[ODGI] Object Detection with Grouped Instances

This is a Tensorflow implementation for ODGI: Object Detection with Grouped Instances.

The library requirements are:

• Tensorflow (1.4)
• Python (3.5)
• Numpy

Model

Current state-of-the-art detection systems often suffer of two important shortcomings: processing speed and detecting objects at varying scales. In this project, we propose ODGI (Object Detection with Grouped Instances) a new detection scheme that addresses these issues; The main idea is to allow the detector to predict groups of objects rather than individuals, when it is needed. The proposed model allows working at lower resolution, thereby saving computations, and that the ability to identify groups leads to fewer, yet more meaningful, regions proposal than existing methods.

Datasets

We originally experimented on the VEDAI dataset and the Stanford Drone Dataset. For each dataset, we provide, in the Data directory:

• {}_{train, test} are TFRecords containing the dataset. Each example has features im_id (an image id that we use to resolve the path of the corresponding image), num_boxes (number of valid bounding boxes), bounding_boxes and classes.
• metadata_{} contains paths to the train and test TFRecords for this dataset, path to the main image folder and other information (number of samples, etc.)
• {}_split{train, test} contains the image IDS for the train and test split we used in our experiments

Additionally you'll need to have the images stored in some image_folder that you can modify in metadata_{}. See the notebook input_pipeline.ipynb for how the images and annotations TFRecords are generated.

See the notebook create_dataset.py for how these datasets were created.

Train the model

We provide scripts train_standard.py to train and evaluate a standard tiny-yolov2 model, and train_odgi.py to train and evaluate a two-stage ODGI pipeline. Each notebook contains a configuration set-up and build the Tensorflow graph for both training and evaluation using functions defined in net.py, loss_utils.py and eval_utils.py. The default configuration options and short descriptions can be found in defaults.py.

Most of the training process can be monitored via Tensorboard (the default output directory is ./log). In particular we output the following summaries:

• [text] config_summary contains all configuration options for the current run.
• [scalars] We report the training losses (train_tinyyolov2 for standard and train_stage1 and train_stage2 for ODGI). We also report running evaluation metrics. In particular the final detection metrics for both models are respectively eval/tinyyolov2_avgprec_* for standard and eval/stage2_avgprec_* for ODGI.
• [images] Image summaries contain
  • image inputs (vizualized with ground-truth bounding boxes, and empty cells at lower opacity),
  • during training it contains the predicted boxes assigned to the ground-truth (before and after rescaling in the ODGI setting)
  • the output bounding boxes above a certain confidence threhsold (default is 0.5)
  • extracted crops after intermediate ODGI stages
  • group flag confusion matrix

Launch a pre-trained model

load_and_eval is a small example of how to load a pretrained model (ODGI or standard) and compute detection metrics on a given dataset as well as output the resulting images.

Citation

If you found this code useful please cite

		        author = {Royer, Am\'{e}lie and Lampert, Christoph H.},
		        title = {Localizing Grouped Instances for Efficient Detection in Low-Resource Scenarios},
		        journal = {Winter Conference on Applications of Computer Vision (WACV)},
		        year = {2020}
		      }