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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>