diff --git a/GETTING_STARTED.md b/GETTING_STARTED.md
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+## Getting Started with Detectron2
+
+This document provides a brief intro of the usage of builtin command-line tools in detectron2.
+
+For a tutorial that involves actual coding with the API,
+see our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
+which covers how to run inference with an
+existing model, and how to train a builtin model on a custom dataset.
+
+For more advanced tutorials, refer to our [documentation](https://detectron2.readthedocs.io/tutorials/extend.html).
+
+
+### Inference Demo with Pre-trained Models
+
+1. Pick a model and its config file from
+ [model zoo](MODEL_ZOO.md),
+ for example, `mask_rcnn_R_50_FPN_3x.yaml`.
+2. We provide `demo.py` that is able to demo builtin configs. Run it with:
+```
+cd demo/
+python demo.py --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \
+ --input input1.jpg input2.jpg \
+ [--other-options]
+ --opts MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl
+```
+The configs are made for training, therefore we need to specify `MODEL.WEIGHTS` to a model from model zoo for evaluation.
+This command will run the inference and show visualizations in an OpenCV window.
+
+For details of the command line arguments, see `demo.py -h` or look at its source code
+to understand its behavior. Some common arguments are:
+* To run __on your webcam__, replace `--input files` with `--webcam`.
+* To run __on a video__, replace `--input files` with `--video-input video.mp4`.
+* To run __on cpu__, add `MODEL.DEVICE cpu` after `--opts`.
+* To save outputs to a directory (for images) or a file (for webcam or video), use `--output`.
+
+
+### Training & Evaluation in Command Line
+
+We provide two scripts in "tools/plain_train_net.py" and "tools/train_net.py",
+that are made to train all the configs provided in detectron2. You may want to
+use it as a reference to write your own training script.
+
+Compared to "train_net.py", "plain_train_net.py" supports fewer default
+features. It also includes fewer abstraction, therefore is easier to add custom
+logic.
+
+To train a model with "train_net.py", first
+setup the corresponding datasets following
+[datasets/README.md](./datasets/README.md),
+then run:
+```
+cd tools/
+./train_net.py --num-gpus 8 \
+ --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml
+```
+
+The configs are made for 8-GPU training.
+To train on 1 GPU, you may need to [change some parameters](https://arxiv.org/abs/1706.02677), e.g.:
+```
+./train_net.py \
+ --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \
+ --num-gpus 1 SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025
+```
+
+For most models, CPU training is not supported.
+
+To evaluate a model's performance, use
+```
+./train_net.py \
+ --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \
+ --eval-only MODEL.WEIGHTS /path/to/checkpoint_file
+```
+For more options, see `./train_net.py -h`.
+
+### Use Detectron2 APIs in Your Code
+
+See our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
+to learn how to use detectron2 APIs to:
+1. run inference with an existing model
+2. train a builtin model on a custom dataset
+
+See [detectron2/projects](https://github.com/facebookresearch/detectron2/tree/master/projects)
+for more ways to build your project on detectron2.
diff --git a/MODEL_ZOO.md b/MODEL_ZOO.md
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+# Detectron2 Model Zoo and Baselines
+
+## Introduction
+
+This file documents a large collection of baselines trained
+with detectron2 in Sep-Oct, 2019.
+All numbers were obtained on [Big Basin](https://engineering.fb.com/data-center-engineering/introducing-big-basin-our-next-generation-ai-hardware/)
+servers with 8 NVIDIA V100 GPUs & NVLink. The software in use were PyTorch 1.3, CUDA 9.2, cuDNN 7.4.2 or 7.6.3.
+You can access these models from code using [detectron2.model_zoo](https://detectron2.readthedocs.io/modules/model_zoo.html) APIs.
+
+In addition to these official baseline models, you can find more models in [projects/](projects/).
+
+#### How to Read the Tables
+* The "Name" column contains a link to the config file. Running `tools/train_net.py --num-gpus 8` with this config file
+ will reproduce the model.
+* Training speed is averaged across the entire training.
+ We keep updating the speed with latest version of detectron2/pytorch/etc.,
+ so they might be different from the `metrics` file.
+ Training speed for multi-machine jobs is not provided.
+* Inference speed is measured by `tools/train_net.py --eval-only`, or [inference_on_dataset()](https://detectron2.readthedocs.io/modules/evaluation.html#detectron2.evaluation.inference_on_dataset),
+ with batch size 1 in detectron2 directly.
