align="center">1x</td>
<td align="center">0.205</td>
<td align="center">0.041</td>
<td align="center">4.1</td>
<td align="center">37.4</td>
<td align="center">190397773</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_1x/190397773/model_final_bfca0b.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_1x/190397773/metrics.json">metrics</a></td>
</tr>
<!-- ROW: retinanet_R_50_FPN_3x -->
<tr><td align="left"><a href="configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml">R50</a></td>
<td align="center">3x</td>
<td align="center">0.205</td>
<td align="center">0.041</td>
<td align="center">4.1</td>
<td align="center">38.7</td>
<td align="center">190397829</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_3x/190397829/model_final_5bd44e.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_3x/190397829/metrics.json">metrics</a></td>
</tr>
<!-- ROW: retinanet_R_101_FPN_3x -->
<tr><td align="left"><a href="configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml">R101</a></td>
<td align="center">3x</td>
<td align="center">0.291</td>
<td align="center">0.054</td>
<td align="center">5.2</td>
<td align="center">40.4</td>
<td align="center">190397697</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_101_FPN_3x/190397697/model_final_971ab9.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_101_FPN_3x/190397697/metrics.json">metrics</a></td>
</tr>
</tbody></table>
#### RPN & Fast R-CNN:
<!--
./gen_html_table.py --config 'COCO-Detection/rpn*' 'COCO-Detection/fast_rcnn*' --name "RPN R50-C4" "RPN R50-FPN" "Fast R-CNN R50-FPN" --fields lr_sched train_speed inference_speed mem box_AP prop_AR
-->
<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom">Name</th>
<th valign="bottom">lr<br/>sched</th>
<th valign="bottom">train<br/>time<br/>(s/iter)</th>
<th valign="bottom">inference<br/>time<br/>(s/im)</th>
<th valign="bottom">train<br/>mem<br/>(GB)</th>
<th valign="bottom">box<br/>AP</th>
<th valign="bottom">prop.<br/>AR</th>
<th valign="bottom">model id</th>
<th valign="bottom">download</th>
<!-- TABLE BODY -->
<!-- ROW: rpn_R_50_C4_1x -->
<tr><td align="left"><a href="configs/COCO-Detection/rpn_R_50_C4_1x.yaml">RPN R50-C4</a></td>
<td align="center">1x</td>
<td align="center">0.130</td>
<td align="center">0.034</td>
<td align="center">1.5</td>
<td align="center"></td>
<td align="center">51.6</td>
<td align="center">137258005</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/rpn_R_50_C4_1x/137258005/model_final_450694.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/rpn_R_50_C4_1x/137258005/metrics.json">metrics</a></td>
</tr>
<!-- ROW: rpn_R_50_FPN_1x -->
<tr><td align="left"><a href="configs/COCO-Detection/rpn_R_50_FPN_1x.yaml">RPN R50-FPN</a></td>
<td align="center">1x</td>
<td align="center">0.186</td>
<td align="center">0.032</td>
<td align="center">2.7</td>
<td align="center"></td>
<td align="center">58.0</td>
<td align="center">137258492</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/rpn_R_50_FPN_1x/137258492/model_final_02ce48.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/rpn_R_50_FPN_1x/137258492/metrics.json">metrics</a></td>
</tr>
<!-- ROW: fast_rcnn_R_50_FPN_1x -->
<tr><td align="left"><a href="configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml">Fast R-CNN R50-FPN</a></td>
<td align="center">1x</td>
<td align="center">0.140</td>
<td align="center">0.029</td>
<td align="center">2.6</td>
<td align="center">37.8</td>
<td align="center"></td>
<td align="center">137635226</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/fast_rcnn_R_50_FPN_1x/137635226/model_final_e5f7ce.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/fast_rcnn_R_50_FPN_1x/137635226/metrics.json">metrics</a></td>
</tr>
</tbody></table>
### COCO Instance Segmentation Baselines with Mask R-CNN
<!--
./gen_html_table.py --config 'COCO-InstanceSegmentation/mask*50*'{1x,3x}'*' 'COCO-InstanceSegmentation/mask*101*' --name R50-C4 R50-DC5 R50-FPN R50-C4 R50-DC5 R50-FPN R101-C4 R101-DC5 R101-FPN X101-FPN --fields lr_sched train_speed inference_speed mem box_AP mask_AP
-->
<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom">Name</th>
<th valign="bottom">lr<br/>sched</th>
<th valign="bottom">train<br/>time<br/>(s/iter)</th>
<th valign="bottom">inference<br/>time<br/>(s/im)</th>
<th valign="bottom">train<br/>mem<br/>(GB)</th>
<th valign="bottom">box<br/>AP</th>
<th valign="bottom">mask<br/>AP</th>
<th valign="bottom">model id</th>
<th valign="bottom">download</th>
<!-- TABLE BODY -->
<!-- ROW: mask_rcnn_R_50_C4_1x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml">R50-C4</a></td>
<td align="center">1x</td>
<td align="center">0.584</td>
<td align="center">0.110</td>
<td align="center">5.2</td>
<td align="center">36.8</td>
<td align="center">32.2</td>
<td align="center">137259246</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x/137259246/model_final_9243eb.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x/137259246/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_50_DC5_1x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x.yaml">R50-DC5</a></td>
<td align="center">1x</td>
<td align="center">0.471</td>
<td align="center">0.076</td>
<td align="center">6.5</td>
<td align="center">38.3</td>
<td align="center">34.2</td>
<td align="center">137260150</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x/137260150/model_final_4f86c3.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x/137260150/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_50_FPN_1x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml">R50-FPN</a></td>
<td align="center">1x</td>
<td align="center">0.261</td>
<td align="center">0.043</td>
<td align="center">3.4</td>
<td align="center">38.6</td>
<td align="center">35.2</td>
<td align="center">137260431</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/137260431/model_final_a54504.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/137260431/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_50_C4_3x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml">R50-C4</a></td>
<td align="center">3x</td>
<td align="center">0.575</td>
<td align="center">0.111</td>
<td align="center">5.2</td>
<td align="center">39.8</td>
<td align="center">34.4</td>
<td align="center">137849525</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x/137849525/model_final_4ce675.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x/137849525/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_50_DC5_3x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml">R50-DC5</a></td>
<td align="center">3x</td>
<td align="center">0.470</td>
<td align="center">0.076</td>
<td align="center">6.5</td>
<td align="center">40.0</td>
<td align="center">35.9</td>
<td align="center">137849551</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x/137849551/model_final_84107b.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x/137849551/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_50_FPN_3x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml">R50-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.261</td>
<td align="center">0.043</td>
<td align="center">3.4</td>
<td align="center">41.0</td>
<td align="center">37.2</td>
<td align="center">137849600</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_101_C4_3x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml">R101-C4</a></td>
<td align="center">3x</td>
<td align="center">0.652</td>
<td align="center">0.145</td>
<td align="center">6.3</td>
<td align="center">42.6</td>
<td align="center">36.7</td>
<td align="center">138363239</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x/138363239/model_final_a2914c.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x/138363239/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_101_DC5_3x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x.yaml">R101-DC5</a></td>
<td align="center">3x</td>
<td align="center">0.545</td>
<td align="center">0.092</td>
<td align="center">7.6</td>
<td align="center">41.9</td>
<td align="center">37.3</td>
<td align="center">138363294</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x/138363294/model_final_0464b7.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x/138363294/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_101_FPN_3x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml">R101-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.340</td>
<td align="center">0.056</td>
<td align="center">4.6</td>
<td align="center">42.9</td>
<td align="center">38.6</td>
<td align="center">138205316</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x/138205316/model_final_a3ec72.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x/138205316/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_X_101_32x8d_FPN_3x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml">X101-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.690</td>
<td align="center">0.103</td>
<td align="center">7.2</td>
<td align="center">44.3</td>
<td align="center">39.5</td>
<td align="center">139653917</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x/139653917/model_final_2d9806.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x/139653917/metrics.json">metrics</a></td>
</tr>
</tbody></table>
### COCO Person Keypoint Detection Baselines with Keypoint R-CNN
<!--
./gen_html_table.py --config 'COCO-Keypoints/*50*' 'COCO-Keypoints/*101*' --name R50-FPN R50-FPN R101-FPN X101-FPN --fields lr_sched train_speed inference_speed mem box_AP keypoint_AP
-->
<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom">Name</th>
<th valign="bottom">lr<br/>sched</th>
<th valign="bottom">train<br/>time<br/>(s/iter)</th>
<th valign="bottom">inference<br/>time<br/>(s/im)</th>
<th valign="bottom">train<br/>mem<br/>(GB)</th>
<th valign="bottom">box<br/>AP</th>
<th valign="bottom">kp.<br/>AP</th>
<th valign="bottom">model id</th>
<th valign="bottom">download</th>
<!-- TABLE BODY -->
<!-- ROW: keypoint_rcnn_R_50_FPN_1x -->
<tr><td align="left"><a href="configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.yaml">R50-FPN</a></td>
<td align="center">1x</td>
<td align="center">0.315</td>
<td align="center">0.072</td>
<td align="center">5.0</td>
<td align="center">53.6</td>
<td align="center">64.0</td>
<td align="center">137261548</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x/137261548/model_final_04e291.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x/137261548/metrics.json">metrics</a></td>
</tr>
<!-- ROW: keypoint_rcnn_R_50_FPN_3x -->
<tr><td align="left"><a href="configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml">R50-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.316</td>
<td align="center">0.066</td>
<td align="center">5.0</td>
<td align="center">55.4</td>
<td align="center">65.5</td>
<td align="center">137849621</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x/137849621/model_final_a6e10b.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x/137849621/metrics.json">metrics</a></td>
</tr>
<!-- ROW: keypoint_rcnn_R_101_FPN_3x -->
<tr><td align="left"><a href="configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x.yaml">R101-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.390</td>
<td align="center">0.076</td>
<td align="center">6.1</td>
<td align="center">56.4</td>
<td align="center">66.1</td>
<td align="center">138363331</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x/138363331/model_final_997cc7.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x/138363331/metrics.json">metrics</a></td>
</tr>
<!-- ROW: keypoint_rcnn_X_101_32x8d_FPN_3x -->
<tr><td align="left"><a href="configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml">X101-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.738</td>
<td align="center">0.121</td>
<td align="center">8.7</td>
<td align="center">57.3</td>
<td align="center">66.0</td>
<td align="center">139686956</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x/139686956/model_final_5ad38f.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x/139686956/metrics.json">metrics</a></td>
</tr>
</tbody></table>
### COCO Panoptic Segmentation Baselines with Panoptic FPN
<!--
./gen_html_table.py --config 'COCO-PanopticSegmentation/*50*' 'COCO-PanopticSegmentation/*101*' --name R50-FPN R50-FPN R101-FPN --fields lr_sched train_speed inference_speed mem box_AP mask_AP PQ
-->
<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom">Name</th>
<th valign="bottom">lr<br/>sched</th>
<th valign="bottom">train<br/>time<br/>(s/iter)</th>
<th valign="bottom">inference<br/>time<br/>(s/im)</th>
<th valign="bottom">train<br/>mem<br/>(GB)</th>
<th valign="bottom">box<br/>AP</th>
<th valign="bottom">mask<br/>AP</th>
<th valign="bottom">PQ</th>
<th valign="bottom">model id</th>
<th valign="bottom">download</th>
<!-- TABLE BODY -->
<!-- ROW: panoptic_fpn_R_50_1x -->
<tr><td align="left"><a href="configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.yaml">R50-FPN</a></td>
<td align="center">1x</td>
<td align="center">0.304</td>
<td align="center">0.053</td>
<td align="center">4.8</td>
<td align="center">37.6</td>
<td align="center">34.7</td>
<td align="center">39.4</td>
<td align="center">139514544</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x/139514544/model_final_dbfeb4.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x/139514544/metrics.json">metrics</a></td>
</tr>
<!-- ROW: panoptic_fpn_R_50_3x -->
<tr><td align="left"><a href="configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml">R50-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.302</td>
<td align="center">0.053</td>
<td align="center">4.8</td>
<td align="center">40.0</td>
<td align="center">36.5</td>
<td align="center">41.5</td>
<td align="center">139514569</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x/139514569/model_final_c10459.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x/139514569/metrics.json">metrics</a></td>
</tr>
<!-- ROW: panoptic_fpn_R_101_3x -->
<tr><td align="left"><a href="configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml">R101-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.392</td>
<td align="center">0.066</td>
<td align="center">6.0</td>
<td align="center">42.4</td>
<td align="center">38.5</td>
<td align="center">43.0</td>
<td align="center">139514519</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x/139514519/model_final_cafdb1.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x/139514519/metrics.json">metrics</a></td>
</tr>
</tbody></table>
### LVIS Instance Segmentation Baselines with Mask R-CNN
Mask R-CNN baselines on the [LVIS dataset](https://lvisdataset.org), v0.5.
These baselines are described in Table 3(c) of the [LVIS paper](https://arxiv.org/abs/1908.03195).
NOTE: the 1x schedule here has the same amount of __iterations__ as the COCO 1x baselines.
They are roughly 24 epochs of LVISv0.5 data.
The final results of these configs have large variance across different runs.
<!--
