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EZMode

This is the official project page for "Active Search for Rare Examples via Proximity-based Selection". EZMode is an iterative algorithm for selecting rare events in large, unlabeled datasets

Requirements

Install the requitements with pip install -r requirements.txt

Reproducing Experiments

To reproduce experiments on the ImageNet-VID and YouTube-BB datasets, run examples/im-vid.py and examples/youtube.py.

For each dataset, you will need to do the following

  1. Organize the dataset in the by videos (see below)
  2. Create a CSV storing annotations
  3. Create a CSV storing labels

We have provided the annotation and label CSVs for the ImageNet-VID and YouTube-BB datasets at this link. Please download and unzip this file to access the annotation and label CSVs for ImageNet-VID and Youtube-BB. To run EZMode on other labeled datasets, please replicate this format.

Dataset Organization

You must organize the dataset as follows:

  1. Create a directory to exclusively store the dataset. We will call this the dataset_root
  2. For every video in the dataset, create a directory in dataset_root.
  3. Store every frame in the corresponding video directory in chronological order.
├── dataset_root	   #directory specific to each dataset
│   ├── video_1		   #directory for every video
│   └── video_2
	├── 0001.jpg	   #frames of each video
	├── 0002.jpg
	├── 0003.jpg
	├── ...
|	...
│   └── video_n

Config

Hyperparameters and options that you can customize when running the EZModeEnd2End engine.

  • db: Path to project database
  • init_db: Boolean that determines whether to initialize the database if it does not exist
  • annotations_csv: Path to annotations CSV (see examples for desired format)
  • labels_csv: Path to labels CSV (see examples for desired format)
  • num_pos_train_seed: Number of positive examples in seed training set
  • num_pos_neg_seed: Number of negative examples in seed training set
  • rare_class: Index of rare class to select
  • root: Path to dataset (same as dataset root above)
  • working_dir: Path to write outputs (models, scores, etc.)
  • project_name: Project name (i.e. "im-vid", or "youtube")
  • select_to_recall: Boolean that determines whether to run EZMode until recall target is achieved
  • target_recall: Target recall to run to if select_to_recall set to True
  • num_rounds: Number of rounds to run (only used select_to_recall set to False)
  • select_per_round: Maximum number of oracle calls per round
  • lr: Learning rate
  • nb_epochs: Number of training epochs
  • train_batch_size: Batch size for training
  • infer_batch_size: Batch size for inference
  • gpus: Number of GPUs on your machine
  • agg_every_n: When generating the rank ordering, we select the max-scoring frame across every-n frames in a given video.
  • prox: Boolean that determines whether to perform proximity sampling
  • prox_rad: Proximity radius
  • cluster: Boolean that determines whether to perform training on easiest examples from each cluster
  • cluster_p: Upper percentile of examples by confidence to add back to the training set