+ Measuring it with custom code may introduce other overhead.
+ Actual deployment in production should in general be faster than the given inference
+ speed due to more optimizations.
+* The *model id* column is provided for ease of reference.
+ To check downloaded file integrity, any model on this page contains its md5 prefix in its file name.
+* Training curves and other statistics can be found in `metrics` for each model.
+
+#### Common Settings for COCO Models
+* All COCO models were trained on `train2017` and evaluated on `val2017`.
+* The default settings are __not directly comparable__ with Detectron's standard settings.
+ For example, our default training data augmentation uses scale jittering in addition to horizontal flipping.
+
+ To make fair comparisons with Detectron's settings, see
+ [Detectron1-Comparisons](configs/Detectron1-Comparisons/) for accuracy comparison,
+ and [benchmarks](https://detectron2.readthedocs.io/notes/benchmarks.html)
+ for speed comparison.
+* For Faster/Mask R-CNN, we provide baselines based on __3 different backbone combinations__:
+ * __FPN__: Use a ResNet+FPN backbone with standard conv and FC heads for mask and box prediction,
+ respectively. It obtains the best
+ speed/accuracy tradeoff, but the other two are still useful for research.
+ * __C4__: Use a ResNet conv4 backbone with conv5 head. The original baseline in the Faster R-CNN paper.
+ * __DC5__ (Dilated-C5): Use a ResNet conv5 backbone with dilations in conv5, and standard conv and FC heads
+ for mask and box prediction, respectively.
+ This is used by the Deformable ConvNet paper.
+* Most models are trained with the 3x schedule (~37 COCO epochs).
+ Although 1x models are heavily under-trained, we provide some ResNet-50 models with the 1x (~12 COCO epochs)
+ training schedule for comparison when doing quick research iteration.
+
+#### ImageNet Pretrained Models
+
+It's common to initialize from backbone models pre-trained on ImageNet classification tasks. The following backbone models are available:
+
+* [R-50.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-50.pkl): converted copy of [MSRA's original ResNet-50](https://github.com/KaimingHe/deep-residual-networks) model.
+* [R-101.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-101.pkl): converted copy of [MSRA's original ResNet-101](https://github.com/KaimingHe/deep-residual-networks) model.
+* [X-101-32x8d.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/FAIR/X-101-32x8d.pkl): ResNeXt-101-32x8d model trained with Caffe2 at FB.
+* [R-50.pkl (torchvision)](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/torchvision/R-50.pkl): converted copy of [torchvision's ResNet-50](https://pytorch.org/docs/stable/torchvision/models.html#torchvision.models.resnet50) model.
+ More details can be found in [the conversion script](tools/convert-torchvision-to-d2.py).
+
+Note that the above models have __different__ format from those provided in Detectron: we do not fuse BatchNorm into an affine layer.
+Pretrained models in Detectron's format can still be used. For example:
+* [X-152-32x8d-IN5k.pkl](https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/25093814/X-152-32x8d-IN5k.pkl):
+ ResNeXt-152-32x8d model trained on ImageNet-5k with Caffe2 at FB (see ResNeXt paper for details on ImageNet-5k).
+* [R-50-GN.pkl](https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/47261647/R-50-GN.pkl):
+ ResNet-50 with Group Normalization.
+* [R-101-GN.pkl](https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/47592356/R-101-GN.pkl):
+ ResNet-101 with Group Normalization.
+
+#### License
+
+All models available for download through this document are licensed under the
+[Creative Commons Attribution-ShareAlike 3.0 license](https://creativecommons.org/licenses/by-sa/3.0/).
+
+### COCO Object Detection Baselines
+
+#### Faster R-CNN:
+
+
+
+
+
+#### RetinaNet:
+
+
+
+
+
+| Name |
+lr
sched |
+train
time
(s/iter) |
+inference
time
(s/im) |
+train
mem
(GB) |
+box
AP |
+model id |
+download |
+
+
+ | R50 |
+1x |
+0.205 |
+0.041 |
+4.1 |
+37.4 |
+190397773 |
+model | metrics |
+
+
+ | R50 |
+3x |
+0.205 |
+0.041 |
+4.1 |
+38.7 |
+190397829 |
+model | metrics |
+
+
+ | R101 |
+3x |
+0.291 |
+0.054 |
+5.2 |
+40.4 |
+190397697 |
+model | metrics |
+
+
+
+
+#### RPN & Fast R-CNN:
+
+
+
+
+### COCO Instance Segmentation Baselines with Mask R-CNN
+
+
+
+
+
+
+### COCO Person Keypoint Detection Baselines with Keypoint R-CNN
+
+
+
+
+
+
+| Name |
+lr
sched |
+train
time
(s/iter) |
+inference
time
(s/im) |
+train
mem
(GB) |
+box
AP |
+kp.
