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## Getting Started with Detectron2
This document provides a brief intro of the usage of builtin command-line tools in detectron2.
For a tutorial that involves actual coding with the API,
see our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
which covers how to run inference with an
existing model, and how to train a builtin model on a custom dataset.
### Inference Demo with Pre-trained Models
1. Pick a model and its config file from
[model zoo](MODEL_ZOO.md),
for example, `mask_rcnn_R_50_FPN_3x.yaml`.
2. We provide `demo.py` that is able to demo builtin configs. Run it with:
```
cd demo/
python demo.py --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \
--input input1.jpg input2.jpg \
[--other-options]
--opts MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl
```
The configs are made for training, therefore we need to specify `MODEL.WEIGHTS` to a model from model zoo for evaluation.
This command will run the inference and show visualizations in an OpenCV window.
For details of the command line arguments, see `demo.py -h` or look at its source code
to understand its behavior. Some common arguments are:
* To run __on your webcam__, replace `--input files` with `--webcam`.
* To run __on a video__, replace `--input files` with `--video-input video.mp4`.
* To run __on cpu__, add `MODEL.DEVICE cpu` after `--opts`.
* To save outputs to a directory (for images) or a file (for webcam or video), use `--output`.
### Training & Evaluation in Command Line
We provide two scripts in "tools/plain_train_net.py" and "tools/train_net.py",
that are made to train all the configs provided in detectron2. You may want to
use it as a reference to write your own training script.
Compared to "train_net.py", "plain_train_net.py" supports fewer default
features. It also includes fewer abstraction, therefore is easier to add custom
logic.
To train a model with "train_net.py", first
setup the corresponding datasets following
[datasets/README.md](./datasets/README.md),
then run:
```
cd tools/
./train_net.py --num-gpus 8 \
--config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml
```
The configs are made for 8-GPU training.
To train on 1 GPU, you may need to [change some parameters](https://arxiv.org/abs/1706.02677), e.g.:
```
./train_net.py \
--config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \
--num-gpus 1 SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025
```
To evaluate a model's performance, use
```
./train_net.py \
--config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \
--eval-only MODEL.WEIGHTS /path/to/checkpoint_file
```
For more options, see `./train_net.py -h`.
### Use Detectron2 APIs in Your Code
See our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
to learn how to use detectron2 APIs to:
1. run inference with an existing model
2. train a builtin model on a custom dataset
See [detectron2/projects](https://github.com/facebookresearch/detectron2/tree/master/projects)
for more ways to build your project on detectron2.
INSTALL.md 0 → 100644
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## Installation
### Requirements
- Linux or macOS with Python ≥ 3.6
- PyTorch ≥ 1.8 and [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation.
Install them together at [pytorch.org](https://pytorch.org) to make sure of this
- OpenCV is optional but needed by demo and visualization
### Build Detectron2 from Source
gcc & g++ ≥ 5.4 are required. [ninja](https://ninja-build.org/) is optional but recommended for faster build.
After having them, run:
```
python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
# (add --user if you don't have permission)
# Or, to install it from a local clone:
git clone https://github.com/facebookresearch/detectron2.git
python -m pip install -e detectron2
# On macOS, you may need to prepend the above commands with a few environment variables:
CC=clang CXX=clang++ ARCHFLAGS="-arch x86_64" python -m pip install ...
```
To __rebuild__ detectron2 that's built from a local clone, use `rm -rf build/ **/*.so` to clean the
old build first. You often need to rebuild detectron2 after reinstalling PyTorch.
### Install Pre-Built Detectron2 (Linux only)
Choose from this table to install [v0.6 (Oct 2021)](https://github.com/facebookresearch/detectron2/releases):
<table class="docutils"><tbody><th width="80"> CUDA </th><th valign="bottom" align="left" width="100">torch 1.10</th><th valign="bottom" align="left" width="100">torch 1.9</th><th valign="bottom" align="left" width="100">torch 1.8</th> <tr><td align="left">11.3</td><td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html
</code></pre> </details> </td> <td align="left"> </td> <td align="left"> </td> </tr> <tr><td align="left">11.1</td><td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.10/index.html
</code></pre> </details> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.9/index.html
</code></pre> </details> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.8/index.html
</code></pre> </details> </td> </tr> <tr><td align="left">10.2</td><td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.10/index.html
</code></pre> </details> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.9/index.html
</code></pre> </details> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.8/index.html
</code></pre> </details> </td> </tr> <tr><td align="left">10.1</td><td align="left"> </td> <td align="left"> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.8/index.html
</code></pre> </details> </td> </tr> <tr><td align="left">cpu</td><td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/torch1.10/index.html
</code></pre> </details> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/torch1.9/index.html
</code></pre> </details> </td> <td align="left"><details><summary> install </summary><pre><code>python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/torch1.8/index.html
</code></pre> </details> </td> </tr></tbody></table>
Note that:
1. The pre-built packages have to be used with corresponding version of CUDA and the official package of PyTorch.
Otherwise, please build detectron2 from source.
