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## Getting Started with Detectron2
This document provides a brief intro of the usage of builtin command-line tools in detectron2.
For a tutorial that involves actual coding with the API,
see our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
which covers how to run inference with an
existing model, and how to train a builtin model on a custom dataset.
For more advanced tutorials, refer to our [documentation](https://detectron2.readthedocs.io/tutorials/extend.html).
### Inference Demo with Pre-trained Models
1. Pick a model and its config file from
[model zoo](MODEL_ZOO.md),
for example, `mask_rcnn_R_50_FPN_3x.yaml`.
2. We provide `demo.py` that is able to run builtin standard models. 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 a script in "tools/{,plain_}train_net.py", that is made to train
all the configs provided in detectron2.
You may want to use it as a reference to write your own training script.
To train a model with "train_net.py", first
setup the corresponding datasets following
[datasets/README.md](./datasets/README.md),
then run:
```
cd tools/
./train_net.py --num-gpus 8 \
--config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml
```
The configs are made for 8-GPU training.
To train on 1 GPU, you may need to [change some parameters](https://arxiv.org/abs/1706.02677), e.g.:
```
./train_net.py \
--config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \
--num-gpus 1 SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025
```
For most models, CPU training is not supported.
To evaluate a model's performance, use
```
./train_net.py \
--config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \
--eval-only MODEL.WEIGHTS /path/to/checkpoint_file
```
For more options, see `./train_net.py -h`.
### Use Detectron2 APIs in Your Code
See our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
to learn how to use detectron2 APIs to:
1. run inference with an existing model
2. train a builtin model on a custom dataset
See [detectron2/projects](https://github.com/facebookresearch/detectron2/tree/master/projects)
for more ways to build your project on detectron2.
INSTALL.md 0 → 100644
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## Installation
Our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
has step-by-step instructions that install detectron2.
The [Dockerfile](docker)
also installs detectron2 with a few simple commands.
### Requirements
- Linux or macOS with Python ≥ 3.6
- PyTorch ≥ 1.4
- [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation.
You can install them together at [pytorch.org](https://pytorch.org) to make sure of this.
- OpenCV, optional, needed by demo and visualization
- pycocotools: `pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'`
### Build Detectron2 from Source
gcc & g++ ≥ 5 are required. [ninja](https://ninja-build.org/) is recommended for faster build.
After having them, run:
```
python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
# (add --user if you don't have permission)
# Or, to install it from a local clone:
git clone https://github.com/facebookresearch/detectron2.git
python -m pip install -e detectron2
# Or if you are on macOS
# CC=clang CXX=clang++ python -m pip install -e .
```
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)
```
# for CUDA 10.1:
python -m pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/index.html
```
You can replace cu101 with "cu{100,92}" or "cpu".
Note that:
1. Such installation has to be used with certain version of official PyTorch release.
See [releases](https://github.com/facebookresearch/detectron2/releases) for requirements.
It will not work with a different version of PyTorch or a non-official build of PyTorch.
2. Such installation is out-of-date w.r.t. master branch of detectron2. It may not be
compatible with the master branch of a research project that uses detectron2 (e.g. those in
[projects](projects) or [meshrcnn](https://github.com/facebookresearch/meshrcnn/)).
### Common Installation Issues
If you met issues using the pre-built detectron2, please uninstall it and try building it from source.
Click each issue for its solutions:
<details>
<summary>
Undefined torch/aten/caffe2 symbols, or segmentation fault immediately when running the library.
</summary>
<br/>
This usually happens when detectron2 or torchvision is not
compiled with the version of PyTorch you're running.
Pre-built torchvision or detectron2 has to work with the corresponding official release of pytorch.
If the error comes from a pre-built torchvision, uninstall torchvision and pytorch and reinstall them
following [pytorch.org](http://pytorch.org). So the versions will match.
If the error comes from a pre-built detectron2, check [release notes](https://github.com/facebookresearch/detectron2/releases)
to see the corresponding pytorch version required for each pre-built detectron2.
If the error comes from detectron2 or torchvision that you built manually from source,
remove files you built (`build/`, `**/*.so`) and rebuild it so it can pick up the version of pytorch currently in your environment.
