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detectron2/GETTING_STARTED.md

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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.
+
+
+### 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
+```
+
+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/main/projects)
+for more ways to build your project on detectron2.

detectron2/INSTALL.md

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+## Installation
+
+### Requirements
+- Linux or macOS with Python ≥ 3.7
+- PyTorch ≥ 1.8 and [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation.
+  Install them together at [pytorch.org](https://pytorch.org) to make sure of this
+- OpenCV is optional but needed by demo and visualization
+
+
+### Build Detectron2 from Source
+
+gcc & g++ ≥ 5.4 are required. [ninja](https://ninja-build.org/) is optional but 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
+
+# On macOS, you may need to prepend the above commands with a few environment variables:
+CC=clang CXX=clang++ ARCHFLAGS="-arch x86_64" 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 [v0.6 (Oct 2021)](https://github.com/facebookresearch/detectron2/releases):
+
+<table class="docutils"><tbody><th width="80"> CUDA </th><th valign="bottom" align="left" width="100">torch 1.10</th><th valign="bottom" align="left" width="100">torch 1.9</th><th valign="bottom" align="left" width="100">torch 1.8</th> <tr><td align="left">11.3</td><td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
+  https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html
+</code></pre> </details> </td> <td align="left"> </td> <td align="left"> </td> </tr> <tr><td align="left">11.1</td><td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
+  https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.10/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/cu111/torch1.9/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/cu111/torch1.8/index.html
+</code></pre> </details> </td> </tr> <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/torch1.10/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/torch1.9/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/torch1.8/index.html
+</code></pre> </details> </td> </tr> <tr><td align="left">10.1</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/cu101/torch1.8/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/torch1.10/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/torch1.9/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/torch1.8/index.html
+</code></pre> </details> </td> </tr></tbody></table>
+
+Note that:
+1. The pre-built packages have to be used with corresponding version of CUDA and the official package of PyTorch.
+   Otherwise, please build detectron2 from source.
+2. New packages are released every few months. Therefore, packages may not contain latest features in the main
+   branch and may not be compatible with the main 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 looks like "TH..","at::Tensor...","torch..."
+</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),
+uninstall and reinstall the correct pre-built detectron2 that matches pytorch version.
+
+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 the above instructions do not resolve this problem, please provide an environment (e.g. a dockerfile) that can reproduce the issue.
+</details>
+
+<details>
+<summary>
+Missing torch dynamic libraries, OR segmentation fault immediately when using detectron2.
+</summary>
+This usually happens when detectron2 or torchvision is not
+compiled with the version of PyTorch you're running. See the previous common issue for the solution.
+</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.
+It may help to run `conda update libgcc` to upgrade its runtime.
+
+The fundamental solution is to avoid the mismatch, either by compiling using older version of C++
+compiler, or run the code with proper C++ runtime.
+To run the code with a specific C++ runtime, you can use environment variable `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 SM 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 / NVRTC, or "Unsupported gpu architecture"
+</summary>
+<br/>
+A few possibilities:
+
+1. Local CUDA/NVCC version has to match the CUDA version of your PyTorch. Both can be found in `python collect_env.py`
+   (download from [here](./detectron2/utils/collect_env.py)).
+   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.
+
+2. Local CUDA/NVCC version shall support the SM architecture (a.k.a. compute capability) of your GPU.
+   The capability of your GPU can be found at [developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus).
+   The capability supported by NVCC is listed at [here](https://gist.github.com/ax3l/9489132).
+   If your NVCC version is too old, this can be workaround by setting environment variable
+   `TORCH_CUDA_ARCH_LIST` to a lower, supported capability.
+
+3. 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.
+   Notably, CUDA<=10.1.105 doesn't support GCC>7.3.
+
+   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=main).
+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>
+
+
+<details>
+<summary>
+"library not found for -lstdc++" on older version of MacOS
+</summary>
+<br/>
+
+See [this stackoverflow answer](https://stackoverflow.com/questions/56083725/macos-build-issues-lstdc-not-found-while-building-python-package).
+
+</details>
+
+
+### Installation inside specific environments:
+
+* __Colab__: see our [Colab Tutorial](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
+  which has step-by-step instructions.
+
+* __Docker__: The official [Dockerfile](docker) installs detectron2 with a few simple commands.

