https://github.com/shadyboukhary/gun-detection
https://github.com/shadyboukhary/gun-detection
Last synced: 8 months ago
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- Host: GitHub
- URL: https://github.com/shadyboukhary/gun-detection
- Owner: ShadyBoukhary
- Created: 2020-03-08T18:31:08.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-03-08T18:57:53.000Z (over 5 years ago)
- Last Synced: 2025-01-05T07:15:03.670Z (9 months ago)
- Language: Python
- Size: 59.6 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Faster R-CNN / Mask R-CNN on COCO
This example provides a minimal (2k lines) and faithful implementation of the
following object detection / instance segmentation papers:
+ [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/abs/1506.01497)
+ [Feature Pyramid Networks for Object Detection](https://arxiv.org/abs/1612.03144)
+ [Mask R-CNN](https://arxiv.org/abs/1703.06870)
+ [Cascade R-CNN: Delving into High Quality Object Detection](https://arxiv.org/abs/1712.00726)
with the support of:
+ Multi-GPU / multi-node distributed training, multi-GPU evaluation
+ Cross-GPU BatchNorm (aka Sync-BN, from [MegDet: A Large Mini-Batch Object Detector](https://arxiv.org/abs/1711.07240))
+ [Group Normalization](https://arxiv.org/abs/1803.08494)
+ Training from scratch (from [Rethinking ImageNet Pre-training](https://arxiv.org/abs/1811.08883))
This is likely the best-performing open source TensorFlow reimplementation of the above papers.
## Dependencies
+ OpenCV, TensorFlow ≥ 1.6
+ pycocotools/scipy: `for i in cython 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' scipy; do pip install $i; done`
+ Pre-trained [ImageNet ResNet model](http://models.tensorpack.com/#FasterRCNN)
from tensorpack model zoo
+ [COCO data](http://cocodataset.org/#download). It needs to have the following directory structure:
```
COCO/DIR/
annotations/
instances_train201?.json
instances_val201?.json
train201?/
# image files that are mentioned in the corresponding json
val201?/
# image files that are mentioned in corresponding json
```
You can use either the 2014 version or the 2017 version of the dataset.
To use the common "trainval35k + minival" split for the 2014 dataset, just
download the annotation files `instances_minival2014.json`,
`instances_valminusminival2014.json` from
[here](https://github.com/rbgirshick/py-faster-rcnn/blob/master/data/README.md)
to `annotations/` as well.
## Usage
It is recommended to get familiar the relevant papers listed above before using this code.
Otherwise you may end up doing something unreasonable.
### Train:
To train on a single machine (with 1 or more GPUs):
```
./train.py --config \
BACKBONE.WEIGHTS=/path/to/ImageNet-R50-AlignPadding.npz \
DATA.BASEDIR=/path/to/COCO/DIR \
[OTHER-ARCHITECTURE-SETTINGS]
```
Alternatively, use `TRAINER=horovod` which supports distributed training as well, but less straightforward to run.
Refer to [HorovodTrainer docs](http://tensorpack.readthedocs.io/modules/train.html#tensorpack.train.HorovodTrainer) for details.
All options can be changed by either the command line or the `config.py` file (recommended).
Some reasonable configurations are listed in the table below.
See [config.py](config.py) for details about how to correctly set `BACKBONE.WEIGHTS` and other configs.
### Inference:
To predict on given images (needs DISPLAY to show the outputs):
```
./predict.py --predict input1.jpg input2.jpg --load /path/to/Trained-Model-Checkpoint --config SAME-AS-TRAINING
```
To evaluate the performance of a model on COCO:
```
./predict.py --evaluate output.json --load /path/to/Trained-Model-Checkpoint \
--config SAME-AS-TRAINING
```
Several trained models can be downloaded in the table below. Evaluation and
prediction have to be run with the corresponding configs used in training.
## Results
These models are trained on train2017 and evaluated on val2017 using mAP@IoU=0.50:0.95.
Unless otherwise noted, all models are fine-tuned from ImageNet pre-trained R50/R101 models in
[tensorpack model zoo](http://models.tensorpack.com/#FasterRCNN),
using 8 NVIDIA V100s.
Performance in [Detectron](https://github.com/facebookresearch/Detectron/) can be reproduced.