./gen_html_table.py --config 'LVISv0.5-InstanceSegmentation/mask*50*' 'LVISv0.5-InstanceSegmentation/mask*101*' --name R50-FPN R101-FPN X101-FPN --fields lr_sched train_speed inference_speed mem box_AP mask_AP
-->
<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom">Name</th>
<th valign="bottom">lr<br/>sched</th>
<th valign="bottom">train<br/>time<br/>(s/iter)</th>
<th valign="bottom">inference<br/>time<br/>(s/im)</th>
<th valign="bottom">train<br/>mem<br/>(GB)</th>
<th valign="bottom">box<br/>AP</th>
<th valign="bottom">mask<br/>AP</th>
<th valign="bottom">model id</th>
<th valign="bottom">download</th>
<!-- TABLE BODY -->
<!-- ROW: mask_rcnn_R_50_FPN_1x -->
<tr><td align="left"><a href="configs/LVISv0.5-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml">R50-FPN</a></td>
<td align="center">1x</td>
<td align="center">0.292</td>
<td align="center">0.107</td>
<td align="center">7.1</td>
<td align="center">23.6</td>
<td align="center">24.4</td>
<td align="center">144219072</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/LVISv0.5-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/144219072/model_final_571f7c.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/LVISv0.5-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/144219072/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_101_FPN_1x -->
<tr><td align="left"><a href="configs/LVISv0.5-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml">R101-FPN</a></td>
<td align="center">1x</td>
<td align="center">0.371</td>
<td align="center">0.114</td>
<td align="center">7.8</td>
<td align="center">25.6</td>
<td align="center">25.9</td>
<td align="center">144219035</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/LVISv0.5-InstanceSegmentation/mask_rcnn_R_101_FPN_1x/144219035/model_final_824ab5.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/LVISv0.5-InstanceSegmentation/mask_rcnn_R_101_FPN_1x/144219035/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_X_101_32x8d_FPN_1x -->
<tr><td align="left"><a href="configs/LVISv0.5-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml">X101-FPN</a></td>
<td align="center">1x</td>
<td align="center">0.712</td>
<td align="center">0.151</td>
<td align="center">10.2</td>
<td align="center">26.7</td>
<td align="center">27.1</td>
<td align="center">144219108</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/LVISv0.5-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x/144219108/model_final_5e3439.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/LVISv0.5-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x/144219108/metrics.json">metrics</a></td>
</tr>
</tbody></table>
### Cityscapes & Pascal VOC Baselines
Simple baselines for
* Mask R-CNN on Cityscapes instance segmentation (initialized from COCO pre-training, then trained on Cityscapes fine annotations only)
* Faster R-CNN on PASCAL VOC object detection (trained on VOC 2007 train+val + VOC 2012 train+val, tested on VOC 2007 using 11-point interpolated AP)
<!--
./gen_html_table.py --config 'Cityscapes/*' 'PascalVOC-Detection/*' --name "R50-FPN, Cityscapes" "R50-C4, VOC" --fields train_speed inference_speed mem box_AP box_AP50 mask_AP
-->
<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom">Name</th>
<th valign="bottom">train<br/>time<br/>(s/iter)</th>
<th valign="bottom">inference<br/>time<br/>(s/im)</th>
<th valign="bottom">train<br/>mem<br/>(GB)</th>
<th valign="bottom">box<br/>AP</th>
<th valign="bottom">box<br/>AP50</th>
<th valign="bottom">mask<br/>AP</th>
<th valign="bottom">model id</th>
<th valign="bottom">download</th>
<!-- TABLE BODY -->
<!-- ROW: mask_rcnn_R_50_FPN -->
<tr><td align="left"><a href="configs/Cityscapes/mask_rcnn_R_50_FPN.yaml">R50-FPN, Cityscapes</a></td>
<td align="center">0.240</td>
<td align="center">0.078</td>
<td align="center">4.4</td>
<td align="center"></td>
<td align="center"></td>
<td align="center">36.5</td>
<td align="center">142423278</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Cityscapes/mask_rcnn_R_50_FPN/142423278/model_final_af9cf5.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Cityscapes/mask_rcnn_R_50_FPN/142423278/metrics.json">metrics</a></td>
</tr>
<!-- ROW: faster_rcnn_R_50_C4 -->
<tr><td align="left"><a href="configs/PascalVOC-Detection/faster_rcnn_R_50_C4.yaml">R50-C4, VOC</a></td>
<td align="center">0.537</td>
<td align="center">0.081</td>
<td align="center">4.8</td>
<td align="center">51.9</td>
<td align="center">80.3</td>
<td align="center"></td>
<td align="center">142202221</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/PascalVOC-Detection/faster_rcnn_R_50_C4/142202221/model_final_b1acc2.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/PascalVOC-Detection/faster_rcnn_R_50_C4/142202221/metrics.json">metrics</a></td>
</tr>
</tbody></table>
### Other Settings
Ablations for Deformable Conv and Cascade R-CNN:
<!--
./gen_html_table.py --config 'COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml' 'Misc/*R_50_FPN_1x_dconv*' 'Misc/cascade*1x.yaml' 'COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml' 'Misc/*R_50_FPN_3x_dconv*' 'Misc/cascade*3x.yaml' --name "Baseline R50-FPN" "Deformable Conv" "Cascade R-CNN" "Baseline R50-FPN" "Deformable Conv" "Cascade R-CNN" --fields lr_sched train_speed inference_speed mem box_AP mask_AP
-->
<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom">Name</th>
<th valign="bottom">lr<br/>sched</th>
<th valign="bottom">train<br/>time<br/>(s/iter)</th>
<th valign="bottom">inference<br/>time<br/>(s/im)</th>
<th valign="bottom">train<br/>mem<br/>(GB)</th>
<th valign="bottom">box<br/>AP</th>
<th valign="bottom">mask<br/>AP</th>
<th valign="bottom">model id</th>
<th valign="bottom">download</th>
<!-- TABLE BODY -->
<!-- ROW: mask_rcnn_R_50_FPN_1x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml">Baseline R50-FPN</a></td>
<td align="center">1x</td>
<td align="center">0.261</td>
<td align="center">0.043</td>
<td align="center">3.4</td>
<td align="center">38.6</td>
<td align="center">35.2</td>
<td align="center">137260431</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/137260431/model_final_a54504.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/137260431/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_50_FPN_1x_dconv_c3-c5 -->
<tr><td align="left"><a href="configs/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml">Deformable Conv</a></td>
<td align="center">1x</td>
<td align="center">0.342</td>
<td align="center">0.048</td>
<td align="center">3.5</td>
<td align="center">41.5</td>
<td align="center">37.5</td>
<td align="center">138602867</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5/138602867/model_final_65c703.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5/138602867/metrics.json">metrics</a></td>
</tr>
<!-- ROW: cascade_mask_rcnn_R_50_FPN_1x -->
<tr><td align="left"><a href="configs/Misc/cascade_mask_rcnn_R_50_FPN_1x.yaml">Cascade R-CNN</a></td>
<td align="center">1x</td>
<td align="center">0.317</td>
<td align="center">0.052</td>
<td align="center">4.0</td>
<td align="center">42.1</td>
<td align="center">36.4</td>
<td align="center">138602847</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/cascade_mask_rcnn_R_50_FPN_1x/138602847/model_final_e9d89b.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/cascade_mask_rcnn_R_50_FPN_1x/138602847/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_50_FPN_3x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml">Baseline R50-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.261</td>
<td align="center">0.043</td>
<td align="center">3.4</td>
<td align="center">41.0</td>
<td align="center">37.2</td>
<td align="center">137849600</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_50_FPN_3x_dconv_c3-c5 -->
<tr><td align="left"><a href="configs/Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5.yaml">Deformable Conv</a></td>
<td align="center">3x</td>
<td align="center">0.349</td>
<td align="center">0.047</td>
<td align="center">3.5</td>
<td align="center">42.7</td>
<td align="center">38.5</td>
<td align="center">144998336</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5/144998336/model_final_821d0b.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5/144998336/metrics.json">metrics</a></td>
</tr>
<!-- ROW: cascade_mask_rcnn_R_50_FPN_3x -->
<tr><td align="left"><a href="configs/Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml">Cascade R-CNN</a></td>
<td align="center">3x</td>
<td align="center">0.328</td>
<td align="center">0.053</td>
<td align="center">4.0</td>
<td align="center">44.3</td>
<td align="center">38.5</td>
<td align="center">144998488</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/cascade_mask_rcnn_R_50_FPN_3x/144998488/model_final_480dd8.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/cascade_mask_rcnn_R_50_FPN_3x/144998488/metrics.json">metrics</a></td>
</tr>
</tbody></table>
Ablations for normalization methods, and a few models trained from scratch following [Rethinking ImageNet Pre-training](https://arxiv.org/abs/1811.08883).
(Note: The baseline uses `2fc` head while the others use [`4conv1fc` head](https://arxiv.org/abs/1803.08494))
<!--
./gen_html_table.py --config 'COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml' 'Misc/mask*50_FPN_3x_gn.yaml' 'Misc/mask*50_FPN_3x_syncbn.yaml' 'Misc/scratch*' --name "Baseline R50-FPN" "GN" "SyncBN" "GN (from scratch)" "GN (from scratch)" "SyncBN (from scratch)" --fields lr_sched train_speed inference_speed mem box_AP mask_AP
-->
<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom">Name</th>
<th valign="bottom">lr<br/>sched</th>
<th valign="bottom">train<br/>time<br/>(s/iter)</th>
<th valign="bottom">inference<br/>time<br/>(s/im)</th>
<th valign="bottom">train<br/>mem<br/>(GB)</th>
<th valign="bottom">box<br/>AP</th>
<th valign="bottom">mask<br/>AP</th>
<th valign="bottom">model id</th>
<th valign="bottom">download</th>
<!-- TABLE BODY -->
<!-- ROW: mask_rcnn_R_50_FPN_3x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml">Baseline R50-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.261</td>
<td align="center">0.043</td>
<td align="center">3.4</td>
<td align="center">41.0</td>
<td align="center">37.2</td>
<td align="center">137849600</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_50_FPN_3x_gn -->
<tr><td align="left"><a href="configs/Misc/mask_rcnn_R_50_FPN_3x_gn.yaml">GN</a></td>
<td align="center">3x</td>
<td align="center">0.309</td>
<td align="center">0.060</td>
<td align="center">5.6</td>
<td align="center">42.6</td>
<td align="center">38.6</td>
<td align="center">138602888</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_3x_gn/138602888/model_final_dc5d9e.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_3x_gn/138602888/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_50_FPN_3x_syncbn -->
<tr><td align="left"><a href="configs/Misc/mask_rcnn_R_50_FPN_3x_syncbn.yaml">SyncBN</a></td>
<td align="center">3x</td>
<td align="center">0.345</td>
<td align="center">0.053</td>
<td align="center">5.5</td>
<td align="center">41.9</td>
<td align="center">37.8</td>
<td align="center">169527823</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_3x_syncbn/169527823/model_final_3b3c51.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_3x_syncbn/169527823/metrics.json">metrics</a></td>
</tr>
<!-- ROW: scratch_mask_rcnn_R_50_FPN_3x_gn -->
<tr><td align="left"><a href="configs/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml">GN (from scratch)</a></td>
<td align="center">3x</td>
<td align="center">0.338</td>
<td align="center">0.061</td>
<td align="center">7.2</td>
<td align="center">39.9</td>
<td align="center">36.6</td>
<td align="center">138602908</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn/138602908/model_final_01ca85.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn/138602908/metrics.json">metrics</a></td>
</tr>
<!-- ROW: scratch_mask_rcnn_R_50_FPN_9x_gn -->
<tr><td align="left"><a href="configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_gn.yaml">GN (from scratch)</a></td>
<td align="center">9x</td>
<td align="center">N/A</td>
<td align="center">0.061</td>
<td align="center">7.2</td>
<td align="center">43.7</td>
<td align="center">39.6</td>
<td align="center">183808979</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_9x_gn/183808979/model_final_da7b4c.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_9x_gn/183808979/metrics.json">metrics</a></td>
</tr>
<!-- ROW: scratch_mask_rcnn_R_50_FPN_9x_syncbn -->
<tr><td align="left"><a href="configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn.yaml">SyncBN (from scratch)</a></td>
<td align="center">9x</td>
<td align="center">N/A</td>
<td align="center">0.055</td>
<td align="center">7.2</td>
<td align="center">43.6</td>
<td align="center">39.3</td>
<td align="center">184226666</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn/184226666/model_final_5ce33e.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn/184226666/metrics.json">metrics</a></td>
</tr>
</tbody></table>
A few very large models trained for a long time, for demo purposes. They are trained using multiple machines:
<!--
./gen_html_table.py --config 'Misc/panoptic_*dconv*' 'Misc/cascade_*152*' --name "Panoptic FPN R101" "Mask R-CNN X152" --fields inference_speed mem box_AP mask_AP PQ
# manually add TTA results
-->
<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom">Name</th>
<th valign="bottom">inference<br/>time<br/>(s/im)</th>
<th valign="bottom">train<br/>mem<br/>(GB)</th>
<th valign="bottom">box<br/>AP</th>
<th valign="bottom">mask<br/>AP</th>
<th valign="bottom">PQ</th>
<th valign="bottom">model id</th>
<th valign="bottom">download</th>
<!-- TABLE BODY -->
<!-- ROW: panoptic_fpn_R_101_dconv_cascade_gn_3x -->
<tr><td align="left"><a href="configs/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x.yaml">Panoptic FPN R101</a></td>
<td align="center">0.098</td>
<td align="center">11.4</td>
<td align="center">47.4</td>
<td align="center">41.3</td>
<td align="center">46.1</td>
<td align="center">139797668</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x/139797668/model_final_be35db.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x/139797668/metrics.json">metrics</a></td>
</tr>
<!-- ROW: cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv -->
<tr><td align="left"><a href="configs/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv.yaml">Mask R-CNN X152</a></td>
<td align="center">0.234</td>
<td align="center">15.1</td>
<td align="center">50.2</td>
<td align="center">44.0</td>
<td align="center"></td>
<td align="center">18131413</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv/18131413/model_0039999_e76410.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv/18131413/metrics.json">metrics</a></td>
</tr>
<!-- ROW: TTA cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv -->
<tr><td align="left">above + test-time aug.</td>
<td align="center"></td>
<td align="center"></td>
<td align="center">51.9</td>
<td align="center">45.9</td>
<td align="center"></td>
<td align="center"></td>
<td align="center"></td>
</tr>
</tbody></table>

