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 # COHI-O365: A Benchmark Dataset for Fisheye Object Detection
 
-**Repository:** [https://github.com/IS2AI/COHI-O365](https://github.com/IS2AI/COHI-O365)
+## Dataset Summary
 
+This work introduces COHI-O365, a benchmark dataset for object detection in hemispherical/fisheye images, designed for field-of-view invariant applications.  It complements a synthetic training dataset, RMFV365, created by applying fisheye transformations to the Objects365 dataset.  COHI-O365 contains 1,000 real fisheye images with 74 classes and an average of 20,798 object instances per image.  These images were captured using an ELP-USB8MP02G-L180 hemispherical camera (2448x3264 pixels) and manually annotated with axis-aligned bounding boxes.  The RMFV365 dataset, used for model training, comprises 5.1 million fisheye images generated from Objects365.  YOLOv7 models were trained on Objects365, RMFV365, and a variant (RMFV365-v1), and evaluated on COHI-O365.
 
-This repository introduces COHI-O365, a benchmark dataset for object detection in hemispherical/fisheye images, designed for field-of-view invariant applications.  It also includes the RMFV365 dataset, a large-scale synthetic fisheye dataset used for training.  Pre-trained YOLOv7 models are provided, trained on various combinations of these datasets.
 
+## Dataset Contents
 
-## Dataset Summary
+The dataset includes:
+
+* **COHI-O365:** A benchmark testing dataset with 1,000 real fisheye images of 74 classes.
+* **RMFV365:** A large-scale synthetic fisheye dataset derived from Objects365, containing 5.1 million images.
+
+A visualization of sample images from both datasets is provided in the GitHub repository.  A table detailing the number of bounding boxes per class in COHI-O365 is planned for future inclusion.
+
+
+## Benchmarks
+
+YOLOv7 models were trained on different datasets and evaluated on COHI-O365. The results are summarized below:
+
+| S/N | Model       | Objects365 mAP50 | Objects365 mAP50:95 | RMFV365-v1 mAP50 | RMFV365-v1 mAP50:95 | RMFV365 mAP50 | RMFV365 mAP50:95 | COHI-365 mAP50 | COHI-365 mAP50:95 |
+|-----|-------------|--------------------|----------------------|--------------------|---------------------|-----------------|--------------------|-----------------|--------------------|
+| 1   | FPN         | **35.5**           | 22.5                  | N/A                | N/A                 | N/A             | N/A                | N/A             | N/A                |
+| 2   | RetinaNet   | 27.3               | 18.7                  | N/A                | N/A                 | N/A             | N/A                | N/A             | N/A                |
+| 3   | YOLOv5m     | 27.3               | 18.8                  | 22.6               | 14.1                | 18.7            | 10.1               | 40.4            | 28.0               |
+| 4   | YOLOv7-0    | 34.97              | **24.57**             | 29.1               | 18.3                | 24.2            | 13.0               | 47.5            | 33.5               |
+| 5   | YOLOv7-T1   | 34.3               | 24.0                  | 32.7               | 22.7                | 32.0            | 22.0               | 49.1            | 34.6               |
+| 6   | YOLOv7-T2   | 34                 | 23.1                  | **32.9**           | **23**               | **33**          | **22.8**           | **49.9**        | **34.9**           |
+
+**Table:** Object recognition results on Objects365, RMFV365-v1, RMFV365, and COHI-365 testing sets.  Bold values represent the best performance within each column.
+
+
+## Citation
+
+*(Citation information to be added)*
 