detectron2/LICENSE

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+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.

detectron2/MODEL_ZOO.md

@@ -0,0 +1,1052 @@
+# 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 speed numbers are periodically updated with latest PyTorch/CUDA/cuDNN versions.
+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. Models can be reproduced using `tools/train_net.py` with the corresponding yaml config file,
+  or `tools/lazyconfig_train_net.py` for python config files.
+* 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.
+
+These models require slightly different settings regarding normalization and architecture. See the model zoo configs for reference.
+
+#### 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>
+
+
+
+#### New baselines using Large-Scale Jitter and Longer Training Schedule
+
+The following baselines of COCO Instance Segmentation with Mask R-CNN are generated
+using a longer training schedule and large-scale jitter as described in Google's
+[Simple Copy-Paste Data Augmentation](https://arxiv.org/pdf/2012.07177.pdf) paper. These
+models are trained from scratch using random initialization. These baselines exceed the
+previous Mask R-CNN baselines.
+
+In the following table, one epoch consists of training on 118000 COCO images.
+
+<table><tbody>
+<!-- START TABLE -->
+<!-- TABLE HEADER -->
+<th valign="bottom">Name</th>
+<th valign="bottom">epochs</th>
+<th valign="bottom">train<br/>time<br/>(s/im)</th>
+<th valign="bottom">inference<br/>time<br/>(s/im)</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_100ep_LSJ -->
+ <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ.py">R50-FPN</a></td>
+<td align="center">100</td>
+<td align="center">0.376</td>
+<td align="center">0.069</td>
+<td align="center">44.6</td>
+<td align="center">40.3</td>
+<td align="center">42047764</td>
+<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ/42047764/model_final_bb69de.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ/42047764/metrics.json">metrics</a></td>
+</tr>
+<!-- ROW: mask_rcnn_R_50_FPN_200ep_LSJ -->
+ <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ.py">R50-FPN</a></td>
+<td align="center">200</td>
+<td align="center">0.376</td>
+<td align="center">0.069</td>
+<td align="center">46.3</td>
+<td align="center">41.7</td>
+<td align="center">42047638</td>
+<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ/42047638/model_final_89a8d3.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ/42047638/metrics.json">metrics</a></td>
+</tr>
+<!-- ROW: mask_rcnn_R_50_FPN_400ep_LSJ -->
+ <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_R_50_FPN_400ep_LSJ.py">R50-FPN</a></td>
+<td align="center">400</td>
+<td align="center">0.376</td>
+<td align="center">0.069</td>
+<td align="center">47.4</td>
+<td align="center">42.5</td>
+<td align="center">42019571</td>
+<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_50_FPN_400ep_LSJ/42019571/model_final_14d201.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_50_FPN_400ep_LSJ/42019571/metrics.json">metrics</a></td>
+</tr>
+<!-- ROW: mask_rcnn_R_101_FPN_100ep_LSJ -->
+ <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_R_101_FPN_100ep_LSJ.py">R101-FPN</a></td>
+<td align="center">100</td>
+<td align="center">0.518</td>
+<td align="center">0.073</td>
+<td align="center">46.4</td>
+<td align="center">41.6</td>
+<td align="center">42025812</td>
+<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_101_FPN_100ep_LSJ/42025812/model_final_4f7b58.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_101_FPN_100ep_LSJ/42025812/metrics.json">metrics</a></td>
+</tr>
+<!-- ROW: mask_rcnn_R_101_FPN_200ep_LSJ -->
+ <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ.py">R101-FPN</a></td>
+<td align="center">200</td>
+<td align="center">0.518</td>
+<td align="center">0.073</td>
+<td align="center">48.0</td>
+<td align="center">43.1</td>