59
README.md
View File

@ -1,2 +1,57 @@
# OWOD
Open-World Object Detection
<img src=".github/Detectron2-Logo-Horz.svg" width="300" >
Detectron2 is Facebook AI Research's next generation software system
that implements state-of-the-art object detection algorithms.
It is a ground-up rewrite of the previous version,
[Detectron](https://github.com/facebookresearch/Detectron/),
and it originates from [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/).
<div align="center">
<img src="https://user-images.githubusercontent.com/1381301/66535560-d3422200-eace-11e9-9123-5535d469db19.png"/>
</div>
### What's New
* It is powered by the [PyTorch](https://pytorch.org) deep learning framework.
* Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend,
DeepLab, etc.
* Can be used as a library to support [different projects](projects/) on top of it.
We'll open source more research projects in this way.
* It [trains much faster](https://detectron2.readthedocs.io/notes/benchmarks.html).
See our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/)
to see more demos and learn about detectron2.
## Installation
See [INSTALL.md](INSTALL.md).
## Quick Start
See [GETTING_STARTED.md](GETTING_STARTED.md),
or the [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5).
Learn more at our [documentation](https://detectron2.readthedocs.org).
And see [projects/](projects/) for some projects that are built on top of detectron2.
## Model Zoo and Baselines
We provide a large set of baseline results and trained models available for download in the [Detectron2 Model Zoo](MODEL_ZOO.md).
## License
Detectron2 is released under the [Apache 2.0 license](LICENSE).
## Citing Detectron2
If you use Detectron2 in your research or wish to refer to the baseline results published in the [Model Zoo](MODEL_ZOO.md), please use the following BibTeX entry.
```BibTeX
@misc{wu2019detectron2,
author = {Yuxin Wu and Alexander Kirillov and Francisco Massa and
Wan-Yen Lo and Ross Girshick},
title = {Detectron2},
howpublished = {\url{https://github.com/facebookresearch/detectron2}},
year = {2019}
}
```

18
configs/Base-RCNN-C4.yaml 100644
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MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
RPN:
PRE_NMS_TOPK_TEST: 6000
POST_NMS_TOPK_TEST: 1000
ROI_HEADS:
NAME: "Res5ROIHeads"
DATASETS:
TRAIN: ("coco_2017_train",)
TEST: ("coco_2017_val",)
SOLVER:
IMS_PER_BATCH: 16
BASE_LR: 0.02
STEPS: (60000, 80000)
MAX_ITER: 90000
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
VERSION: 2

