mabogee03
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Aerial-Object-Detection-Model

Object Detection

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mabogee03 commited on Sep 11

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@@ -15,12 +15,6 @@ pipeline_tag: object-detection
 A YOLOv8 object detection model trained to identify **planes, ships, harbours, and vehicles** in aerial imagery, built for UNISA's inaugural Hackathon.
-**Team: Kernel Ninjas**
-Team Lead: Mabo Giqwa
-Teammates: Dr San Sahasra, Siphelele Ndlovu, Sello Serutle, Fortune Mkhwebane
-[Hackathon reference](https://www.unisa.ac.za/sites/myunisa/default/News/Articles/Unisa-hosts-first%E2%80%93ever-Hackathon-to-ignite-student-learning-experience) · [Live demo](https://mabogiqwa.github.io/aerial-ml/) · [Source code](https://github.com/mabogiqwa/aerial-ml)
 ---
 ## Overview
@@ -72,8 +66,8 @@ Evaluated on the validation split (109 images).
 | harbor | 0.516 | 0.174 | 0.260 | 0.198 | 0.098 |
 | small-vehicle | 0.724 | 0.060 | 0.111 | 0.130 | 0.054 |
-![Full evaluation breakdown](images/model_evaluation_comprehensive.png)
-![F1 score per class](images/f1_score_evaluation.png)
 ### Limitations, stated plainly

 A YOLOv8 object detection model trained to identify **planes, ships, harbours, and vehicles** in aerial imagery, built for UNISA's inaugural Hackathon.
 ---
 ## Overview
 | harbor | 0.516 | 0.174 | 0.260 | 0.198 | 0.098 |
 | small-vehicle | 0.724 | 0.060 | 0.111 | 0.130 | 0.054 |
+![Full evaluation breakdown](model_evaluation_comprehensive.png)
+![F1 score per class](f1_score_evaluation.png)
 ### Limitations, stated plainly