[17], in which authors proposed to detect objects as center, top-left and bottom-right points. ExtremeNet is presented by [PERSON] et al. [18]. ExtremeNet detects objects by identifying a single center point and four extreme points in different directions. [PERSON] et al. [14] also proposed a method to detect object by its center point, they therefore named its algorithm CenterNet. In recent years, attention architecture has become popular in deep learning tasks, which is also used by many new proposed object detection algorithms. Such methods have proved to be useful for refining and emphasizing informative features. [PERSON] et al. [19] proposed a method to enhance the spatial features using the mask module. This module is employed with a trunk branch consisting of bottom-up and top-down feedforward structure. [PERSON] et al. [8] introduced SENet module for calculating channel-wise weights of a convolutional layer to capture channel-wise responses. [PERSON] et al. [20] proposed ECANetblock to model channel-wise features more effectively and efficiently. [PERSON] et al. [21] proposed an attention mechanism network to classify and localize liver lesions on CT images. [PERSON] et al. [22] proposed to use channel and spatial attention blocks within the convolutional block. [PERSON] et al. [23] proposed a network for learning spatial information using the attention mechanism. They added the calculated attention weights to the output of the classification layer. Normally, training of deep learning-based models follows a supervised learning scheme and relies on large annotated training datasets. A deep learning model suffers performance degradation due to domain shift (source-to-target domain) during inference time [24; 25]. In practice, such performance degradation limits the scalability and applicability of deep learning-based models. On the other hand, fine-tuning a model on new domain might face the problem of lack of training data because object-level annotation is basically a time-consuming and labor-intensive task. Training a well-generalized model which is able to be applied to different domains is a hot research topic today. As a result, recently, several domain adaptation-based methods have been proposed to learn model from one domain and generalize well to another domain [24; 25; 26; 27]. Inspired by the existing adversarial learning-based domain adaptation method [25; 26] which has been developed primarily for segmentation applications, in this work, we present a domain adaptation method for logo detection using adversarial learning to mitigate errors caused by domain shift. We have used annotated logo images (source-domain) and unlabeled logo images (target-domain) for training to bring closer these source and target domains. The added discriminator-based network can be learned into an end-to-end manner like a normal detector. Since anchor-free detectors train the network to learn objects in terms of some keypoints, we propose to use mid-level output feature maps instead of class-wise heatmaps to align the distribution of target and source domains. Our adversarial learning approach is motivated by the fact that the use of mid-level outputs benefits from robust information about the domain while retaining object-level information. This method can be easily adapted to other anchor-free detectors.