If you cannot resolve this problem, please include the output of `gdb -ex "r" -ex "bt" -ex "quit" --args python -m detectron2.utils.collect_env`
in your issue.
</details>
<details>
<summary>
Undefined C++ symbols (e.g. `GLIBCXX`) or C++ symbols not found.
</summary>
<br/>
Usually it's because the library is compiled with a newer C++ compiler but run with an old C++ runtime.
This often happens with old anaconda.
Try `conda update libgcc`. Then rebuild detectron2.
The fundamental solution is to run the code with proper C++ runtime.
One way is to use `LD_PRELOAD=/path/to/libstdc++.so`.
</details>
<details>
<summary>
"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 valid outputs 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.
* Detectron2 or PyTorch/torchvision is not built for the correct GPU architecture (compute compatibility).
The GPU architecture for PyTorch/detectron2/torchvision is available in the "architecture flags" in
`python -m detectron2.utils.collect_env`.
The GPU architecture flags of detectron2/torchvision by default matches the GPU model detected
during compilation. This means the compiled code may not work on a different GPU model.
To overwrite the GPU architecture for detectron2/torchvision, use `TORCH_CUDA_ARCH_LIST` environment variable during compilation.
For example, `export TORCH_CUDA_ARCH_LIST=6.0,7.0` makes it compile for both P100s and V100s.
Visit [developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus) to find out
the correct compute compatibility number for your device.
</details>
<details>
<summary>
Undefined CUDA symbols; cannot open libcudart.so; other nvcc failures.
</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>
"ImportError: cannot import name '_C'".
</summary>
<br/>
Please build and install detectron2 following the instructions above.
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>
ONNX conversion segfault after some "TraceWarning".
</summary>
<br/>
The ONNX package is compiled with too old compiler.
Please build and install ONNX from its source code using a compiler
whose version is closer to what's used by PyTorch (available in `torch.__config__.show()`).
</details>
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MODEL_ZOO.md 0 → 100644
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# Detectron2 Model Zoo and Baselines
## Introduction
This file documents a large collection of baselines trained
with detectron2 in Sep-Oct, 2019.
All numbers were obtained on [Big Basin](https://engineering.fb.com/data-center-engineering/introducing-big-basin-our-next-generation-ai-hardware/)
servers with 8 NVIDIA V100 GPUs & NVLink. The software in use were PyTorch 1.3, CUDA 9.2, cuDNN 7.4.2 or 7.6.3.
You can access these models from code using [detectron2.model_zoo](https://detectron2.readthedocs.io/modules/model_zoo.html) APIs.
In addition to these official baseline models, you can find more models in [projects/](projects/).
#### How to Read the Tables
* The "Name" column contains a link to the config file. Running `tools/train_net.py` with this config file
and 8 GPUs will reproduce the model.
* Training speed is averaged across the entire training.
We keep updating the speed with latest version of detectron2/pytorch/etc.,
so they might be different from the `metrics` file.
Training speed for multi-machine jobs is not provided.
* Inference speed is measured by `tools/train_net.py --eval-only`, or [inference_on_dataset()](https://detectron2.readthedocs.io/modules/evaluation.html#detectron2.evaluation.inference_on_dataset),
with batch size 1 in detectron2 directly.
Measuring it with your own code will likely 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
We provide backbone models pretrained on ImageNet-1k dataset.
These models have __different__ format from those provided in Detectron: we do not fuse BatchNorm into an affine layer.
* [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.
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.
Torchvision's ResNet models can be used after converted by [this script](tools/convert-torchvision-to-d2.py).