31
configs/Base-RCNN-DilatedC5.yaml 100644
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MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
RESNETS:
OUT_FEATURES: ["res5"]
RES5_DILATION: 2
RPN:
IN_FEATURES: ["res5"]
PRE_NMS_TOPK_TEST: 6000
POST_NMS_TOPK_TEST: 1000
ROI_HEADS:
NAME: "StandardROIHeads"
IN_FEATURES: ["res5"]
ROI_BOX_HEAD:
NAME: "FastRCNNConvFCHead"
NUM_FC: 2
POOLER_RESOLUTION: 7
ROI_MASK_HEAD:
NAME: "MaskRCNNConvUpsampleHead"
NUM_CONV: 4
POOLER_RESOLUTION: 14
DATASETS:
TRAIN: ("coco_2017_train",)
TEST: ("coco_2017_val",)
SOLVER:
IMS_PER_BATCH: 16
BASE_LR: 0.02
STEPS: (60000, 80000)
MAX_ITER: 90000
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
VERSION: 2

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configs/Base-RCNN-FPN.yaml 100644
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MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
BACKBONE:
NAME: "build_resnet_fpn_backbone"
RESNETS:
OUT_FEATURES: ["res2", "res3", "res4", "res5"]
FPN:
IN_FEATURES: ["res2", "res3", "res4", "res5"]
ANCHOR_GENERATOR:
SIZES: [[32], [64], [128], [256], [512]] # One size for each in feature map
ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps)
RPN:
IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level
PRE_NMS_TOPK_TEST: 1000 # Per FPN level
# Detectron1 uses 2000 proposals per-batch,
# (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
# which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
POST_NMS_TOPK_TRAIN: 1000
POST_NMS_TOPK_TEST: 1000
ROI_HEADS:
NAME: "StandardROIHeads"
IN_FEATURES: ["p2", "p3", "p4", "p5"]
ROI_BOX_HEAD:
NAME: "FastRCNNConvFCHead"
NUM_FC: 2
POOLER_RESOLUTION: 7
ROI_MASK_HEAD:
NAME: "MaskRCNNConvUpsampleHead"
NUM_CONV: 4
POOLER_RESOLUTION: 14
DATASETS:
TRAIN: ("coco_2017_train",)
TEST: ("coco_2017_val",)
SOLVER:
IMS_PER_BATCH: 16
BASE_LR: 0.02
STEPS: (60000, 80000)
MAX_ITER: 90000
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
VERSION: 2

25
configs/Base-RetinaNet.yaml 100644
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MODEL:
META_ARCHITECTURE: "RetinaNet"
BACKBONE:
NAME: "build_retinanet_resnet_fpn_backbone"
RESNETS:
OUT_FEATURES: ["res3", "res4", "res5"]
ANCHOR_GENERATOR:
SIZES: !!python/object/apply:eval ["[[x, x * 2**(1.0/3), x * 2**(2.0/3) ] for x in [32, 64, 128, 256, 512 ]]"]
FPN:
IN_FEATURES: ["res3", "res4", "res5"]
RETINANET:
IOU_THRESHOLDS: [0.4, 0.5]
IOU_LABELS: [0, -1, 1]
SMOOTH_L1_LOSS_BETA: 0.0
DATASETS:
TRAIN: ("coco_2017_train",)
TEST: ("coco_2017_val",)
SOLVER:
IMS_PER_BATCH: 16
BASE_LR: 0.01 # Note that RetinaNet uses a different default learning rate
STEPS: (60000, 80000)
MAX_ITER: 90000
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
VERSION: 2

17
configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml 100644
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: False
LOAD_PROPOSALS: True
RESNETS:
DEPTH: 50
PROPOSAL_GENERATOR:
NAME: "PrecomputedProposals"
DATASETS:
TRAIN: ("coco_2017_train",)
PROPOSAL_FILES_TRAIN: ("detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/coco_2017_train_box_proposals_21bc3a.pkl", )
TEST: ("coco_2017_val",)
PROPOSAL_FILES_TEST: ("detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/coco_2017_val_box_proposals_ee0dad.pkl", )
DATALOADER:
# proposals are part of the dataset_dicts, and take a lot of RAM
NUM_WORKERS: 2

9
configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml 100644
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_BASE_: "../Base-RCNN-C4.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
MASK_ON: False
RESNETS:
DEPTH: 101
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

9
configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml 100644
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@ -0,0 +1,9 @@
_BASE_: "../Base-RCNN-DilatedC5.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
MASK_ON: False
RESNETS:
DEPTH: 101
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

9
configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml 100644
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@ -0,0 +1,9 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
MASK_ON: False
RESNETS:
DEPTH: 101
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

6
configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml 100644
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@ -0,0 +1,6 @@
_BASE_: "../Base-RCNN-C4.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: False
RESNETS:
DEPTH: 50

9
configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml 100644
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@ -0,0 +1,9 @@
_BASE_: "../Base-RCNN-C4.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: False
RESNETS:
DEPTH: 50
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

6
configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml 100644
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@ -0,0 +1,6 @@
_BASE_: "../Base-RCNN-DilatedC5.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: False
RESNETS:
DEPTH: 50

9
configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml 100644
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@ -0,0 +1,9 @@
_BASE_: "../Base-RCNN-DilatedC5.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: False
RESNETS:
DEPTH: 50
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

6
configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml 100644
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@ -0,0 +1,6 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: False
RESNETS:
DEPTH: 50

9
configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml 100644
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@ -0,0 +1,9 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: False
RESNETS:
DEPTH: 50
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

13
configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml 100644
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@ -0,0 +1,13 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
MASK_ON: False
WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl"
PIXEL_STD: [57.375, 57.120, 58.395]
RESNETS:
STRIDE_IN_1X1: False # this is a C2 model
NUM_GROUPS: 32
WIDTH_PER_GROUP: 8
DEPTH: 101
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

8
configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml 100644
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@ -0,0 +1,8 @@
_BASE_: "../Base-RetinaNet.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
RESNETS:
DEPTH: 101
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

5
configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml 100644
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@ -0,0 +1,5 @@
_BASE_: "../Base-RetinaNet.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
RESNETS:
DEPTH: 50

8
configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml 100644
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@ -0,0 +1,8 @@
_BASE_: "../Base-RetinaNet.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
RESNETS:
DEPTH: 50
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

10
configs/COCO-Detection/rpn_R_50_C4_1x.yaml 100644
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@ -0,0 +1,10 @@
_BASE_: "../Base-RCNN-C4.yaml"
MODEL:
META_ARCHITECTURE: "ProposalNetwork"
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: False
RESNETS:
DEPTH: 50
RPN:
PRE_NMS_TOPK_TEST: 12000
POST_NMS_TOPK_TEST: 2000

9
configs/COCO-Detection/rpn_R_50_FPN_1x.yaml 100644
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@ -0,0 +1,9 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
META_ARCHITECTURE: "ProposalNetwork"
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: False
RESNETS:
DEPTH: 50
RPN:
POST_NMS_TOPK_TEST: 2000

9
configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml 100644
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@ -0,0 +1,9 @@
_BASE_: "../Base-RCNN-C4.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
MASK_ON: True
RESNETS:
DEPTH: 101
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

9
configs/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x.yaml 100644
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@ -0,0 +1,9 @@
_BASE_: "../Base-RCNN-DilatedC5.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
MASK_ON: True
RESNETS:
DEPTH: 101
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

9
configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml 100644
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@ -0,0 +1,9 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
MASK_ON: True
RESNETS:
DEPTH: 101
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

6
configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml 100644
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@ -0,0 +1,6 @@
_BASE_: "../Base-RCNN-C4.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
RESNETS:
DEPTH: 50

9
configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml 100644
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@ -0,0 +1,9 @@
_BASE_: "../Base-RCNN-C4.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
RESNETS:
DEPTH: 50
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

6
configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x.yaml 100644
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@ -0,0 +1,6 @@
_BASE_: "../Base-RCNN-DilatedC5.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
RESNETS:
DEPTH: 50

9
configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml 100644
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@ -0,0 +1,9 @@
_BASE_: "../Base-RCNN-DilatedC5.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
RESNETS:
DEPTH: 50
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

6
configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml 100644
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@ -0,0 +1,6 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
RESNETS:
DEPTH: 50

12
configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x_giou.yaml 100644
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@ -0,0 +1,12 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
RESNETS:
DEPTH: 50
RPN:
BBOX_REG_LOSS_TYPE: "giou"
BBOX_REG_LOSS_WEIGHT: 2.0
ROI_BOX_HEAD:
BBOX_REG_LOSS_TYPE: "giou"
BBOX_REG_LOSS_WEIGHT: 10.0

9
configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml 100644
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@ -0,0 +1,9 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
RESNETS:
DEPTH: 50
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

13
configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml 100644
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@ -0,0 +1,13 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
MASK_ON: True
WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl"
PIXEL_STD: [57.375, 57.120, 58.395]
RESNETS:
STRIDE_IN_1X1: False # this is a C2 model
NUM_GROUPS: 32
WIDTH_PER_GROUP: 8
DEPTH: 101
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

15
configs/COCO-Keypoints/Base-Keypoint-RCNN-FPN.yaml 100644
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@ -0,0 +1,15 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
KEYPOINT_ON: True
ROI_HEADS:
NUM_CLASSES: 1
ROI_BOX_HEAD:
SMOOTH_L1_BETA: 0.5 # Keypoint AP degrades (though box AP improves) when using plain L1 loss
RPN:
# Detectron1 uses 2000 proposals per-batch, but this option is per-image in detectron2.
# 1000 proposals per-image is found to hurt box AP.
# Therefore we increase it to 1500 per-image.
POST_NMS_TOPK_TRAIN: 1500
DATASETS:
TRAIN: ("keypoints_coco_2017_train",)
TEST: ("keypoints_coco_2017_val",)

8
configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x.yaml 100644
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@ -0,0 +1,8 @@
_BASE_: "Base-Keypoint-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
RESNETS:
DEPTH: 101
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

5
configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.yaml 100644
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@ -0,0 +1,5 @@
_BASE_: "Base-Keypoint-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
RESNETS:
DEPTH: 50

8
configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml 100644
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@ -0,0 +1,8 @@
_BASE_: "Base-Keypoint-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
RESNETS:
DEPTH: 50
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