+<td align="center">42131867</td>
+<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ/42131867/model_final_0bb7ae.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ/42131867/metrics.json">metrics</a></td>
+</tr>
+<!-- ROW: mask_rcnn_R_101_FPN_400ep_LSJ -->
+ <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ.py">R101-FPN</a></td>
+<td align="center">400</td>
+<td align="center">0.518</td>
+<td align="center">0.073</td>
+<td align="center">48.9</td>
+<td align="center">43.7</td>
+<td align="center">42073830</td>
+<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ/42073830/model_final_f96b26.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ/42073830/metrics.json">metrics</a></td>
+</tr>
+<!-- ROW: mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ -->
+ <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ.py">regnetx_4gf_dds_FPN</a></td>
+<td align="center">100</td>
+<td align="center">0.474</td>
+<td align="center">0.071</td>
+<td align="center">46.0</td>
+<td align="center">41.3</td>
+<td align="center">42047771</td>
+<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ/42047771/model_final_b7fbab.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ/42047771/metrics.json">metrics</a></td>
+</tr>
+<!-- ROW: mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ -->
+ <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ.py">regnetx_4gf_dds_FPN</a></td>
+<td align="center">200</td>
+<td align="center">0.474</td>
+<td align="center">0.071</td>
+<td align="center">48.1</td>
+<td align="center">43.1</td>
+<td align="center">42132721</td>
+<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ/42132721/model_final_5d87c1.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ/42132721/metrics.json">metrics</a></td>
+</tr>
+<!-- ROW: mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ -->
+ <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ.py">regnetx_4gf_dds_FPN</a></td>
+<td align="center">400</td>
+<td align="center">0.474</td>
+<td align="center">0.071</td>
+<td align="center">48.6</td>
+<td align="center">43.5</td>
+<td align="center">42025447</td>
+<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ/42025447/model_final_f1362d.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ/42025447/metrics.json">metrics</a></td>
+</tr>
+<!-- ROW: mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ -->
+ <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ.py">regnety_4gf_dds_FPN</a></td>
+<td align="center">100</td>
+<td align="center">0.487</td>
+<td align="center">0.073</td>
+<td align="center">46.1</td>
+<td align="center">41.6</td>
+<td align="center">42047784</td>
+<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ/42047784/model_final_6ba57e.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ/42047784/metrics.json">metrics</a></td>
+</tr>
+<!-- ROW: mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ -->
+ <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ.py">regnety_4gf_dds_FPN</a></td>
+<td align="center">200</td>
+<td align="center">0.487</td>
+<td align="center">0.072</td>
+<td align="center">47.8</td>
+<td align="center">43.0</td>
+<td align="center">42047642</td>
+<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ/42047642/model_final_27b9c1.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ/42047642/metrics.json">metrics</a></td>
+</tr>
+<!-- ROW: mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ -->
+ <tr><td align="left"><a href="configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ.py">regnety_4gf_dds_FPN</a></td>
+<td align="center">400</td>
+<td align="center">0.487</td>
+<td align="center">0.072</td>
+<td align="center">48.2</td>
+<td align="center">43.3</td>
+<td align="center">42045954</td>
+<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ/42045954/model_final_ef3a80.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ/42045954/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>