2. New packages are released every few months. Therefore, packages may not contain latest features in the master
branch and may not be compatible with the master branch of a research project that uses detectron2
(e.g. those in [projects](projects)).
### Common Installation Issues
Click each issue for its solutions:
<details>
<summary>
Undefined symbols that contains TH,aten,torch,caffe2.
</summary>
<br/>
This usually happens when detectron2 or torchvision is not
compiled with the version of PyTorch you're running.
If the error comes from a pre-built torchvision, uninstall torchvision and pytorch and reinstall them
following [pytorch.org](http://pytorch.org). So the versions will match.
If the error comes from a pre-built detectron2, check [release notes](https://github.com/facebookresearch/detectron2/releases),
uninstall and reinstall the correct pre-built detectron2 that matches pytorch version.
If the error comes from detectron2 or torchvision that you built manually from source,
remove files you built (`build/`, `**/*.so`) and rebuild it so it can pick up the version of pytorch currently in your environment.
If the above instructions do not resolve this problem, please provide an environment (e.g. a dockerfile) that can reproduce the issue.
</details>
<details>
<summary>
Missing torch dynamic libraries, OR segmentation fault immediately when using detectron2.
</summary>
This usually happens when detectron2 or torchvision is not
compiled with the version of PyTorch you're running. See the previous common issue for the solution.
</details>
<details>
<summary>
Undefined C++ symbols (e.g. GLIBCXX) or C++ symbols not found.
</summary>
<br/>
Usually it's because the library is compiled with a newer C++ compiler but run with an old C++ runtime.
This often happens with old anaconda.
It may help to run `conda update libgcc` to upgrade its runtime.
The fundamental solution is to avoid the mismatch, either by compiling using older version of C++
compiler, or run the code with proper C++ runtime.
To run the code with a specific C++ runtime, you can use environment variable `LD_PRELOAD=/path/to/libstdc++.so`.
</details>
<details>
<summary>
"nvcc not found" or "Not compiled with GPU support" or "Detectron2 CUDA Compiler: not available".
</summary>
<br/>
CUDA is not found when building detectron2.
You should make sure
```
python -c 'import torch; from torch.utils.cpp_extension import CUDA_HOME; print(torch.cuda.is_available(), CUDA_HOME)'
```
print `(True, a directory with cuda)` at the time you build detectron2.
Most models can run inference (but not training) without GPU support. To use CPUs, set `MODEL.DEVICE='cpu'` in the config.
</details>
<details>
<summary>
"invalid device function" or "no kernel image is available for execution".
</summary>
<br/>
Two possibilities:
* You build detectron2 with one version of CUDA but run it with a different version.
To check whether it is the case,
use `python -m detectron2.utils.collect_env` to find out inconsistent CUDA versions.
In the output of this command, you should expect "Detectron2 CUDA Compiler", "CUDA_HOME", "PyTorch built with - CUDA"
to contain cuda libraries of the same version.
When they are inconsistent,
you need to either install a different build of PyTorch (or build by yourself)
to match your local CUDA installation, or install a different version of CUDA to match PyTorch.
* PyTorch/torchvision/Detectron2 is not built for the correct GPU SM architecture (aka. compute capability).
The architecture included by PyTorch/detectron2/torchvision is available in the "architecture flags" in
`python -m detectron2.utils.collect_env`. It must include
the architecture of your GPU, which can be found at [developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus).
If you're using pre-built PyTorch/detectron2/torchvision, they have included support for most popular GPUs already.
If not supported, you need to build them from source.
When building detectron2/torchvision from source, they detect the GPU device and build for only the device.
This means the compiled code may not work on a different GPU device.
To recompile them for the correct architecture, remove all installed/compiled files,
and rebuild them with the `TORCH_CUDA_ARCH_LIST` environment variable set properly.
For example, `export TORCH_CUDA_ARCH_LIST="6.0;7.0"` makes it compile for both P100s and V100s.
</details>
<details>
<summary>
Undefined CUDA symbols; Cannot open libcudart.so
</summary>
<br/>
The version of NVCC you use to build detectron2 or torchvision does
not match the version of CUDA you are running with.
This often happens when using anaconda's CUDA runtime.
Use `python -m detectron2.utils.collect_env` to find out inconsistent CUDA versions.