12
configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml 100644
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@ -0,0 +1,12 @@
_BASE_: "Base-Keypoint-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl"
PIXEL_STD: [57.375, 57.120, 58.395]
RESNETS:
STRIDE_IN_1X1: False # this is a C2 model
NUM_GROUPS: 32
WIDTH_PER_GROUP: 8
DEPTH: 101
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

11
configs/COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml 100644
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@ -0,0 +1,11 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
META_ARCHITECTURE: "PanopticFPN"
MASK_ON: True
SEM_SEG_HEAD:
LOSS_WEIGHT: 0.5
DATASETS:
TRAIN: ("coco_2017_train_panoptic_separated",)
TEST: ("coco_2017_val_panoptic_separated",)
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: False

8
configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml 100644
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@ -0,0 +1,8 @@
_BASE_: "Base-Panoptic-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
RESNETS:
DEPTH: 101
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

5
configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.yaml 100644
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@ -0,0 +1,5 @@
_BASE_: "Base-Panoptic-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
RESNETS:
DEPTH: 50

8
configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml 100644
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@ -0,0 +1,8 @@
_BASE_: "Base-Panoptic-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
RESNETS:
DEPTH: 50
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

27
configs/Cityscapes/mask_rcnn_R_50_FPN.yaml 100644
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@ -0,0 +1,27 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
# WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
# For better, more stable performance initialize from COCO
WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl"
MASK_ON: True
ROI_HEADS:
NUM_CLASSES: 8
# This is similar to the setting used in Mask R-CNN paper, Appendix A
# But there are some differences, e.g., we did not initialize the output
# layer using the corresponding classes from COCO
INPUT:
MIN_SIZE_TRAIN: (800, 832, 864, 896, 928, 960, 992, 1024)
MIN_SIZE_TRAIN_SAMPLING: "choice"
MIN_SIZE_TEST: 1024
MAX_SIZE_TRAIN: 2048
MAX_SIZE_TEST: 2048
DATASETS:
TRAIN: ("cityscapes_fine_instance_seg_train",)
TEST: ("cityscapes_fine_instance_seg_val",)
SOLVER:
BASE_LR: 0.01
STEPS: (18000,)
MAX_ITER: 24000
IMS_PER_BATCH: 8
TEST:
EVAL_PERIOD: 8000

83
configs/Detectron1-Comparisons/README.md 100644
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@ -0,0 +1,83 @@
Detectron2 model zoo's experimental settings and a few implementation details are different from Detectron.
The differences in implementation details are shared in
[Compatibility with Other Libraries](../../docs/notes/compatibility.md).
The differences in model zoo's experimental settings include:
* Use scale augmentation during training. This improves AP with lower training cost.
* Use L1 loss instead of smooth L1 loss for simplicity. This sometimes improves box AP but may
affect other AP.
* Use `POOLER_SAMPLING_RATIO=0` instead of 2. This does not significantly affect AP.
* Use `ROIAlignV2`. This does not significantly affect AP.
In this directory, we provide a few configs that __do not__ have the above changes.
They mimic Detectron's behavior as close as possible,
and provide a fair comparison of accuracy and speed against Detectron.
<!--
./gen_html_table.py --config 'Detectron1-Comparisons/*.yaml' --name "Faster R-CNN" "Keypoint R-CNN" "Mask R-CNN" --fields lr_sched train_speed inference_speed mem box_AP mask_AP keypoint_AP --base-dir ../../../configs/Detectron1-Comparisons
-->
<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom">Name</th>
<th valign="bottom">lr<br/>sched</th>
<th valign="bottom">train<br/>time<br/>(s/iter)</th>
<th valign="bottom">inference<br/>time<br/>(s/im)</th>
<th valign="bottom">train<br/>mem<br/>(GB)</th>
<th valign="bottom">box<br/>AP</th>
<th valign="bottom">mask<br/>AP</th>
<th valign="bottom">kp.<br/>AP</th>
<th valign="bottom">model id</th>
<th valign="bottom">download</th>
<!-- TABLE BODY -->
<!-- ROW: faster_rcnn_R_50_FPN_noaug_1x -->
<tr><td align="left"><a href="faster_rcnn_R_50_FPN_noaug_1x.yaml">Faster R-CNN</a></td>
<td align="center">1x</td>
<td align="center">0.219</td>
<td align="center">0.038</td>
<td align="center">3.1</td>
<td align="center">36.9</td>
<td align="center"></td>
<td align="center"></td>
<td align="center">137781054</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x/137781054/model_final_7ab50c.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x/137781054/metrics.json">metrics</a></td>
</tr>
<!-- ROW: keypoint_rcnn_R_50_FPN_1x -->
<tr><td align="left"><a href="keypoint_rcnn_R_50_FPN_1x.yaml">Keypoint R-CNN</a></td>
<td align="center">1x</td>
<td align="center">0.313</td>
<td align="center">0.071</td>
<td align="center">5.0</td>
<td align="center">53.1</td>
<td align="center"></td>
<td align="center">64.2</td>
<td align="center">137781195</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x/137781195/model_final_cce136.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x/137781195/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_50_FPN_noaug_1x -->
<tr><td align="left"><a href="mask_rcnn_R_50_FPN_noaug_1x.yaml">Mask R-CNN</a></td>
<td align="center">1x</td>
<td align="center">0.273</td>
<td align="center">0.043</td>
<td align="center">3.4</td>
<td align="center">37.8</td>
<td align="center">34.9</td>
<td align="center"></td>
<td align="center">137781281</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x/137781281/model_final_62ca52.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x/137781281/metrics.json">metrics</a></td>
</tr>
</tbody></table>
## Comparisons:
* Faster R-CNN: Detectron's AP is 36.7, similar to ours.
* Keypoint R-CNN: Detectron's AP is box 53.6, keypoint 64.2. Fixing a Detectron's
[bug](https://github.com/facebookresearch/Detectron/issues/459) lead to a drop in box AP, and can be
compensated back by some parameter tuning.
* Mask R-CNN: Detectron's AP is box 37.7, mask 33.9. We're 1 AP better in mask AP, due to more correct implementation.
For speed comparison, see [benchmarks](https://detectron2.readthedocs.io/notes/benchmarks.html).

17
configs/Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x.yaml 100644
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: False
RESNETS:
DEPTH: 50
# Detectron1 uses smooth L1 loss with some magic beta values.
# The defaults are changed to L1 loss in Detectron2.
RPN:
SMOOTH_L1_BETA: 0.1111
ROI_BOX_HEAD:
SMOOTH_L1_BETA: 1.0
POOLER_SAMPLING_RATIO: 2
POOLER_TYPE: "ROIAlign"
INPUT:
# no scale augmentation
MIN_SIZE_TRAIN: (800, )

27
configs/Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x.yaml 100644
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@ -0,0 +1,27 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
KEYPOINT_ON: True
RESNETS:
DEPTH: 50
ROI_HEADS:
NUM_CLASSES: 1
ROI_KEYPOINT_HEAD:
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 2
POOLER_TYPE: "ROIAlign"
# Detectron1 uses smooth L1 loss with some magic beta values.
# The defaults are changed to L1 loss in Detectron2.
ROI_BOX_HEAD:
SMOOTH_L1_BETA: 1.0
POOLER_SAMPLING_RATIO: 2
POOLER_TYPE: "ROIAlign"
RPN:
SMOOTH_L1_BETA: 0.1111
# Detectron1 uses 2000 proposals per-batch, but this option is per-image in detectron2
# 1000 proposals per-image is found to hurt box AP.
# Therefore we increase it to 1500 per-image.
POST_NMS_TOPK_TRAIN: 1500
DATASETS:
TRAIN: ("keypoints_coco_2017_train",)
TEST: ("keypoints_coco_2017_val",)

20
configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml 100644
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@ -0,0 +1,20 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
RESNETS:
DEPTH: 50
# Detectron1 uses smooth L1 loss with some magic beta values.
# The defaults are changed to L1 loss in Detectron2.
RPN:
SMOOTH_L1_BETA: 0.1111
ROI_BOX_HEAD:
SMOOTH_L1_BETA: 1.0
POOLER_SAMPLING_RATIO: 2
POOLER_TYPE: "ROIAlign"
ROI_MASK_HEAD:
POOLER_SAMPLING_RATIO: 2
POOLER_TYPE: "ROIAlign"
INPUT:
# no scale augmentation
MIN_SIZE_TRAIN: (800, )

19
configs/LVISv0.5-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml 100644
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@ -0,0 +1,19 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
MASK_ON: True
RESNETS:
DEPTH: 101
ROI_HEADS:
NUM_CLASSES: 1230
SCORE_THRESH_TEST: 0.0001
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
DATASETS:
TRAIN: ("lvis_v0.5_train",)
TEST: ("lvis_v0.5_val",)
TEST:
DETECTIONS_PER_IMAGE: 300 # LVIS allows up to 300
DATALOADER:
SAMPLER_TRAIN: "RepeatFactorTrainingSampler"
REPEAT_THRESHOLD: 0.001

19
configs/LVISv0.5-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml 100644
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@ -0,0 +1,19 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
RESNETS:
DEPTH: 50
ROI_HEADS:
NUM_CLASSES: 1230
SCORE_THRESH_TEST: 0.0001
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
DATASETS:
TRAIN: ("lvis_v0.5_train",)
TEST: ("lvis_v0.5_val",)
TEST:
DETECTIONS_PER_IMAGE: 300 # LVIS allows up to 300
DATALOADER:
SAMPLER_TRAIN: "RepeatFactorTrainingSampler"
REPEAT_THRESHOLD: 0.001

23
configs/LVISv0.5-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml 100644
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@ -0,0 +1,23 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl"
PIXEL_STD: [57.375, 57.120, 58.395]
MASK_ON: True
RESNETS:
STRIDE_IN_1X1: False # this is a C2 model
NUM_GROUPS: 32
WIDTH_PER_GROUP: 8
DEPTH: 101
ROI_HEADS:
NUM_CLASSES: 1230
SCORE_THRESH_TEST: 0.0001
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
DATASETS:
TRAIN: ("lvis_v0.5_train",)
TEST: ("lvis_v0.5_val",)
TEST:
DETECTIONS_PER_IMAGE: 300 # LVIS allows up to 300
DATALOADER:
SAMPLER_TRAIN: "RepeatFactorTrainingSampler"
REPEAT_THRESHOLD: 0.001