| Backbone | mAP
(box;mask) | Detectron mAP [1](#ft1)
(box;mask) | Time
(on 8 V100s) | Configurations
(click to expand) |
| - | - | - | - | - |
| R50-FPN | 34.8 | | 6.5h | super quick`MODE_MASK=False FRCNN.BATCH_PER_IM=64`
`PREPROC.TRAIN_SHORT_EDGE_SIZE=[500,800] PREPROC.MAX_SIZE=1024` |
| R50-C4 | 35.6 | 34.8 | 22.5h | standard`MODE_MASK=False MODE_FPN=False` |
| R50-FPN | 37.5 | 36.7 | 10.5h | standard`MODE_MASK=False` |
| R50-C4 | 36.2;31.8 [:arrow_down:][R50C41x] | 35.8;31.4 | 23h | standard`MODE_FPN=False` |
| R50-FPN | 38.2;34.8 | 37.7;33.9 | 12.5h | standardthis is the default |
| R50-FPN | 38.9;35.4 [:arrow_down:][R50FPN2x] | 38.6;34.5 | 24h | 2x`TRAIN.LR_SCHEDULE=2x` |
| R50-FPN-GN | 40.4;36.3 [:arrow_down:][R50FPN2xGN] | 40.3;35.7 | 29h | 2x+GN`FPN.NORM=GN BACKBONE.NORM=GN`
`FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head`
`FPN.MRCNN_HEAD_FUNC=maskrcnn_up4conv_gn_head`
`TRAIN.LR_SCHEDULE=2x` |
| R50-FPN | 41.7;36.2 [:arrow_down:][R50FPN1xCas] | | 16h | +Cascade`FPN.CASCADE=True` |
| R50-FPN-GN | 46.1;40.1 [:arrow_down:][R50FPN4xGNCasAug] | | 36h (on 16 V100s) | 4x+GN+Cascade+TrainAug`FPN.CASCADE=True`
`FPN.NORM=GN BACKBONE.NORM=GN`
`FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head`
`FPN.MRCNN_HEAD_FUNC=maskrcnn_up4conv_gn_head`
`PREPROC.TRAIN_SHORT_EDGE_SIZE=[640,800]`
`TRAIN.LR_SCHEDULE=4x` |
| R101-C4 | 40.1;34.6 [:arrow_down:][R101C41x] | | 27h | standard`MODE_FPN=False`
`BACKBONE.RESNET_NUM_BLOCKS=[3,4,23,3]` |
| R101-FPN | 40.7;36.8 [:arrow_down:][R101FPN1x] [2](#ft2) | 40.0;35.9 | 17h | standard`BACKBONE.RESNET_NUM_BLOCKS=[3,4,23,3]` |
| R101-FPN | 46.6;40.3 [:arrow_down:][R101FPN3xCasAug] | | 64h | 3x+Cascade+TrainAug` FPN.CASCADE=True`
`BACKBONE.RESNET_NUM_BLOCKS=[3,4,23,3]`
`TEST.RESULT_SCORE_THRESH=1e-4`
`PREPROC.TRAIN_SHORT_EDGE_SIZE=[640,800]`
`TRAIN.LR_SCHEDULE=3x` |
| R101-FPN-GN
(From Scratch) | 47.7;41.7 [:arrow_down:][R101FPN9xGNCasAugScratch] [3](#ft3) | 47.4;40.5 | 28h (on 64 V100s) | 9x+GN+Cascade+TrainAug`FPN.CASCADE=True`
`BACKBONE.RESNET_NUM_BLOCKS=[3,4,23,3]`
`FPN.NORM=GN BACKBONE.NORM=GN`
`FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head`
`FPN.MRCNN_HEAD_FUNC=maskrcnn_up4conv_gn_head`
`PREPROC.TRAIN_SHORT_EDGE_SIZE=[640,800]`
`TRAIN.LR_SCHEDULE=9x`
`BACKBONE.FREEZE_AT=0` |
[R50C41x]: http://models.tensorpack.com/FasterRCNN/COCO-MaskRCNN-R50C41x.npz
[R50FPN2x]: http://models.tensorpack.com/FasterRCNN/COCO-MaskRCNN-R50FPN2x.npz
[R50FPN2xGN]: http://models.tensorpack.com/FasterRCNN/COCO-MaskRCNN-R50FPN2xGN.npz
[R50FPN1xCas]: http://models.tensorpack.com/FasterRCNN/COCO-MaskRCNN-R50FPN1xCas.npz
[R50FPN4xGNCasAug]: http://models.tensorpack.com/FasterRCNN/COCO-MaskRCNN-R50FPN4xGNCasAug.npz
[R101C41x]: http://models.tensorpack.com/FasterRCNN/COCO-MaskRCNN-R101C41x.npz
[R101FPN1x]: http://models.tensorpack.com/FasterRCNN/COCO-MaskRCNN-R101FPN1x.npz
[R101FPN3xCasAug]: http://models.tensorpack.com/FasterRCNN/COCO-MaskRCNN-R101FPN3xCasAug.npz
[R101FPN9xGNCasAugScratch]: http://models.tensorpack.com/FasterRCNN/COCO-MaskRCNN-R101FPN9xGNCasAugScratch.npz
1: Numbers taken from [Detectron Model Zoo](https://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md).
We compare models that have identical training & inference cost between the two implementations.
Their numbers can be different due to small implementation details.
2: Our mAP is __7 point__ better than the official model in
[matterport/Mask_RCNN](https://github.com/matterport/Mask_RCNN/releases/tag/v2.0) which has the same architecture.
Our implementation is also [5x faster](https://github.com/tensorpack/benchmarks/tree/master/MaskRCNN).
3: This entry does not use ImageNet pre-training. Detectron numbers are taken from Fig. 5 in [Rethinking ImageNet Pre-training](https://arxiv.org/abs/1811.08883).
Note that our training strategy is slightly different: we enable cascade throughout the entire training.
As far as I know, this model is the __best open source TF model__ on COCO dataset.
## Use Custom Datasets / Implementation Details / Speed:
See [BALLOON.md](BALLOON.md) and [NOTES.md](NOTES.md) for more details.