# The large-scale retail scenario object detection and counting task (Locount)
# Locount: A large-scale retail scenario object detection and counting task
[Rethinking Object Detection in Retail Stores](http://arxiv.org/abs/2003.08230).
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## Evaluation protocol
To fairly compare algorithms on the *Locount* task, we design a new evaluation protocol, which penalizes algorithms for missing object instances, for duplicate detections of one instance, for false positive detections, and for false counting numbers of detections. Inspired by MS COCO protocol, we design new metrics *AP^{lc}*, *AP_{0.5}^{lc}*, *AP_{0.75}^{lc}*, and *AR^{lc}_{max}=150}* to evaluate the performance of methods, which takes both the localization and counting accuracies into account. Specifically, a correct detection should satisfied two criteria, (1) the localization intersection over union, $\text{IoU} = \frac{\widehat{B} \cap B^\ast}{ \widehat{B} \cup B^\ast}$, between the predicted bounding box $\widehat{B}$ and the ground-truth bounding box $B^\ast$ is larger than the threshold $\theta_{\text{l}}$, \ie, $\text{IoU} \geq \theta_{\text{l}}$; and (2) the counting accuracy, $\text{AC} = \max\big(0, 1-\frac{|\widehat{C} - C^\ast|}{C^\ast} \big)$, between the predicted instance number enclosed in the predicted bounding box $\widehat{C}$ and the ground-truth instance number $C^\ast$ is larger than the threshold $\theta_{\text{c}}$, \ie, $\text{AC} \geq \theta_{\text{c}}$. After that, AP$^{\it lc}$ is computed by averaging over all $10$ IoU thresholds, \ie, $\theta_{\text{l}} \in [0.50, 0.95]$ with the uniform step size $0.05$, and $10$ AC thresholds, \ie, $\theta_{\text{c}} \in [0.50, 0.95]$ with the uniform step size $0.05$, of all categories, which is used as the primary metric for ranking algorithms. Note that, to indicate the localization accuracy, we also report the results of evaluated methods using the MS COCO protocol for reference.
To fairly compare algorithms on the *Locount* task, we design a new evaluation protocol, which penalizes algorithms for missing object instances,
for duplicate detections of one instance, for false positive detections, and for false counting numbers of detections.
Inspired by MS COCO protocol, we design new metrics *AP^{lc}*, *AP_{0.5}^{lc}*, *AP_{0.75}^{lc}*, and *AR^{lc}_{max}=150}* to evaluate the performance of methods,
which takes both the localization and counting accuracies into account. For more detailed definitions, please refer to the [paper](http://arxiv.org/abs/2003.08230).