#### 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">0.200</td>
<td align="center">0.055</td>
<td align="center">3.9</td>
<td align="center">36.5</td>
<td align="center">137593951</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_1x/137593951/model_final_b796dc.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_1x/137593951/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">0.201</td>
<td align="center">0.055</td>
<td align="center">3.9</td>
<td align="center">37.9</td>
<td align="center">137849486</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_3x/137849486/model_final_4cafe0.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_3x/137849486/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">0.280</td>
<td align="center">0.068</td>
<td align="center">5.1</td>
<td align="center">39.9</td>
<td align="center">138363263</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_101_FPN_3x/138363263/model_final_59f53c.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_101_FPN_3x/138363263/metrics.json">metrics</a></td>
</tr>
</tbody></table>
#### RPN & Fast R-CNN:
<!--
./gen_html_table.py --config 'COCO-Detection/rpn*' 'COCO-Detection/fast_rcnn*' --name "RPN R50-C4" "RPN R50-FPN" "Fast R-CNN R50-FPN" --fields lr_sched train_speed inference_speed mem box_AP prop_AR
-->
<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom">Name</th>
<th valign="bottom">lr<br/>sched</th>
<th valign="bottom">train<br/>time<br/>(s/iter)</th>
<th valign="bottom">inference<br/>time<br/>(s/im)</th>
<th valign="bottom">train<br/>mem<br/>(GB)</th>
<th valign="bottom">box<br/>AP</th>
<th valign="bottom">prop.<br/>AR</th>
<th valign="bottom">model id</th>
<th valign="bottom">download</th>
<!-- TABLE BODY -->
<!-- ROW: rpn_R_50_C4_1x -->
<tr><td align="left"><a href="configs/COCO-Detection/rpn_R_50_C4_1x.yaml">RPN R50-C4</a></td>
<td align="center">1x</td>
<td align="center">0.130</td>
<td align="center">0.034</td>
<td align="center">1.5</td>
<td align="center"></td>
<td align="center">51.6</td>
<td align="center">137258005</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/rpn_R_50_C4_1x/137258005/model_final_450694.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/rpn_R_50_C4_1x/137258005/metrics.json">metrics</a></td>
</tr>
<!-- ROW: rpn_R_50_FPN_1x -->
<tr><td align="left"><a href="configs/COCO-Detection/rpn_R_50_FPN_1x.yaml">RPN R50-FPN</a></td>
<td align="center">1x</td>
<td align="center">0.186</td>
<td align="center">0.032</td>
<td align="center">2.7</td>
<td align="center"></td>
<td align="center">58.0</td>
<td align="center">137258492</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/rpn_R_50_FPN_1x/137258492/model_final_02ce48.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/rpn_R_50_FPN_1x/137258492/metrics.json">metrics</a></td>
</tr>
<!-- ROW: fast_rcnn_R_50_FPN_1x -->
<tr><td align="left"><a href="configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml">Fast R-CNN R50-FPN</a></td>
<td align="center">1x</td>
<td align="center">0.140</td>
<td align="center">0.029</td>
<td align="center">2.6</td>
<td align="center">37.8</td>
<td align="center"></td>
<td align="center">137635226</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/fast_rcnn_R_50_FPN_1x/137635226/model_final_e5f7ce.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/fast_rcnn_R_50_FPN_1x/137635226/metrics.json">metrics</a></td>
</tr>
</tbody></table>
### COCO Instance Segmentation Baselines with Mask R-CNN
<!--
./gen_html_table.py --config 'COCO-InstanceSegmentation/mask*50*'{1x,3x}'*' 'COCO-InstanceSegmentation/mask*101*' --name R50-C4 R50-DC5 R50-FPN R50-C4 R50-DC5 R50-FPN R101-C4 R101-DC5 R101-FPN X101-FPN --fields lr_sched train_speed inference_speed mem box_AP mask_AP
-->
<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom">Name</th>
<th valign="bottom">lr<br/>sched</th>
<th valign="bottom">train<br/>time<br/>(s/iter)</th>
<th valign="bottom">inference<br/>time<br/>(s/im)</th>
<th valign="bottom">train<br/>mem<br/>(GB)</th>
<th valign="bottom">box<br/>AP</th>
<th valign="bottom">mask<br/>AP</th>
<th valign="bottom">model id</th>
<th valign="bottom">download</th>
<!-- TABLE BODY -->
<!-- ROW: mask_rcnn_R_50_C4_1x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml">R50-C4</a></td>
<td align="center">1x</td>
<td align="center">0.584</td>
<td align="center">0.110</td>
<td align="center">5.2</td>
<td align="center">36.8</td>
<td align="center">32.2</td>
<td align="center">137259246</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x/137259246/model_final_9243eb.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x/137259246/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_50_DC5_1x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x.yaml">R50-DC5</a></td>
<td align="center">1x</td>
<td align="center">0.471</td>
