LSKA module structure diagram. by Xiaozhou Feng (2918222)

“…Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. …”

FarsterBlock structure. by Xiaozhou Feng (2918222)

“…Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. …”

Sample augmentation and annotation illustration. by Xiaozhou Feng (2918222)

“…Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. …”

YOLOv8 model architecture diagram. by Xiaozhou Feng (2918222)

“…Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. …”

FLMP-YOLOv8 architecture diagram. by Xiaozhou Feng (2918222)

“…Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. …”

Scores vs Skip ratios on single-agent task. by Hongjie Zhang (136127)

“…The inferences reduction significantly decreases the time and FLOPs required by the <i>LazyAct</i> algorithm to complete tasks. Code is available here <a href="https://www.dropbox.com/scl/fo/wyoqo6q9gyt86zobfgbvx/h?…”

Time(s) and GFLOPs savings of single-agent tasks. by Hongjie Zhang (136127)

“…The inferences reduction significantly decreases the time and FLOPs required by the <i>LazyAct</i> algorithm to complete tasks. Code is available here <a href="https://www.dropbox.com/scl/fo/wyoqo6q9gyt86zobfgbvx/h?…”

Win rate vs Skip ratios on multi-agents tasks. by Hongjie Zhang (136127)

“…The inferences reduction significantly decreases the time and FLOPs required by the <i>LazyAct</i> algorithm to complete tasks. Code is available here <a href="https://www.dropbox.com/scl/fo/wyoqo6q9gyt86zobfgbvx/h?…”

Visualization on SMAC-25m based on <i>LazyAct</i>. by Hongjie Zhang (136127)

“…The inferences reduction significantly decreases the time and FLOPs required by the <i>LazyAct</i> algorithm to complete tasks. Code is available here <a href="https://www.dropbox.com/scl/fo/wyoqo6q9gyt86zobfgbvx/h?…”

Single agent and multi-agents tasks for <i>LazyAct</i>. by Hongjie Zhang (136127)

“…The inferences reduction significantly decreases the time and FLOPs required by the <i>LazyAct</i> algorithm to complete tasks. Code is available here <a href="https://www.dropbox.com/scl/fo/wyoqo6q9gyt86zobfgbvx/h?…”

Network architectures for multi-agents task. by Hongjie Zhang (136127)

“…The inferences reduction significantly decreases the time and FLOPs required by the <i>LazyAct</i> algorithm to complete tasks. Code is available here <a href="https://www.dropbox.com/scl/fo/wyoqo6q9gyt86zobfgbvx/h?…”

A collection of visual features and their extremes. by Hector Torres (11708207)

“…<p>This figure illustrates different visual elements assessed by the M.E.D.V.I.S. algorithm, along with examples representing the two ends of each spectrum. …”

Performance evaluation of SpaVGN on melanoma ST dataset. by Haiyan Wang (25821)

Various metrics of the training model. by Lai Yingdong (22403854)

“…The proposed YOLOv8 model achieved a mean Average Precision (mAP@50) of 0.953, mAP@50–95 of 0.678, precision of 0.91, and recall of 0.88. …”

The window installation progress description. by Lai Yingdong (22403854)

“…The proposed YOLOv8 model achieved a mean Average Precision (mAP@50) of 0.953, mAP@50–95 of 0.678, precision of 0.91, and recall of 0.88. …”

Research Flowchart. by Lai Yingdong (22403854)

“…The proposed YOLOv8 model achieved a mean Average Precision (mAP@50) of 0.953, mAP@50–95 of 0.678, precision of 0.91, and recall of 0.88. …”

Results.cxv. by Lai Yingdong (22403854)

“…The proposed YOLOv8 model achieved a mean Average Precision (mAP@50) of 0.953, mAP@50–95 of 0.678, precision of 0.91, and recall of 0.88. …”

Precision Recall data. by Lai Yingdong (22403854)

“…The proposed YOLOv8 model achieved a mean Average Precision (mAP@50) of 0.953, mAP@50–95 of 0.678, precision of 0.91, and recall of 0.88. …”

The score of precision and recall. by Lai Yingdong (22403854)

“…The proposed YOLOv8 model achieved a mean Average Precision (mAP@50) of 0.953, mAP@50–95 of 0.678, precision of 0.91, and recall of 0.88. …”

Installation Process of a Window. by Lai Yingdong (22403854)

“…The proposed YOLOv8 model achieved a mean Average Precision (mAP@50) of 0.953, mAP@50–95 of 0.678, precision of 0.91, and recall of 0.88. …”