detectron2/README.md

@@ -0,0 +1,68 @@
+<img src=".github/Detectron2-Logo-Horz.svg" width="300" >
+
+<a href="https://opensource.facebook.com/support-ukraine">
+  <img src="https://img.shields.io/badge/Support-Ukraine-FFD500?style=flat&labelColor=005BBB" alt="Support Ukraine - Help Provide Humanitarian Aid to Ukraine." />
+</a>
+
+Detectron2 is Facebook AI Research's next generation library
+that provides state-of-the-art detection and segmentation algorithms.
+It is the successor of
+[Detectron](https://github.com/facebookresearch/Detectron/)
+and [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/).
+It supports a number of computer vision research projects and production applications in Facebook.
+
+<div align="center">
+  <img src="https://user-images.githubusercontent.com/1381301/66535560-d3422200-eace-11e9-9123-5535d469db19.png"/>
+</div>
+<br>
+
+## Learn More about Detectron2
+
+Explain Like I’m 5: Detectron2            |  Using Machine Learning with Detectron2
+:-------------------------:|:-------------------------:
+[![Explain Like I’m 5: Detectron2](https://img.youtube.com/vi/1oq1Ye7dFqc/0.jpg)](https://www.youtube.com/watch?v=1oq1Ye7dFqc)  |  [![Using Machine Learning with Detectron2](https://img.youtube.com/vi/eUSgtfK4ivk/0.jpg)](https://www.youtube.com/watch?v=eUSgtfK4ivk)
+
+## What's New
+* Includes new capabilities such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend,
+  DeepLab, ViTDet, MViTv2 etc.
+* Used as a library to support building [research projects](projects/) on top of it.
+* Models can be exported to TorchScript format or Caffe2 format for deployment.
+* 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 [installation instructions](https://detectron2.readthedocs.io/tutorials/install.html).
+
+## Getting Started
+
+See [Getting Started with Detectron2](https://detectron2.readthedocs.io/tutorials/getting_started.html),
+and the [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
+to learn about basic usage.
+
+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}
+}
+```

detectron2/configs/Base-RCNN-C4.yaml

@@ -0,0 +1,18 @@
+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

detectron2/configs/Base-RCNN-DilatedC5.yaml

@@ -0,0 +1,31 @@
+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

detectron2/configs/Base-RCNN-FPN.yaml

@@ -0,0 +1,42 @@
+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

detectron2/configs/Base-RetinaNet.yaml

@@ -0,0 +1,25 @@
+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

detectron2/configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml

@@ -0,0 +1,17 @@
+_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

detectron2/configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml

@@ -0,0 +1,9 @@
+_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

detectron2/configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml

@@ -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

detectron2/configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml

@@ -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

detectron2/configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml

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

detectron2/configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml

@@ -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

detectron2/configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml

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

detectron2/configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml

@@ -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

detectron2/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml

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

detectron2/configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml

@@ -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

detectron2/configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml

@@ -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

detectron2/configs/COCO-Detection/fcos_R_50_FPN_1x.py

@@ -0,0 +1,11 @@
+from ..common.optim import SGD as optimizer
+from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
+from ..common.data.coco import dataloader
+from ..common.models.fcos import model
+from ..common.train import train
+
+dataloader.train.mapper.use_instance_mask = False
+optimizer.lr = 0.01
+
+model.backbone.bottom_up.freeze_at = 2
+train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl"

detectron2/configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml

@@ -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

detectron2/configs/COCO-Detection/retinanet_R_50_FPN_1x.py

@@ -0,0 +1,11 @@
+from ..common.optim import SGD as optimizer
+from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
+from ..common.data.coco import dataloader
+from ..common.models.retinanet import model
+from ..common.train import train
+
+dataloader.train.mapper.use_instance_mask = False
+model.backbone.bottom_up.freeze_at = 2
+optimizer.lr = 0.01
+
+train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl"

detectron2/configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml

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

detectron2/configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml

@@ -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

detectron2/configs/COCO-Detection/rpn_R_50_C4_1x.yaml

@@ -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

detectron2/configs/COCO-Detection/rpn_R_50_FPN_1x.yaml

@@ -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

detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml

@@ -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

detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x.yaml

@@ -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

detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml

@@ -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

detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.py

@@ -0,0 +1,8 @@
+from ..common.train import train
+from ..common.optim import SGD as optimizer
+from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
+from ..common.data.coco import dataloader
+from ..common.models.mask_rcnn_c4 import model
+
+model.backbone.freeze_at = 2
+train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl"

detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml

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

detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml

@@ -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

detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x.yaml

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

detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml

@@ -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

detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.py

@@ -0,0 +1,8 @@
+from ..common.optim import SGD as optimizer
+from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
+from ..common.data.coco import dataloader
+from ..common.models.mask_rcnn_fpn import model
+from ..common.train import train
+
+model.backbone.bottom_up.freeze_at = 2
+train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl"

detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml

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

detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x_giou.yaml

@@ -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

detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml

@@ -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

detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml

@@ -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

detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_regnetx_4gf_dds_fpn_1x.py