22
configs/LVISv1-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml 100644
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@ -0,0 +1,22 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
MASK_ON: True
RESNETS:
DEPTH: 101
ROI_HEADS:
NUM_CLASSES: 1203
SCORE_THRESH_TEST: 0.0001
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
DATASETS:
TRAIN: ("lvis_v1_train",)
TEST: ("lvis_v1_val",)
TEST:
DETECTIONS_PER_IMAGE: 300 # LVIS allows up to 300
SOLVER:
STEPS: (120000, 160000)
MAX_ITER: 180000 # 180000 * 16 / 100000 ~ 28.8 epochs
DATALOADER:
SAMPLER_TRAIN: "RepeatFactorTrainingSampler"
REPEAT_THRESHOLD: 0.001

22
configs/LVISv1-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml 100644
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@ -0,0 +1,22 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
RESNETS:
DEPTH: 50
ROI_HEADS:
NUM_CLASSES: 1203
SCORE_THRESH_TEST: 0.0001
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
DATASETS:
TRAIN: ("lvis_v1_train",)
TEST: ("lvis_v1_val",)
TEST:
DETECTIONS_PER_IMAGE: 300 # LVIS allows up to 300
SOLVER:
STEPS: (120000, 160000)
MAX_ITER: 180000 # 180000 * 16 / 100000 ~ 28.8 epochs
DATALOADER:
SAMPLER_TRAIN: "RepeatFactorTrainingSampler"
REPEAT_THRESHOLD: 0.001

26
configs/LVISv1-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml 100644
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl"
PIXEL_STD: [57.375, 57.120, 58.395]
MASK_ON: True
RESNETS:
STRIDE_IN_1X1: False # this is a C2 model
NUM_GROUPS: 32
WIDTH_PER_GROUP: 8
DEPTH: 101
ROI_HEADS:
NUM_CLASSES: 1203
SCORE_THRESH_TEST: 0.0001
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
DATASETS:
TRAIN: ("lvis_v1_train",)
TEST: ("lvis_v1_val",)
SOLVER:
STEPS: (120000, 160000)
MAX_ITER: 180000 # 180000 * 16 / 100000 ~ 28.8 epochs
TEST:
DETECTIONS_PER_IMAGE: 300 # LVIS allows up to 300
DATALOADER:
SAMPLER_TRAIN: "RepeatFactorTrainingSampler"
REPEAT_THRESHOLD: 0.001

12
configs/Misc/cascade_mask_rcnn_R_50_FPN_1x.yaml 100644
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
RESNETS:
DEPTH: 50
ROI_HEADS:
NAME: CascadeROIHeads
ROI_BOX_HEAD:
CLS_AGNOSTIC_BBOX_REG: True
RPN:
POST_NMS_TOPK_TRAIN: 2000

15
configs/Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml 100644
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
RESNETS:
DEPTH: 50
ROI_HEADS:
NAME: CascadeROIHeads
ROI_BOX_HEAD:
CLS_AGNOSTIC_BBOX_REG: True
RPN:
POST_NMS_TOPK_TRAIN: 2000
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

36
configs/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv.yaml 100644
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
MASK_ON: True
WEIGHTS: "catalog://ImageNetPretrained/FAIR/X-152-32x8d-IN5k"
RESNETS:
STRIDE_IN_1X1: False # this is a C2 model
NUM_GROUPS: 32
WIDTH_PER_GROUP: 8
DEPTH: 152
DEFORM_ON_PER_STAGE: [False, True, True, True]
ROI_HEADS:
NAME: "CascadeROIHeads"
ROI_BOX_HEAD:
NAME: "FastRCNNConvFCHead"
NUM_CONV: 4
NUM_FC: 1
NORM: "GN"
CLS_AGNOSTIC_BBOX_REG: True
ROI_MASK_HEAD:
NUM_CONV: 8
NORM: "GN"
RPN:
POST_NMS_TOPK_TRAIN: 2000
SOLVER:
IMS_PER_BATCH: 128
STEPS: (35000, 45000)
MAX_ITER: 50000
BASE_LR: 0.16
INPUT:
MIN_SIZE_TRAIN: (640, 864)
MIN_SIZE_TRAIN_SAMPLING: "range"
MAX_SIZE_TRAIN: 1440
CROP:
ENABLED: True
TEST:
EVAL_PERIOD: 2500

10
configs/Misc/mask_rcnn_R_50_FPN_1x_cls_agnostic.yaml 100644
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
RESNETS:
DEPTH: 50
ROI_BOX_HEAD:
CLS_AGNOSTIC_BBOX_REG: True
ROI_MASK_HEAD:
CLS_AGNOSTIC_MASK: True

8
configs/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml 100644
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
RESNETS:
DEPTH: 50
DEFORM_ON_PER_STAGE: [False, True, True, True] # on Res3,Res4,Res5
DEFORM_MODULATED: False

11
configs/Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5.yaml 100644
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
RESNETS:
DEPTH: 50
DEFORM_ON_PER_STAGE: [False, True, True, True] # on Res3,Res4,Res5
DEFORM_MODULATED: False
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 270000

21
configs/Misc/mask_rcnn_R_50_FPN_3x_gn.yaml 100644
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "catalog://ImageNetPretrained/FAIR/R-50-GN"
MASK_ON: True
RESNETS:
DEPTH: 50
NORM: "GN"
STRIDE_IN_1X1: False
FPN:
NORM: "GN"
ROI_BOX_HEAD:
NAME: "FastRCNNConvFCHead"
NUM_CONV: 4
NUM_FC: 1
NORM: "GN"
ROI_MASK_HEAD:
NORM: "GN"
SOLVER:
# 3x schedule
STEPS: (210000, 250000)
MAX_ITER: 270000

24
configs/Misc/mask_rcnn_R_50_FPN_3x_syncbn.yaml 100644
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
RESNETS:
DEPTH: 50
NORM: "SyncBN"
STRIDE_IN_1X1: True
FPN:
NORM: "SyncBN"
ROI_BOX_HEAD:
NAME: "FastRCNNConvFCHead"
NUM_CONV: 4
NUM_FC: 1
NORM: "SyncBN"
ROI_MASK_HEAD:
NORM: "SyncBN"
SOLVER:
# 3x schedule
STEPS: (210000, 250000)
MAX_ITER: 270000
TEST:
PRECISE_BN:
ENABLED: True

26
configs/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x.yaml 100644
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# A large PanopticFPN for demo purposes.
# Use GN on backbone to support semantic seg.
# Use Cascade + Deform Conv to improve localization.
_BASE_: "../COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml"
MODEL:
WEIGHTS: "catalog://ImageNetPretrained/FAIR/R-101-GN"
RESNETS:
DEPTH: 101
NORM: "GN"
DEFORM_ON_PER_STAGE: [False, True, True, True]
STRIDE_IN_1X1: False
FPN:
NORM: "GN"
ROI_HEADS:
NAME: CascadeROIHeads
ROI_BOX_HEAD:
CLS_AGNOSTIC_BBOX_REG: True
ROI_MASK_HEAD:
NORM: "GN"
RPN:
POST_NMS_TOPK_TRAIN: 2000
SOLVER:
STEPS: (105000, 125000)
MAX_ITER: 135000
IMS_PER_BATCH: 32
BASE_LR: 0.04

13
configs/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml 100644
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_BASE_: "mask_rcnn_R_50_FPN_3x_gn.yaml"
MODEL:
# Train from random initialization.
WEIGHTS: ""
# It makes sense to divide by STD when training from scratch
# But it seems to make no difference on the results and C2's models didn't do this.
# So we keep things consistent with C2.
# PIXEL_STD: [57.375, 57.12, 58.395]
MASK_ON: True
BACKBONE:
FREEZE_AT: 0
# NOTE: Please refer to Rethinking ImageNet Pre-training https://arxiv.org/abs/1811.08883
# to learn what you need for training from scratch.

19
configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_gn.yaml 100644
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@ -0,0 +1,19 @@
_BASE_: "mask_rcnn_R_50_FPN_3x_gn.yaml"
MODEL:
PIXEL_STD: [57.375, 57.12, 58.395]
WEIGHTS: ""
MASK_ON: True
RESNETS:
STRIDE_IN_1X1: False
BACKBONE:
FREEZE_AT: 0
SOLVER:
# 9x schedule
IMS_PER_BATCH: 64 # 4x the standard
STEPS: (187500, 197500) # last 60/4==15k and last 20/4==5k
MAX_ITER: 202500 # 90k * 9 / 4
BASE_LR: 0.08
TEST:
EVAL_PERIOD: 2500
# NOTE: Please refer to Rethinking ImageNet Pre-training https://arxiv.org/abs/1811.08883
# to learn what you need for training from scratch.

19
configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn.yaml 100644
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_BASE_: "mask_rcnn_R_50_FPN_3x_syncbn.yaml"
MODEL:
PIXEL_STD: [57.375, 57.12, 58.395]
WEIGHTS: ""
MASK_ON: True
RESNETS:
STRIDE_IN_1X1: False
BACKBONE:
FREEZE_AT: 0
SOLVER:
# 9x schedule
IMS_PER_BATCH: 64 # 4x the standard
STEPS: (187500, 197500) # last 60/4==15k and last 20/4==5k
MAX_ITER: 202500 # 90k * 9 / 4
BASE_LR: 0.08
TEST:
EVAL_PERIOD: 2500
# NOTE: Please refer to Rethinking ImageNet Pre-training https://arxiv.org/abs/1811.08883
# to learn what you need for training from scratch.