In the output of this command, you should expect "Detectron2 CUDA Compiler", "CUDA_HOME", "PyTorch built with - CUDA"
to contain cuda libraries of the same version.
When they are inconsistent,
you need to either install a different build of PyTorch (or build by yourself)
to match your local CUDA installation, or install a different version of CUDA to match PyTorch.
</details>
<details>
<summary>
C++ compilation errors from NVCC / NVRTC, or "Unsupported gpu architecture"
</summary>
<br/>
A few possibilities:
1. Local CUDA/NVCC version has to match the CUDA version of your PyTorch. Both can be found in `python collect_env.py`.
When they are inconsistent, you need to either install a different build of PyTorch (or build by yourself)
to match your local CUDA installation, or install a different version of CUDA to match PyTorch.
2. Local CUDA/NVCC version shall support the SM architecture (a.k.a. compute capability) of your GPU.
The capability of your GPU can be found at [developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus).
The capability supported by NVCC is listed at [here](https://gist.github.com/ax3l/9489132).
If your NVCC version is too old, this can be workaround by setting environment variable
`TORCH_CUDA_ARCH_LIST` to a lower, supported capability.
3. The combination of NVCC and GCC you use is incompatible. You need to change one of their versions.
See [here](https://gist.github.com/ax3l/9489132) for some valid combinations.
Notably, CUDA<=10.1.105 doesn't support GCC>7.3.
The CUDA/GCC version used by PyTorch can be found by `print(torch.__config__.show())`.
</details>
<details>
<summary>
"ImportError: cannot import name '_C'".
</summary>
<br/>
Please build and install detectron2 following the instructions above.
Or, if you are running code from detectron2's root directory, `cd` to a different one.
Otherwise you may not import the code that you installed.
</details>
<details>
<summary>
Any issue on windows.
</summary>
<br/>
Detectron2 is continuously built on windows with [CircleCI](https://app.circleci.com/pipelines/github/facebookresearch/detectron2?branch=master).
However we do not provide official support for it.
PRs that improves code compatibility on windows are welcome.
</details>
<details>
<summary>
ONNX conversion segfault after some "TraceWarning".
</summary>
<br/>
The ONNX package is compiled with a too old compiler.
Please build and install ONNX from its source code using a compiler
whose version is closer to what's used by PyTorch (available in `torch.__config__.show()`).
</details>
<details>
<summary>
"library not found for -lstdc++" on older version of MacOS
</summary>
<br/>
See
[this stackoverflow answer](https://stackoverflow.com/questions/56083725/macos-build-issues-lstdc-not-found-while-building-python-package).
</details>
### Installation inside specific environments:
* __Colab__: see our [Colab Tutorial](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
which has step-by-step instructions.
* __Docker__: The official [Dockerfile](docker) installs detectron2 with a few simple commands.
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README_ORIGIN.md 0 → 100644
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<img src=".github/Detectron2-Logo-Horz.svg" width="300" >
Detectron2 is Facebook AI Research's next generation library
that provides state-of-the-art detection and segmentation algorithms.
It is the successor of
[Detectron](https://github.com/facebookresearch/Detectron/)
and [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/).
It supports a number of computer vision research projects and production applications in Facebook.
<div align="center">
<img src="https://user-images.githubusercontent.com/1381301/66535560-d3422200-eace-11e9-9123-5535d469db19.png"/>
</div>
### What's New
* Includes new capabilities such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend,
DeepLab, etc.
* Used as a library to support building [research projects](projects/) on top of it.
* Models can be exported to TorchScript format or Caffe2 format for deployment.
* It [trains much faster](https://detectron2.readthedocs.io/notes/benchmarks.html).
See our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/)
to see more demos and learn about detectron2.
## Installation
See [installation instructions](https://detectron2.readthedocs.io/tutorials/install.html).
## Getting Started
See [Getting Started with Detectron2](https://detectron2.readthedocs.io/tutorials/getting_started.html),
and the [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
to learn about basic usage.
Learn more at our [documentation](https://detectron2.readthedocs.org).
And see [projects/](projects/) for some projects that are built on top of detectron2.
## Model Zoo and Baselines
We provide a large set of baseline results and trained models available for download in the [Detectron2 Model Zoo](MODEL_ZOO.md).
## License
Detectron2 is released under the [Apache 2.0 license](LICENSE).
## Citing Detectron2
If you use Detectron2 in your research or wish to refer to the baseline results published in the [Model Zoo](MODEL_ZOO.md), please use the following BibTeX entry.