<td align="center">0.076</td>
<td align="center">6.5</td>
<td align="center">38.3</td>
<td align="center">34.2</td>
<td align="center">137260150</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x/137260150/model_final_4f86c3.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x/137260150/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_50_FPN_1x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml">R50-FPN</a></td>
<td align="center">1x</td>
<td align="center">0.261</td>
<td align="center">0.043</td>
<td align="center">3.4</td>
<td align="center">38.6</td>
<td align="center">35.2</td>
<td align="center">137260431</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/137260431/model_final_a54504.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/137260431/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_50_C4_3x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml">R50-C4</a></td>
<td align="center">3x</td>
<td align="center">0.575</td>
<td align="center">0.111</td>
<td align="center">5.2</td>
<td align="center">39.8</td>
<td align="center">34.4</td>
<td align="center">137849525</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x/137849525/model_final_4ce675.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x/137849525/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_50_DC5_3x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml">R50-DC5</a></td>
<td align="center">3x</td>
<td align="center">0.470</td>
<td align="center">0.076</td>
<td align="center">6.5</td>
<td align="center">40.0</td>
<td align="center">35.9</td>
<td align="center">137849551</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x/137849551/model_final_84107b.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x/137849551/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_50_FPN_3x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml">R50-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.261</td>
<td align="center">0.043</td>
<td align="center">3.4</td>
<td align="center">41.0</td>
<td align="center">37.2</td>
<td align="center">137849600</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_101_C4_3x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml">R101-C4</a></td>
<td align="center">3x</td>
<td align="center">0.652</td>
<td align="center">0.145</td>
<td align="center">6.3</td>
<td align="center">42.6</td>
<td align="center">36.7</td>
<td align="center">138363239</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x/138363239/model_final_a2914c.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x/138363239/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_101_DC5_3x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x.yaml">R101-DC5</a></td>
<td align="center">3x</td>
<td align="center">0.545</td>
<td align="center">0.092</td>
<td align="center">7.6</td>
<td align="center">41.9</td>
<td align="center">37.3</td>
<td align="center">138363294</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x/138363294/model_final_0464b7.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x/138363294/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_R_101_FPN_3x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml">R101-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.340</td>
<td align="center">0.056</td>
<td align="center">4.6</td>
<td align="center">42.9</td>
<td align="center">38.6</td>
<td align="center">138205316</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x/138205316/model_final_a3ec72.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x/138205316/metrics.json">metrics</a></td>
</tr>
<!-- ROW: mask_rcnn_X_101_32x8d_FPN_3x -->
<tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml">X101-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.690</td>
<td align="center">0.103</td>
<td align="center">7.2</td>
<td align="center">44.3</td>
<td align="center">39.5</td>
<td align="center">139653917</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x/139653917/model_final_2d9806.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x/139653917/metrics.json">metrics</a></td>
</tr>
</tbody></table>
### COCO Person Keypoint Detection Baselines with Keypoint R-CNN
<!--
./gen_html_table.py --config 'COCO-Keypoints/*50*' 'COCO-Keypoints/*101*' --name R50-FPN R50-FPN R101-FPN X101-FPN --fields lr_sched train_speed inference_speed mem box_AP keypoint_AP
-->
<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom">Name</th>
<th valign="bottom">lr<br/>sched</th>
<th valign="bottom">train<br/>time<br/>(s/iter)</th>
<th valign="bottom">inference<br/>time<br/>(s/im)</th>
<th valign="bottom">train<br/>mem<br/>(GB)</th>
<th valign="bottom">box<br/>AP</th>
<th valign="bottom">kp.<br/>AP</th>
<th valign="bottom">model id</th>
<th valign="bottom">download</th>
<!-- TABLE BODY -->
