This is a Pytorch implementation of Mask R-CNN that is in large parts based on Matterport's Mask_RCNN. Matterport's repository is an implementation on Keras and TensorFlow. The following parts of the README are excerpts from the Matterport README. Details on the requirements, training on MS COCO and detection results for this repository can be found at the end of the document.
The Mask R-CNN model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone.
The next four images visualize different stages in the detection pipeline:
1. Anchor sorting and filtering
The Region Proposal Network proposes bounding boxes that are likely to belong to an object. Positive and negative anchors along with anchor box refinement are visualized.
2. Bounding Box Refinement
This is an example of final detection boxes (dotted lines) and the refinement applied to them (solid lines) in the second stage.
3. Mask Generation
Examples of generated masks. These then get scaled and placed on the image in the right location.
4. Composing the different pieces into a final result
- Python 3
- Pytorch 0.3
- matplotlib, scipy, skimage, h5py
Clone this repository.
git clone https://github.com/multimodallearning/pytorch-mask-rcnn.git
We use functions from two more repositories that need to be build with the right
--archoption for cuda support. The two functions are Non-Maximum Suppression from ruotianluo's pytorch-faster-rcnn repository and longcw's RoiAlign.
GPU arch TitanX sm_52 GTX 960M sm_50 GTX 1070 sm_61 GTX 1080 (Ti) sm_61
cd nms/src/cuda/ nvcc -c -o nms_kernel.cu.o nms_kernel.cu -x cu -Xcompiler -fPIC -arch=[arch] cd ../../ python build.py cd ../ cd roialign/roi_align/src/cuda/ nvcc -c -o crop_and_resize_kernel.cu.o crop_and_resize_kernel.cu -x cu -Xcompiler -fPIC -arch=[arch] cd ../../ python build.py cd ../../
ln -s /path/to/coco/cocoapi/PythonAPI/pycocotools/ pycocotools
Download the pretrained models on COCO and ImageNet from Google Drive.
To test your installation simply run the demo with
It works on CPU or GPU and the result should look like this:
Training on COCO
Training and evaluation code is in coco.py. You can run it from the command line as such:
# Train a new model starting from pre-trained COCO weights python coco.py train --dataset=/path/to/coco/ --model=coco # Train a new model starting from ImageNet weights python coco.py train --dataset=/path/to/coco/ --model=imagenet # Continue training a model that you had trained earlier python coco.py train --dataset=/path/to/coco/ --model=/path/to/weights.h5 # Continue training the last model you trained. This will find # the last trained weights in the model directory. python coco.py train --dataset=/path/to/coco/ --model=last
If you have not yet downloaded the COCO dataset you should run the command with the download option set, e.g.:
# Train a new model starting from pre-trained COCO weights python coco.py train --dataset=/path/to/coco/ --model=coco --download=true
You can also run the COCO evaluation code with:
# Run COCO evaluation on the last trained model python coco.py evaluate --dataset=/path/to/coco/ --model=last
The training schedule, learning rate, and other parameters can be set in coco.py.
COCO results for bounding box and segmentation are reported based on training with the default configuration and backbone initialized with pretrained ImageNet weights. Used metric is AP on IoU=0.50:0.95.
|from scratch||converted from keras||Matterport's Mask_RCNN||Mask R-CNN paper|