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The RepCount dataset, introduced in TransRAC, comprises 1451 videos with approximately 20,000 fine-grained annotations. It includes videos of varying lengths and multiple anomaly cases, with each video annotated for the beginning and end of each action period. The average video length in the dataset is 39.35 seconds, significantly surpassing the durations of UCFRep and CountixAV. On average, each video clip in the dataset contains about 16 action cycles. The RepCount dataset is divided into two parts, Part-A and Part-B. As Part-B is currently unavailable, our model is trained on the training set of Part-A and evaluated on its corresponding test set. Countix-AV, a subset of Countix, is a dataset sourced from YouTube. It consists of 1863 videos, with 987, 311, and 565 videos allocated for training, validation, and testing, respectively. Since each video only annotates the count of repeated actions, which does not meet our task requirements, we manually annotate the start and end frames of the first action cycle to align with our framework. UCFRep, a subset of UCF101, is a dataset that selectively includes 23 action categories out of the original 101, focusing on cyclically performed actions. The dataset contains a total of 526 videos, with 421 videos assigned to the training set and 105 videos designated as the validation set. Each video in UCFRep is annotated with the temporal boundaries of each repetitive action, making it directly applicable to our task. QUVA, as described in Real-worldrepetitionestimation, comprises 100 videos exhibiting various activity ranges. Due to its relatively small scale, the QUVA dataset is commonly employed for evaluation purposes. All videos in the QUVA dataset are annotated with the temporal bounds of each interval, enabling us to assess the generalizability of our method.