-COHI-O365 is a real-world dataset containing 1,000 fisheye images of 74 classes, sampled from the Objects365 dataset.  These images, captured using an ELP-USB8MP02G-L180 hemispherical camera (2448x3264 resolution), feature an average of 20,798 object instances per image and are annotated with axis-aligned bounding boxes.
-
-
-RMFV365 is a synthetic fisheye dataset created by applying non-linear mapping to the Objects365 dataset.  It comprises 5.1 million images offering a diverse range of perspectives and distortions, useful for training robust object detection models.
-
-
-
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/67056b2e6409e548690b1b6f/3v4VU8qDxSczHCFHIz9bj.png)
-
-*COHI-O365 Sample Images*
-
-
-
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/67056b2e6409e548690b1b6f/Sww7NkXYVnEiXgc4NQyr6.png)
-
-*RMFV365 Sample Images*
-
-
-## Pre-trained Models and Results
-
-Three YOLOv7 models were trained and evaluated:
-
-* **YOLOv7-0:** Trained on Objects365.
-* **YOLOv7-T1:** Trained on Objects365 and a variant of RMFV365 (RMFV365-v1), using a lens and camera-independent fisheye transformation (n=4).
-* **YOLOv7-T2:** Trained on RMFV365.
-
-
-
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/67056b2e6409e548690b1b6f/JMJCqaGedoORAURGclSgP.png)
-
-*Model Training Scheme*
-
-
-The models' performance, measured by mAP<sub>50</sub>, is summarized below:
-
-<table>
-    <thead>
-        <tr>
-            <th rowspan="3">S/N</th>
-            <th rowspan="3">Model</th>
-            <th colspan="8">Test Results (%)</th>
-        </tr>
-        <tr>
-            <th colspan="2">Objects365</th>
-            <th colspan="2">RMFV365-v1</th>
-            <th colspan="2">RMFV365</th>
-            <th colspan="2">COHI-365</th>
-        </tr>
-        <tr>
-            <th>mAP50</th>
-            <th>mAP50:95</th>
-            <th>mAP50</th>
-            <th>mAP50:95</th>
-            <th>mAP50</th>
-            <th>mAP50:95</th>
-            <th>mAP50</th>
-            <th>mAP50:95</th>
-        </tr>
-    </thead>
-    <tbody>
-        <tr>
-            <td>1</td>
-            <td>FPN</td>
-            <td><strong>35.5</strong></td>
-            <td>22.5</td>
-            <td>N/A</td>
-            <td>N/A</td>
-            <td>N/A</td>
-            <td>N/A</td>
-            <td>N/A</td>
-            <td>N/A</td>
-        </tr>
-        <tr>
-            <td>2</td>
-            <td>RetinaNet</td>
-            <td>27.3</td>
-            <td>18.7</td>
-            <td>N/A</td>
-            <td>N/A</td>
-            <td>N/A</td>
-            <td>N/A</td>
-            <td>N/A</td>
-            <td>N/A</td>
-        </tr>
-        <tr>
-            <td>3</td>
-            <td>YOLOv5m</td>
-            <td>27.3</td>
-            <td>18.8</td>
-            <td>22.6</td>
-            <td>14.1</td>
-            <td>18.7</td>
-            <td>10.1</td>
-            <td>40.4</td>
-            <td>28.0</td>
-        </tr>
-        <tr>
-            <td>4</td>
-            <td>YOLOv7-0</td>
-            <td>34.97</td>
-            <td><strong>24.57</strong></td>
-            <td>29.1</td>
-            <td>18.3</td>
-            <td>24.2</td>
-            <td>13.0</td>
-            <td>47.5</td>
-            <td>33.5</td>
-        </tr>
-        <tr>
-            <td>5</td>
-            <td>YOLOv7-T1</td>
-            <td>34.3</td>
-            <td>24.0</td>
-            <td>32.7</td>
-            <td>22.7</td>
-            <td>32.0</td>
-            <td>22.0</td>
-            <td>49.1</td>
-            <td>34.6</td>
-        </tr>
-        <tr>
-            <td>6</td>
-            <td>YOLOv7-T2</td>
-            <td>34</td>
-            <td>23.1</td>
-            <td><strong>32.9</strong></td>
-            <td><strong>23</strong></td>
-            <td><strong>33</strong></td>
-            <td><strong>22.8</strong></td>
-            <td><strong>49.9</strong></td>
-            <td><strong>34.9</strong></td>
-        </tr>
-    </tbody>
-</table>
-
-**Table:** Objects Recognition Results on Objects365, RMFV365-v1, RMFV365, and COHI-365 Testing Sets
 
+## GitHub Repository
 
+[https://github.com/IS2AI/COHI-O365](https://github.com/IS2AI/COHI-O365)