maskrcnn

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pytorch mask rcnn 736px，box mAP 39.0, seg mAP 33.7 (COCO val) 23.64fps(RTX 2080Ti)

maskrcnn 최신버젼 다운로드

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# Mask RCNN
This is an unofficial pytorch implementation of MaskRCNN instance aware segmentation as described in [Mask R-CNN](https://arxiv.org/abs/1703.06870) by Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick

## requirement
```text
tqdm
pyyaml
numpy
opencv-python
pycocotools
torch >= 1.5
torchvision >=0.6.0
```
## result
we trained this repo on 4 GPUs with batch size 16(4 image per node).the total epoch is 24(about 180k iter),Adam with cosine lr decay is used for optimizing.
finally, this repo achieves 39.0 mAP(box) 33.7mAP(seg) at 736px(max thresh) resolution with resnet50 backbone.(about 23.64)
### detection mAP
```shell script
Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.390
Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.598
Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.419
Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.214
Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.436
Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.530
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.322
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.508
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.534
Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.330
Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.585
Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.705
```
### instance segmentation mAP
```shell script
Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.337
Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.557
Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.353
Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.128
Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.371
Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.530
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.288
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.443
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.462
Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237
Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.516
Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.651
```
## training
for now we only support coco data.
### COCO
* modify main.py (modify config file path)
```python
from solver.ddp_mix_solver import DDPMixSolver

if __name__ == '__main__':
    processor = DDPMixSolver(cfg_path="config/maskrcnn.yaml")
    processor.run()

```
* custom some parameters in *config.yaml*
```yaml
model_name: mask_rcnn
data:
  train_annotation_path: data/coco/annotations/instances_train2017.json
#  train_annotation_path: data/coco/annotations/instances_val2017.json
  val_annotation_path: data/coco/annotations/instances_val2017.json
  train_img_root: data/coco/train2017
#  train_img_root: data/coco/val2017
  val_img_root: data/coco/val2017
  max_thresh: 768
  use_crowd: False
  batch_size: 4
  num_workers: 2
  debug: False
  remove_blank: Ture

model:
  num_cls: 80
  backbone: resnet50
  pretrained: True
  reduction: False
  fpn_channel: 256
  fpn_bias: True
  anchor_sizes: [32.0, 64.0, 128.0, 256.0, 512.0]
  anchor_scales: [1.0, ]
  anchor_ratios: [0.5, 1.0, 2.0]
  strides: [4.0, 8.0, 16.0, 32.0, 64.0]
  box_score_thresh: 0.05
  box_nms_thresh: 0.5
  box_detections_per_img: 100

optim:
  optimizer: Adam
  lr: 0.0001
  milestones: [24,]
  warm_up_epoch: 0
  weight_decay: 0.0001
  epochs: 24
  sync_bn: True
  amp: True
val:
  interval: 1
  weight_path: weights

gpus: 0,1,2,3
```
**detailed settings reference to nets.mask_rcnn.default_cfg**
* run train scripts
```shell script
nohup python -m torch.distributed.launch --nproc_per_node=4 main.py >>train.log 2>&1 &
```

## TODO
- [x] Color Jitter
- [x] Perspective Transform
- [x] Mosaic Augment
- [x] MixUp Augment
- [x] IOU GIOU DIOU CIOU
- [x] Warming UP
- [x] Cosine Lr Decay
- [x] EMA(Exponential Moving Average)
- [x] Mixed Precision Training (supported by apex)
- [x] Sync Batch Normalize
- [ ] PANet(neck)
- [ ] BiFPN(EfficientDet neck)
- [ ] VOC data train\test scripts
- [ ] custom data train\test scripts
- [ ] MobileNet Backbone support

maskrcnn

maskrcnn 최신버젼 다운로드

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maskrcnn 최신버젼 다운로드

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