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Evaluating Large-Vocabulary Detectors: The Devil is in the Details

Authors: Achal Dave, Piotr Dollar, Deva Ramanan, Alex Kirillov, Ross Girshick

This repository contains code to evaluate large-vocabulary detectors on the LVIS dataset with our 'Fixed AP' and 'Pooled AP' metrics, as defined in our paper.

Installation

  1. Clone this repository.
  2. Install locally: 
    pip install -e .

Fixed AP evaluation

Fixed AP evalutes the top K detections for each category (K=10,000 for LVIS), without limiting the number of detections per image. To evaluate a model's outputs using Fixed AP, use the scripts/evaluate_ap_fixed.py script.

python scripts/evaluate_ap_fixed.py \
/path/to/lvis_v1_val.json \
/path/to/lvis_results.json \
/path/to/output_dir

Ensuring K detections per class

Since the standard inference procedure for detectors will result in far fewer than 10,000 detections for most classes, Fixed AP will be artificially low. We provide a modified detectron2 inference script which ensures that models output 10,000 detections for each class:

python lvdevil/infer_topk.py \
--config-file /path/to/detectron2/config.yaml \
MODEL.WEIGHTS /path/to/detectron2/model_final.pth \
OUTPUT_DIR /path/to/output_dir/

This script has been tested with Mask R-CNN and Cascade R-CNN models and detectron2 v0.3. To run this, you'll need to have detectron2 set up.

AP-Pool Evaluation

To evaluate using pooled AP, run the following command. Note that AP-pool similarly uses a limit of 10,000 detections per class, with no limit on detections per image.

python scripts/evaluate_ap_pooled.py \
/path/to/lvis_v1_val.json \
/path/to/lvis_results.json \
/path/to/output_dir

Calibration

To calibrate models, first run inference using lvdevil/infer_topk.py on LVIS train and validation sets. Then, use one of the configs in ./configs/calib/ as follows:

python scripts/train_calibration.py \
./configs/calib/platt.yaml \
--output-dir /path/to/output_dir/ \
--opts \
TRAIN.RESULTS /path/to/train_results.json \
TEST.RESULTS /path/to/test_results.json \
TRAIN.ANNOTATIONS /path/to/lvis_v1_train.json \
TEST.ANNOTATIONS /path/to/lvis_v1_val.json

To calibrate on the test set (as an oracle analysis), use the following command

python scripts/train_calibration.py \
./configs/calib/platt.yaml \
--output-dir /path/to/output_dir/ \
--opts \
TRAIN.TRAIN_SET test \
TEST.RESULTS /path/to/test_results.json \
TEST.ANNOTATIONS /path/to/lvis_v1_val.json