<!-- ROW: keypoint_rcnn_R_50_FPN_1x -->
<tr><td align="left"><a href="configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.yaml">R50-FPN</a></td>
<td align="center">1x</td>
<td align="center">0.315</td>
<td align="center">0.072</td>
<td align="center">5.0</td>
<td align="center">53.6</td>
<td align="center">64.0</td>
<td align="center">137261548</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x/137261548/model_final_04e291.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x/137261548/metrics.json">metrics</a></td>
</tr>
<!-- ROW: keypoint_rcnn_R_50_FPN_3x -->
<tr><td align="left"><a href="configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml">R50-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.316</td>
<td align="center">0.066</td>
<td align="center">5.0</td>
<td align="center">55.4</td>
<td align="center">65.5</td>
<td align="center">137849621</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x/137849621/model_final_a6e10b.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x/137849621/metrics.json">metrics</a></td>
</tr>
<!-- ROW: keypoint_rcnn_R_101_FPN_3x -->
<tr><td align="left"><a href="configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x.yaml">R101-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.390</td>
<td align="center">0.076</td>
<td align="center">6.1</td>
<td align="center">56.4</td>
<td align="center">66.1</td>
<td align="center">138363331</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x/138363331/model_final_997cc7.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x/138363331/metrics.json">metrics</a></td>
</tr>
<!-- ROW: keypoint_rcnn_X_101_32x8d_FPN_3x -->
<tr><td align="left"><a href="configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml">X101-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.738</td>
<td align="center">0.121</td>
<td align="center">8.7</td>
<td align="center">57.3</td>
<td align="center">66.0</td>
<td align="center">139686956</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x/139686956/model_final_5ad38f.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x/139686956/metrics.json">metrics</a></td>
</tr>
</tbody></table>
### COCO Panoptic Segmentation Baselines with Panoptic FPN
<!--
./gen_html_table.py --config 'COCO-PanopticSegmentation/*50*' 'COCO-PanopticSegmentation/*101*' --name R50-FPN R50-FPN R101-FPN --fields lr_sched train_speed inference_speed mem box_AP mask_AP PQ
-->
<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th valign="bottom">Name</th>
<th valign="bottom">lr<br/>sched</th>
<th valign="bottom">train<br/>time<br/>(s/iter)</th>
<th valign="bottom">inference<br/>time<br/>(s/im)</th>
<th valign="bottom">train<br/>mem<br/>(GB)</th>
<th valign="bottom">box<br/>AP</th>
<th valign="bottom">mask<br/>AP</th>
<th valign="bottom">PQ</th>
<th valign="bottom">model id</th>
<th valign="bottom">download</th>
<!-- TABLE BODY -->
<!-- ROW: panoptic_fpn_R_50_1x -->
<tr><td align="left"><a href="configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.yaml">R50-FPN</a></td>
<td align="center">1x</td>
<td align="center">0.304</td>
<td align="center">0.053</td>
<td align="center">4.8</td>
<td align="center">37.6</td>
<td align="center">34.7</td>
<td align="center">39.4</td>
<td align="center">139514544</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x/139514544/model_final_dbfeb4.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x/139514544/metrics.json">metrics</a></td>
</tr>
<!-- ROW: panoptic_fpn_R_50_3x -->
<tr><td align="left"><a href="configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml">R50-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.302</td>
<td align="center">0.053</td>
<td align="center">4.8</td>
<td align="center">40.0</td>
<td align="center">36.5</td>
<td align="center">41.5</td>
<td align="center">139514569</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x/139514569/model_final_c10459.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x/139514569/metrics.json">metrics</a></td>
</tr>
<!-- ROW: panoptic_fpn_R_101_3x -->
<tr><td align="left"><a href="configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml">R101-FPN</a></td>
<td align="center">3x</td>
<td align="center">0.392</td>
<td align="center">0.066</td>
<td align="center">6.0</td>
<td align="center">42.4</td>
<td align="center">38.5</td>
<td align="center">43.0</td>
<td align="center">139514519</td>
<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x/139514519/model_final_cafdb1.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x/139514519/metrics.json">metrics</a></td>
</tr>
</tbody></table>
### LVIS Instance Segmentation Baselines with Mask R-CNN
Mask R-CNN baselines on the [LVIS dataset](https://lvisdataset.org), v0.5.
These baselines are described in Table 3(c) of the [LVIS paper](https://arxiv.org/abs/1908.03195).