```BibTeX
@misc{wu2019detectron2,
author = {Yuxin Wu and Alexander Kirillov and Francisco Massa and
Wan-Yen Lo and Ross Girshick},
title = {Detectron2},
howpublished = {\url{https://github.com/facebookresearch/detectron2}},
year = {2019}
}
```
configs/Base-RCNN-C4.yaml 0 → 100644
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MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
RPN:
PRE_NMS_TOPK_TEST: 6000
POST_NMS_TOPK_TEST: 1000
ROI_HEADS:
NAME: "Res5ROIHeads"
DATASETS:
TRAIN: ("coco_2017_train",)
TEST: ("coco_2017_val",)
SOLVER:
IMS_PER_BATCH: 16
BASE_LR: 0.02
STEPS: (60000, 80000)
MAX_ITER: 90000
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
VERSION: 2
configs/Base-RCNN-DilatedC5.yaml 0 → 100644
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MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
RESNETS:
OUT_FEATURES: ["res5"]
RES5_DILATION: 2
RPN:
IN_FEATURES: ["res5"]
PRE_NMS_TOPK_TEST: 6000
POST_NMS_TOPK_TEST: 1000
ROI_HEADS:
NAME: "StandardROIHeads"
IN_FEATURES: ["res5"]
ROI_BOX_HEAD:
NAME: "FastRCNNConvFCHead"
NUM_FC: 2
POOLER_RESOLUTION: 7
ROI_MASK_HEAD:
NAME: "MaskRCNNConvUpsampleHead"
NUM_CONV: 4
POOLER_RESOLUTION: 14
DATASETS:
TRAIN: ("coco_2017_train",)
TEST: ("coco_2017_val",)
SOLVER:
IMS_PER_BATCH: 16
BASE_LR: 0.02
STEPS: (60000, 80000)
MAX_ITER: 90000
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
VERSION: 2
configs/Base-RCNN-FPN.yaml 0 → 100644
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MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
BACKBONE:
NAME: "build_resnet_fpn_backbone"
RESNETS:
OUT_FEATURES: ["res2", "res3", "res4", "res5"]
FPN:
IN_FEATURES: ["res2", "res3", "res4", "res5"]
ANCHOR_GENERATOR:
SIZES: [[32], [64], [128], [256], [512]] # One size for each in feature map
ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps)
RPN:
IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level
PRE_NMS_TOPK_TEST: 1000 # Per FPN level
# Detectron1 uses 2000 proposals per-batch,
# (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
# which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
POST_NMS_TOPK_TRAIN: 1000
POST_NMS_TOPK_TEST: 1000
ROI_HEADS:
NAME: "StandardROIHeads"
IN_FEATURES: ["p2", "p3", "p4", "p5"]
ROI_BOX_HEAD:
NAME: "FastRCNNConvFCHead"
NUM_FC: 2
POOLER_RESOLUTION: 7
ROI_MASK_HEAD:
NAME: "MaskRCNNConvUpsampleHead"
NUM_CONV: 4
POOLER_RESOLUTION: 14
DATASETS:
TRAIN: ("coco_2017_train",)
TEST: ("coco_2017_val",)
SOLVER:
IMS_PER_BATCH: 16
BASE_LR: 0.02
STEPS: (60000, 80000)
MAX_ITER: 90000
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
VERSION: 2
configs/Base-RetinaNet.yaml 0 → 100644
View file @ b634945d
MODEL:
META_ARCHITECTURE: "RetinaNet"
BACKBONE:
NAME: "build_retinanet_resnet_fpn_backbone"
RESNETS:
OUT_FEATURES: ["res3", "res4", "res5"]
ANCHOR_GENERATOR:
SIZES: !!python/object/apply:eval ["[[x, x * 2**(1.0/3), x * 2**(2.0/3) ] for x in [32, 64, 128, 256, 512 ]]"]
FPN:
IN_FEATURES: ["res3", "res4", "res5"]
RETINANET:
IOU_THRESHOLDS: [0.4, 0.5]
IOU_LABELS: [0, -1, 1]
SMOOTH_L1_LOSS_BETA: 0.0
DATASETS:
TRAIN: ("coco_2017_train",)
TEST: ("coco_2017_val",)
SOLVER:
IMS_PER_BATCH: 16
BASE_LR: 0.01 # Note that RetinaNet uses a different default learning rate
STEPS: (60000, 80000)
MAX_ITER: 90000
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
VERSION: 2
configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml 0 → 100644
View file @ b634945d
_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 @ b634945d
_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 @ b634945d
_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 @ b634945d
_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 @ b634945d
_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 @ b634945d
_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 @ b634945d
_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 @ b634945d
_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 @ b634945d
_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 @ b634945d
_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 @ b634945d
_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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