michelecafagna26 / faster-rcnn-bottom-up-py

Extract features and bounding boxes using the original Bottom-up Attention Faster-RCNN in a few lines of Python code

Bottom-up Attention with Detectron2 (Pytorch)

Extract features and bounding boxes predictions in a few lines of Python code.

This repo is a cleaned version of [airsplay] (https://github.com/airsplay/py-bottom-up-attention) repo. For more details refer to that repo.

The detectron2 system with exactly the same model and weight as the Caffe VG Faster R-CNN provided in bottom-up-attetion.

Installation

git clone https://github.com/michelecafagna26/faster-rcnn-bottom-up-py.git
cd faster-rcnn-bottom-up-py

# Install python libraries
pip install -r requirements.txt
pip install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'

# Install detectron2
python setup.py build develop

# or if you are on macOS
# MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build develop

# or, as an alternative to `setup.py`, do
# pip install [--editable] .

Quick start: Feature Extraction + Object Detection

from wrappers import FasterRCNNBottomUp
import cv2

IMG_FILE = "data/images/COCO_train2014_000000084002.jpg"

cfg_file = "configs/VG-Detection/faster_rcnn_R_101_C4_caffemaxpool_wrapper.yaml"
vg_objects = "data/genome/1600-400-20/objects_vocab.txt"

im = cv2.imread(IMG_FILE)
model = FasterRCNNBottomUp(cfg_file, object_txt = vg_objects, MAX_BOXES=150, MIN_BOXES=150)

instances, boxes = model([im], return_features=True)

To access the predicted object class:

class_id = instances[0].pred_classes
model.classes['thing_classes'][class_id]

Note from the original repo

1. The default weight is same to the 'alternative pretrained model' in the original github here, which is trained with 36 bbxes. If you want to use the original detetion trained with 10~100 bbxes, please use the following weight:

   http://nlp.cs.unc.edu/models/faster_rcnn_from_caffe_attr_original.pkl
   

2. The coordinate generated from the code is (x_left_corner, y_top_corner, x_right_corner, y_bottom_corner). Here is a visualization. Suppose the box = [x0, y0, x1, y1], it annotates an RoI of:

   0-------------------------------------
    |                                   |
    y0 box[1]   |-----------|           |
    |           |           |           |
    |           |  Object   |           |
    y1 box[3]   |-----------|           |
    |                                   |
   H----------x0 box[0]-----x1 box[2]----
    0                                   W
   

3. If the link breaks, please use this Google Drive: https://drive.google.com/drive/folders/1ICBed8F9JaayAshptGMiGtRj78esg3m4?usp=sharing.

External Links

1. The orignal CAFFE implementation https://github.com/peteanderson80/bottom-up-attention, and its docker image.
2. bottom-up-attention.pytorch maintained by MIL-LAB.

Acknowledgement

• original repo airsplay
• The Caffe2PyTorch conversion code (not released here) is based on Ruotian Luo's PyTorch-ResNet project.
• The project also refers to Ross Girshick's old py-faster-rcnn on its way.

References

Detectron2:

@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}
}

Bottom-up Attention:

@inproceedings{Anderson2017up-down,
  author = {Peter Anderson and Xiaodong He and Chris Buehler and Damien Teney and Mark Johnson and Stephen Gould and Lei Zhang},
  title = {Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering},
  booktitle={CVPR},
  year = {2018}
}