AP |
+model id |
+download |
+
+
+ | R50-FPN |
+1x |
+0.315 |
+0.072 |
+5.0 |
+53.6 |
+64.0 |
+137261548 |
+model | metrics |
+
+
+ | R50-FPN |
+3x |
+0.316 |
+0.066 |
+5.0 |
+55.4 |
+65.5 |
+137849621 |
+model | metrics |
+
+
+ | R101-FPN |
+3x |
+0.390 |
+0.076 |
+6.1 |
+56.4 |
+66.1 |
+138363331 |
+model | metrics |
+
+
+ | X101-FPN |
+3x |
+0.738 |
+0.121 |
+8.7 |
+57.3 |
+66.0 |
+139686956 |
+model | metrics |
+
+
+
+### COCO Panoptic Segmentation Baselines with Panoptic FPN
+
+
+
+
+
+
+| Name |
+lr
sched |
+train
time
(s/iter) |
+inference
time
(s/im) |
+train
mem
(GB) |
+box
AP |
+mask
AP |
+PQ |
+model id |
+download |
+
+
+ | R50-FPN |
+1x |
+0.304 |
+0.053 |
+4.8 |
+37.6 |
+34.7 |
+39.4 |
+139514544 |
+model | metrics |
+
+
+ | R50-FPN |
+3x |
+0.302 |
+0.053 |
+4.8 |
+40.0 |
+36.5 |
+41.5 |
+139514569 |
+model | metrics |
+
+
+ | R101-FPN |
+3x |
+0.392 |
+0.066 |
+6.0 |
+42.4 |
+38.5 |
+43.0 |
+139514519 |
+model | metrics |
+
+
+
+
+### 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.
+
+
+
+
+
+
+
+| Name |
+lr
sched |
+train
time
(s/iter) |
+inference
time
(s/im) |
+train
mem
(GB) |
+box
AP |
+mask
AP |
+model id |
+download |
+
+
+ | R50-FPN |
+1x |
+0.292 |
+0.107 |
+7.1 |
+23.6 |
+24.4 |
+144219072 |
+model | metrics |
+
+
+ | R101-FPN |
+1x |
+0.371 |
+0.114 |
+7.8 |
+25.6 |
+25.9 |
+144219035 |
+model | metrics |
+
+
+ | X101-FPN |
+1x |
+0.712 |
+0.151 |
+10.2 |
+26.7 |
+27.1 |
+144219108 |
+model | metrics |
+
+
+
+
+
+### 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)
+
+
+
+
+
+
+
+| Name |
+train
time
(s/iter) |
+inference
time
(s/im) |
+train
mem
(GB) |
+box
AP |
+box
AP50 |
+mask
AP |
+model id |
+download |
+
+
+ | R50-FPN, Cityscapes |
+0.240 |
+0.078 |
+4.4 |
+ |
+ |
+36.5 |
+142423278 |
+model | metrics |
+
+
+ | R50-C4, VOC |
+0.537 |
+0.081 |
+4.8 |
+51.9 |
+80.3 |
+ |
+142202221 |
+model | metrics |
+
+
+
+
+
+### Other Settings
+
+Ablations for Deformable Conv and Cascade R-CNN:
+
+
+
+
+
+
+
+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))
+
+
+
+
+
+
+A few very large models trained for a long time, for demo purposes. They are trained using multiple machines:
+
+
+
+
+
+
+
+| Name |
+inference
time
(s/im) |
+train
mem
(GB) |
+box
AP |
+mask
AP |
+PQ |
+model id |
+download |
+
+
+ | Panoptic FPN R101 |
+0.098 |
+11.4 |
+47.4 |
+41.3 |
+46.1 |
+139797668 |
+model | metrics |
+
+
+ | Mask R-CNN X152 |
+0.234 |
+15.1 |
+50.2 |
+44.0 |
+ |
+18131413 |
+model | metrics |
+
+
+ | above + test-time aug. |
+ |
+ |
+51.9 |
+45.9 |
+ |
+ |
+ |
+
+