11
configs/Misc/semantic_R_50_FPN_1x.yaml 100644
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
META_ARCHITECTURE: "SemanticSegmentor"
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
RESNETS:
DEPTH: 50
DATASETS:
TRAIN: ("coco_2017_train_panoptic_stuffonly",)
TEST: ("coco_2017_val_panoptic_stuffonly",)
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)

18
configs/PascalVOC-Detection/faster_rcnn_R_50_C4.yaml 100644
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@ -0,0 +1,18 @@
_BASE_: "../Base-RCNN-C4.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: False
RESNETS:
DEPTH: 50
ROI_HEADS:
NUM_CLASSES: 20
INPUT:
MIN_SIZE_TRAIN: (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800)
MIN_SIZE_TEST: 800
DATASETS:
TRAIN: ('voc_2007_trainval', 'voc_2012_trainval')
TEST: ('voc_2007_test',)
SOLVER:
STEPS: (12000, 16000)
MAX_ITER: 18000 # 17.4 epochs
WARMUP_ITERS: 100

18
configs/PascalVOC-Detection/faster_rcnn_R_50_FPN.yaml 100644
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@ -0,0 +1,18 @@
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: False
RESNETS:
DEPTH: 50
ROI_HEADS:
NUM_CLASSES: 20
INPUT:
MIN_SIZE_TRAIN: (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800)
MIN_SIZE_TEST: 800
DATASETS:
TRAIN: ('voc_2007_trainval', 'voc_2012_trainval')
TEST: ('voc_2007_test',)
SOLVER:
STEPS: (12000, 16000)
MAX_ITER: 18000 # 17.4 epochs
WARMUP_ITERS: 100

1
configs/quick_schedules/README.md 100644
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@ -0,0 +1 @@
These are quick configs for performance or accuracy regression tracking purposes.

7
configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_inference_acc_test.yaml 100644
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_BASE_: "../Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml"
MODEL:
WEIGHTS: "detectron2://Misc/cascade_mask_rcnn_R_50_FPN_3x/144998488/model_final_480dd8.pkl"
DATASETS:
TEST: ("coco_2017_val_100",)
TEST:
EXPECTED_RESULTS: [["bbox", "AP", 50.18, 0.02], ["segm", "AP", 43.87, 0.02]]

11
configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_instant_test.yaml 100644
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_BASE_: "../Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml"
DATASETS:
TRAIN: ("coco_2017_val_100",)
TEST: ("coco_2017_val_100",)
SOLVER:
BASE_LR: 0.005
STEPS: (30,)
MAX_ITER: 40
IMS_PER_BATCH: 4
DATALOADER:
NUM_WORKERS: 2

7
configs/quick_schedules/fast_rcnn_R_50_FPN_inference_acc_test.yaml 100644
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_BASE_: "../COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml"
MODEL:
WEIGHTS: "detectron2://COCO-Detection/fast_rcnn_R_50_FPN_1x/137635226/model_final_e5f7ce.pkl"
DATASETS:
TEST: ("coco_2017_val_100",)
TEST:
EXPECTED_RESULTS: [["bbox", "AP", 45.70, 0.02]]

15
configs/quick_schedules/fast_rcnn_R_50_FPN_instant_test.yaml 100644
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@ -0,0 +1,15 @@
_BASE_: "../COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
DATASETS:
TRAIN: ("coco_2017_val_100",)
PROPOSAL_FILES_TRAIN: ("detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/coco_2017_val_box_proposals_ee0dad.pkl", )
TEST: ("coco_2017_val_100",)
PROPOSAL_FILES_TEST: ("detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/coco_2017_val_box_proposals_ee0dad.pkl", )
SOLVER:
BASE_LR: 0.005
STEPS: (30,)
MAX_ITER: 40
IMS_PER_BATCH: 4
DATALOADER:
NUM_WORKERS: 2

7
configs/quick_schedules/keypoint_rcnn_R_50_FPN_inference_acc_test.yaml 100644
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_BASE_: "../COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml"
MODEL:
WEIGHTS: "detectron2://COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x/137849621/model_final_a6e10b.pkl"
DATASETS:
TEST: ("keypoints_coco_2017_val_100",)
TEST:
EXPECTED_RESULTS: [["bbox", "AP", 52.47, 0.02], ["keypoints", "AP", 67.36, 0.02]]

14
configs/quick_schedules/keypoint_rcnn_R_50_FPN_instant_test.yaml 100644
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
KEYPOINT_ON: True
DATASETS:
TRAIN: ("keypoints_coco_2017_val_100",)
TEST: ("keypoints_coco_2017_val_100",)
SOLVER:
BASE_LR: 0.005
STEPS: (30,)
MAX_ITER: 40
IMS_PER_BATCH: 4
DATALOADER:
NUM_WORKERS: 2

30
configs/quick_schedules/keypoint_rcnn_R_50_FPN_normalized_training_acc_test.yaml 100644
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
KEYPOINT_ON: True
RESNETS:
DEPTH: 50
ROI_HEADS:
BATCH_SIZE_PER_IMAGE: 256
NUM_CLASSES: 1
ROI_KEYPOINT_HEAD:
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 2
NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: False
LOSS_WEIGHT: 4.0
ROI_BOX_HEAD:
SMOOTH_L1_BETA: 1.0 # Keypoint AP degrades when using plain L1 loss
RPN:
SMOOTH_L1_BETA: 0.2 # Keypoint AP degrades when using plain L1 loss
DATASETS:
TRAIN: ("keypoints_coco_2017_val",)
TEST: ("keypoints_coco_2017_val",)
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
SOLVER:
WARMUP_FACTOR: 0.33333333
WARMUP_ITERS: 100
STEPS: (5500, 5800)
MAX_ITER: 6000
TEST:
EXPECTED_RESULTS: [["bbox", "AP", 55.35, 1.0], ["keypoints", "AP", 76.91, 1.0]]

28
configs/quick_schedules/keypoint_rcnn_R_50_FPN_training_acc_test.yaml 100644
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
KEYPOINT_ON: True
RESNETS:
DEPTH: 50
ROI_HEADS:
BATCH_SIZE_PER_IMAGE: 256
NUM_CLASSES: 1
ROI_KEYPOINT_HEAD:
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 2
ROI_BOX_HEAD:
SMOOTH_L1_BETA: 1.0 # Keypoint AP degrades when using plain L1 loss
RPN:
SMOOTH_L1_BETA: 0.2 # Keypoint AP degrades when using plain L1 loss
DATASETS:
TRAIN: ("keypoints_coco_2017_val",)
TEST: ("keypoints_coco_2017_val",)
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
SOLVER:
WARMUP_FACTOR: 0.33333333
WARMUP_ITERS: 100
STEPS: (5500, 5800)
MAX_ITER: 6000
TEST:
EXPECTED_RESULTS: [["bbox", "AP", 53.5, 1.0], ["keypoints", "AP", 72.4, 1.0]]

18
configs/quick_schedules/mask_rcnn_R_50_C4_GCV_instant_test.yaml 100644
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_BASE_: "../Base-RCNN-C4.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
DATASETS:
TRAIN: ("coco_2017_val_100",)
TEST: ("coco_2017_val_100",)
SOLVER:
BASE_LR: 0.001
STEPS: (30,)
MAX_ITER: 40
IMS_PER_BATCH: 4
CLIP_GRADIENTS:
ENABLED: True
CLIP_TYPE: "value"
CLIP_VALUE: 1.0
DATALOADER:
NUM_WORKERS: 2

7
configs/quick_schedules/mask_rcnn_R_50_C4_inference_acc_test.yaml 100644
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_BASE_: "../COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml"
MODEL:
WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x/137849525/model_final_4ce675.pkl"
DATASETS:
TEST: ("coco_2017_val_100",)
TEST:
EXPECTED_RESULTS: [["bbox", "AP", 47.37, 0.02], ["segm", "AP", 40.99, 0.02]]

14
configs/quick_schedules/mask_rcnn_R_50_C4_instant_test.yaml 100644
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@ -0,0 +1,14 @@
_BASE_: "../Base-RCNN-C4.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
DATASETS:
TRAIN: ("coco_2017_val_100",)
TEST: ("coco_2017_val_100",)
SOLVER:
BASE_LR: 0.001
STEPS: (30,)
MAX_ITER: 40
IMS_PER_BATCH: 4
DATALOADER:
NUM_WORKERS: 2

22
configs/quick_schedules/mask_rcnn_R_50_C4_training_acc_test.yaml 100644
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_BASE_: "../Base-RCNN-C4.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
ROI_HEADS:
BATCH_SIZE_PER_IMAGE: 256
MASK_ON: True
DATASETS:
TRAIN: ("coco_2017_val",)
TEST: ("coco_2017_val",)
INPUT:
MIN_SIZE_TRAIN: (600,)
MAX_SIZE_TRAIN: 1000
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1000
SOLVER:
IMS_PER_BATCH: 8 # base uses 16
WARMUP_FACTOR: 0.33333
WARMUP_ITERS: 100
STEPS: (11000, 11600)
MAX_ITER: 12000
TEST:
EXPECTED_RESULTS: [["bbox", "AP", 41.88, 0.7], ["segm", "AP", 33.79, 0.5]]

7
configs/quick_schedules/mask_rcnn_R_50_DC5_inference_acc_test.yaml 100644
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_BASE_: "../COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml"
MODEL:
WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x/137849551/model_final_84107b.pkl"
DATASETS:
TEST: ("coco_2017_val_100",)
TEST:
EXPECTED_RESULTS: [["bbox", "AP", 47.44, 0.02], ["segm", "AP", 42.94, 0.02]]

10
configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml 100644
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_BASE_: "../COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"
MODEL:
WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl"
DATASETS:
TEST: ("coco_2017_val_100",)
TEST:
EXPECTED_RESULTS: [["bbox", "AP", 47.34, 0.02], ["segm", "AP", 42.67, 0.02], ["bbox_TTA", "AP", 49.11, 0.02], ["segm_TTA", "AP", 45.04, 0.02]]
AUG:
ENABLED: True
MIN_SIZES: (700, 800) # to save some time

14
configs/quick_schedules/mask_rcnn_R_50_FPN_instant_test.yaml 100644
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
DATASETS:
TRAIN: ("coco_2017_val_100",)
TEST: ("coco_2017_val_100",)
SOLVER:
BASE_LR: 0.005
STEPS: (30,)
MAX_ITER: 40
IMS_PER_BATCH: 4
DATALOADER:
NUM_WORKERS: 2

6
configs/quick_schedules/mask_rcnn_R_50_FPN_pred_boxes_training_acc_test.yaml 100644
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@ -0,0 +1,6 @@
_BASE_: "./mask_rcnn_R_50_FPN_training_acc_test.yaml"
MODEL:
ROI_BOX_HEAD:
TRAIN_ON_PRED_BOXES: True
TEST:
EXPECTED_RESULTS: [["bbox", "AP", 42.6, 1.0], ["segm", "AP", 35.8, 0.8]]