NOTE: the 1x schedule here has the same amount of __iterations__ as the COCO 1x baselines.
They are roughly 24 epochs of LVISv0.5 data.
The final results of these configs have large variance across different runs.
<!--
./gen_html_table.py --config 'LVIS-InstanceSegmentation/mask*50*' 'LVIS-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/LVIS-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/LVIS-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/144219072/model_final_571f7c.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/LVIS-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/LVIS-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/LVIS-InstanceSegmentation/mask_rcnn_R_101_FPN_1x/144219035/model_final_824ab5.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/LVIS-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/LVIS-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/LVIS-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x/144219108/model_final_5e3439.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/LVIS-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">0.356</td>
<td align="center">0.069</td>
<td align="center">7.3</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">0.371</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">0.400</td>
<td align="center">0.069</td>
<td align="center">9.8</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">0.070</td>
<td align="center">9.8</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">0.107</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">0.242</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>
README.md
View file @ c732df65
xx
<img src=".github/Detectron2-Logo-Horz.svg" width="300" >
Detectron2 is Facebook AI Research's next generation software system
that implements state-of-the-art object detection algorithms.
It is a ground-up rewrite of the previous version,
[Detectron](https://github.com/facebookresearch/Detectron/),
and it originates from [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/).
<div align="center">
<img src="https://user-images.githubusercontent.com/1381301/66535560-d3422200-eace-11e9-9123-5535d469db19.png"/>
</div>
### What's New
* It is powered by the [PyTorch](https://pytorch.org) deep learning framework.
* Includes more features such as panoptic segmentation, densepose, Cascade R-CNN, rotated bounding boxes, etc.
* Can be used as a library to support [different projects](projects/) on top of it.
We'll open source more research projects in this way.
* It [trains much faster](https://detectron2.readthedocs.io/notes/benchmarks.html).
See our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/)
to see more demos and learn about detectron2.
## Installation
See [INSTALL.md](INSTALL.md).
## Quick Start
See [GETTING_STARTED.md](GETTING_STARTED.md),
or the [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5).
Learn more at our [documentation](https://detectron2.readthedocs.org).
And see [projects/](projects/) for some projects that are built on top of detectron2.
## Model Zoo and Baselines
We provide a large set of baseline results and trained models available for download in the [Detectron2 Model Zoo](MODEL_ZOO.md).
## License
Detectron2 is released under the [Apache 2.0 license](LICENSE).
## Citing Detectron2
If you use Detectron2 in your research or wish to refer to the baseline results published in the [Model Zoo](MODEL_ZOO.md), please use the following BibTeX entry.
```BibTeX
@misc{wu2019detectron2,
author = {Yuxin Wu and Alexander Kirillov and Francisco Massa and
Wan-Yen Lo and Ross Girshick},
title = {Detectron2},
howpublished = {\url{https://github.com/facebookresearch/detectron2}},
year = {2019}
}
```
configs/Base-RCNN-C4.yaml 0 → 100644
View file @ c732df65
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
configs/Base-RCNN-DilatedC5.yaml 0 → 100644
View file @ c732df65
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
configs/Base-RCNN-FPN.yaml 0 → 100644
View file @ c732df65
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
configs/Base-RetinaNet.yaml 0 → 100644
View file @ c732df65
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]
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
configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml 0 → 100644
View file @ c732df65
_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
configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml 0 → 100644
View file @ c732df65
_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
configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml 0 → 100644
View file @ c732df65
_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
configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml 0 → 100644
View file @ c732df65
_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
configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml 0 → 100644
View file @ c732df65
_BASE_: "../Base-RCNN-C4.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: False
RESNETS:
DEPTH: 50
configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml 0 → 100644
View file @ c732df65
_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
configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml 0 → 100644
View file @ c732df65
_BASE_: "../Base-RCNN-DilatedC5.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: False
RESNETS:
DEPTH: 50
configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml 0 → 100644
View file @ c732df65
_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
configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml 0 → 100644
View file @ c732df65
_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: False
RESNETS:
DEPTH: 50
configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml 0 → 100644
View file @ c732df65
_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
configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml 0 → 100644
View file @ c732df65
_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
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