@@ -0,0 +1,34 @@
+from ..common.optim import SGD as optimizer
+from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
+from ..common.data.coco import dataloader
+from ..common.models.mask_rcnn_fpn import model
+from ..common.train import train
+
+from detectron2.config import LazyCall as L
+from detectron2.modeling.backbone import RegNet
+from detectron2.modeling.backbone.regnet import SimpleStem, ResBottleneckBlock
+
+
+# Replace default ResNet with RegNetX-4GF from the DDS paper. Config source:
+# https://github.com/facebookresearch/pycls/blob/2c152a6e5d913e898cca4f0a758f41e6b976714d/configs/dds_baselines/regnetx/RegNetX-4.0GF_dds_8gpu.yaml#L4-L9  # noqa
+model.backbone.bottom_up = L(RegNet)(
+    stem_class=SimpleStem,
+    stem_width=32,
+    block_class=ResBottleneckBlock,
+    depth=23,
+    w_a=38.65,
+    w_0=96,
+    w_m=2.43,
+    group_width=40,
+    freeze_at=2,
+    norm="FrozenBN",
+    out_features=["s1", "s2", "s3", "s4"],
+)
+model.pixel_std = [57.375, 57.120, 58.395]
+
+optimizer.weight_decay = 5e-5
+train.init_checkpoint = (
+    "https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906383/RegNetX-4.0GF_dds_8gpu.pyth"
+)
+# RegNets benefit from enabling cudnn benchmark mode
+train.cudnn_benchmark = True

detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_regnety_4gf_dds_fpn_1x.py

@@ -0,0 +1,35 @@
+from ..common.optim import SGD as optimizer
+from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
+from ..common.data.coco import dataloader
+from ..common.models.mask_rcnn_fpn import model
+from ..common.train import train
+
+from detectron2.config import LazyCall as L
+from detectron2.modeling.backbone import RegNet
+from detectron2.modeling.backbone.regnet import SimpleStem, ResBottleneckBlock
+
+
+# Replace default ResNet with RegNetY-4GF from the DDS paper. Config source:
+# https://github.com/facebookresearch/pycls/blob/2c152a6e5d913e898cca4f0a758f41e6b976714d/configs/dds_baselines/regnety/RegNetY-4.0GF_dds_8gpu.yaml#L4-L10  # noqa
+model.backbone.bottom_up = L(RegNet)(
+    stem_class=SimpleStem,
+    stem_width=32,
+    block_class=ResBottleneckBlock,
+    depth=22,
+    w_a=31.41,
+    w_0=96,
+    w_m=2.24,
+    group_width=64,
+    se_ratio=0.25,
+    freeze_at=2,
+    norm="FrozenBN",
+    out_features=["s1", "s2", "s3", "s4"],
+)
+model.pixel_std = [57.375, 57.120, 58.395]
+
+optimizer.weight_decay = 5e-5
+train.init_checkpoint = (
+    "https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906838/RegNetY-4.0GF_dds_8gpu.pyth"
+)
+# RegNets benefit from enabling cudnn benchmark mode
+train.cudnn_benchmark = True

detectron2/configs/COCO-Keypoints/Base-Keypoint-RCNN-FPN.yaml

@@ -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",)

detectron2/configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x.yaml

@@ -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

detectron2/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.py

@@ -0,0 +1,8 @@
+from ..common.optim import SGD as optimizer
+from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
+from ..common.data.coco_keypoint import dataloader
+from ..common.models.keypoint_rcnn_fpn import model
+from ..common.train import train
+
+model.backbone.bottom_up.freeze_at = 2
+train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl"

detectron2/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.yaml

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

detectron2/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml

@@ -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

detectron2/configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml

@@ -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

detectron2/configs/COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml

@@ -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

detectron2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml

@@ -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