21
configs/quick_schedules/mask_rcnn_R_50_FPN_training_acc_test.yaml 100644
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
ROI_HEADS:
BATCH_SIZE_PER_IMAGE: 256
MASK_ON: True
DATASETS:
TRAIN: ("coco_2017_val",)
TEST: ("coco_2017_val",)
INPUT:
MIN_SIZE_TRAIN: (600,)
MAX_SIZE_TRAIN: 1000
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1000
SOLVER:
WARMUP_FACTOR: 0.3333333
WARMUP_ITERS: 100
STEPS: (5500, 5800)
MAX_ITER: 6000
TEST:
EXPECTED_RESULTS: [["bbox", "AP", 42.5, 1.0], ["segm", "AP", 35.8, 0.8]]

7
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_BASE_: "../COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml"
MODEL:
WEIGHTS: "detectron2://COCO-PanopticSegmentation/panoptic_fpn_R_50_3x/139514569/model_final_c10459.pkl"
DATASETS:
TEST: ("coco_2017_val_100_panoptic_separated",)
TEST:
EXPECTED_RESULTS: [["bbox", "AP", 46.47, 0.02], ["segm", "AP", 43.39, 0.02], ["sem_seg", "mIoU", 42.55, 0.02], ["panoptic_seg", "PQ", 38.99, 0.02]]

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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
META_ARCHITECTURE: "PanopticFPN"
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
RESNETS:
DEPTH: 50
SEM_SEG_HEAD:
LOSS_WEIGHT: 0.5
DATASETS:
TRAIN: ("coco_2017_val_100_panoptic_separated",)
TEST: ("coco_2017_val_100_panoptic_separated",)
SOLVER:
BASE_LR: 0.005
STEPS: (30,)
MAX_ITER: 40
IMS_PER_BATCH: 4
DATALOADER:
NUM_WORKERS: 1

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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
META_ARCHITECTURE: "PanopticFPN"
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
RESNETS:
DEPTH: 50
SEM_SEG_HEAD:
LOSS_WEIGHT: 0.5
DATASETS:
TRAIN: ("coco_2017_val_panoptic_separated",)
TEST: ("coco_2017_val_panoptic_separated",)
SOLVER:
BASE_LR: 0.01
WARMUP_FACTOR: 0.001
WARMUP_ITERS: 500
STEPS: (5500,)
MAX_ITER: 7000
TEST:
EXPECTED_RESULTS: [["bbox", "AP", 46.70, 1.1], ["segm", "AP", 39.0, 0.7], ["sem_seg", "mIoU", 64.73, 1.3], ["panoptic_seg", "PQ", 48.13, 0.8]]

7
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_BASE_: "../COCO-Detection/retinanet_R_50_FPN_3x.yaml"
MODEL:
WEIGHTS: "detectron2://COCO-Detection/retinanet_R_50_FPN_3x/190397829/model_final_5bd44e.pkl"
DATASETS:
TEST: ("coco_2017_val_100",)
TEST:
EXPECTED_RESULTS: [["bbox", "AP", 44.45, 0.02]]

13
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_BASE_: "../COCO-Detection/retinanet_R_50_FPN_1x.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
DATASETS:
TRAIN: ("coco_2017_val_100",)
TEST: ("coco_2017_val_100",)
SOLVER:
BASE_LR: 0.005
STEPS: (30,)
MAX_ITER: 40
IMS_PER_BATCH: 4
DATALOADER:
NUM_WORKERS: 2

7
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_BASE_: "../COCO-Detection/rpn_R_50_FPN_1x.yaml"
MODEL:
WEIGHTS: "detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/model_final_02ce48.pkl"
DATASETS:
TEST: ("coco_2017_val_100",)
TEST:
EXPECTED_RESULTS: [["box_proposals", "AR@1000", 58.16, 0.02]]

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_BASE_: "../COCO-Detection/rpn_R_50_FPN_1x.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
DATASETS:
TRAIN: ("coco_2017_val_100",)
TEST: ("coco_2017_val_100",)
SOLVER:
STEPS: (30,)
MAX_ITER: 40
BASE_LR: 0.005
IMS_PER_BATCH: 4
DATALOADER:
NUM_WORKERS: 2

10
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
META_ARCHITECTURE: "SemanticSegmentor"
WEIGHTS: "detectron2://semantic_R_50_FPN_1x/111802073/model_final_c18079783c55a94968edc28b7101c5f0.pkl"
RESNETS:
DEPTH: 50
DATASETS:
TEST: ("coco_2017_val_100_panoptic_stuffonly",)
TEST:
EXPECTED_RESULTS: [["sem_seg", "mIoU", 39.53, 0.02], ["sem_seg", "mACC", 51.50, 0.02]]

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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
META_ARCHITECTURE: "SemanticSegmentor"
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
RESNETS:
DEPTH: 50
DATASETS:
TRAIN: ("coco_2017_val_100_panoptic_stuffonly",)
TEST: ("coco_2017_val_100_panoptic_stuffonly",)
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
SOLVER:
BASE_LR: 0.005
STEPS: (30,)
MAX_ITER: 40
IMS_PER_BATCH: 4
DATALOADER:
NUM_WORKERS: 2

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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
META_ARCHITECTURE: "SemanticSegmentor"
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
RESNETS:
DEPTH: 50
DATASETS:
TRAIN: ("coco_2017_val_panoptic_stuffonly",)
TEST: ("coco_2017_val_panoptic_stuffonly",)
SOLVER:
BASE_LR: 0.01
WARMUP_FACTOR: 0.001
WARMUP_ITERS: 300
STEPS: (5500,)
MAX_ITER: 7000
TEST:
EXPECTED_RESULTS: [["sem_seg", "mIoU", 76.51, 1.0], ["sem_seg", "mACC", 83.25, 1.0]]
INPUT:
# no scale augmentation
MIN_SIZE_TRAIN: (800, )

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# Use Builtin Datasets
A dataset can be used by accessing [DatasetCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.DatasetCatalog)
for its data, or [MetadataCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.MetadataCatalog) for its metadata (class names, etc).
This document explains how to setup the builtin datasets so they can be used by the above APIs.
[Use Custom Datasets](https://detectron2.readthedocs.io/tutorials/datasets.html) gives a deeper dive on how to use `DatasetCatalog` and `MetadataCatalog`,
and how to add new datasets to them.
Detectron2 has builtin support for a few datasets.
The datasets are assumed to exist in a directory specified by the environment variable
`DETECTRON2_DATASETS`.
Under this directory, detectron2 will look for datasets in the structure described below, if needed.
```
$DETECTRON2_DATASETS/
coco/
lvis/
cityscapes/
VOC20{07,12}/
```
You can set the location for builtin datasets by `export DETECTRON2_DATASETS=/path/to/datasets`.
If left unset, the default is `./datasets` relative to your current working directory.
The [model zoo](https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md)
contains configs and models that use these builtin datasets.
## Expected dataset structure for [COCO instance/keypoint detection](https://cocodataset.org/#download):
```
coco/
annotations/
instances_{train,val}2017.json
person_keypoints_{train,val}2017.json
{train,val}2017/
# image files that are mentioned in the corresponding json
```
You can use the 2014 version of the dataset as well.
Some of the builtin tests (`dev/run_*_tests.sh`) uses a tiny version of the COCO dataset,
which you can download with `./prepare_for_tests.sh`.
## Expected dataset structure for PanopticFPN:
Extract panoptic annotations from [COCO website](https://cocodataset.org/#download)
into the following structure:
```
coco/
annotations/
panoptic_{train,val}2017.json
panoptic_{train,val}2017/ # png annotations
panoptic_stuff_{train,val}2017/ # generated by the script mentioned below
```
Install panopticapi by:
```
pip install git+https://github.com/cocodataset/panopticapi.git
```
Then, run `python prepare_panoptic_fpn.py`, to extract semantic annotations from panoptic annotations.
## Expected dataset structure for [LVIS instance segmentation](https://www.lvisdataset.org/dataset):
```
coco/
{train,val,test}2017/
lvis/
lvis_v0.5_{train,val}.json
lvis_v0.5_image_info_test.json
lvis_v1_{train,val}.json
lvis_v1_image_info_test{,_challenge}.json
```
Install lvis-api by:
```
pip install git+https://github.com/lvis-dataset/lvis-api.git
```
To evaluate models trained on the COCO dataset using LVIS annotations,
run `python prepare_cocofied_lvis.py` to prepare "cocofied" LVIS annotations.
## Expected dataset structure for [cityscapes](https://www.cityscapes-dataset.com/downloads/):
```
cityscapes/
gtFine/
train/
aachen/
color.png, instanceIds.png, labelIds.png, polygons.json,
labelTrainIds.png
...
val/
test/
# below are generated Cityscapes panoptic annotation
cityscapes_panoptic_train.json
cityscapes_panoptic_train/
cityscapes_panoptic_val.json
cityscapes_panoptic_val/
cityscapes_panoptic_test.json
cityscapes_panoptic_test/
leftImg8bit/
train/
val/
test/
```
Install cityscapes scripts by:
```
pip install git+https://github.com/mcordts/cityscapesScripts.git
```
Note: to create labelTrainIds.png, first prepare the above structure, then run cityscapesescript with:
```
CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createTrainIdLabelImgs.py
```
These files are not needed for instance segmentation.
Note: to generate Cityscapes panoptic dataset, run cityscapesescript with:
```
CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createPanopticImgs.py
```
These files are not needed for semantic and instance segmentation.
## Expected dataset structure for [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/index.html):
```
VOC20{07,12}/
Annotations/
ImageSets/
Main/
trainval.txt
test.txt
# train.txt or val.txt, if you use these splits
JPEGImages/
```
## Expected dataset structure for [ADE20k Scene Parsing](http://sceneparsing.csail.mit.edu/):
```
ADEChallengeData2016/
annotations/
annotations_detectron2/
images/
objectInfo150.txt
```
The directory `annotations_detectron2` is generated by running `python prepare_ade20k_sem_seg.py`.

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