Add model-index metadata (HF Evaluation Results widget)

README.md

@@ -26,6 +26,32 @@ metrics:
   - f1
 
 base_model: "Ultralytics/YOLOv9"
+
+model-index:
+  - name: YOLOv9m Finetuned on ExDark
+    results:
+      - task:
+          type: object-detection
+          name: Object Detection
+        dataset:
+          name: ExDark
+          type: exdark
+        metrics:
+          - type: mAP50
+            value: 74.17
+            name: mAP@50 (test split)
+          - type: mAP50-95
+            value: 47.38
+            name: mAP@50-95 (test split)
+          - type: precision
+            value: 76.27
+            name: Precision (test split)
+          - type: recall
+            value: 67.94
+            name: Recall (test split)
+        source:
+          url: https://github.com/dronefreak/DetectionBench
+          name: DetectionBench
 ---
 
 
@@ -89,26 +115,26 @@ Metrics reported in this model card are computed on the ExDark **test** split, u
 
 Every model DetectionBench has trained and evaluated on ExDark so far, for full transparency -- see [DetectionBench](https://github.com/dronefreak/DetectionBench) for the smaller, curated comparison set used on the project README.
 
-| Rank                       | Model                | mAP@50        | mAP@50-95       | Precision         | Recall         |
-| -------------------------- | --------------------- | ------------- | --------------- | ----------------- | -------------- |
-| 1           | RF-DETR Small | 88.98 | 61.67 | 83.07 | 81.89 |
-| 2           | RF-DETR Medium | 88.64 | 62.55 | 86.6 | 79.46 |
-| 3           | RF-DETR Nano | 85.27 | 58.01 | 85.18 | 74.67 |
-| 4           | YOLOv26l | 77.51 | 50.88 | 80.71 | 70.72 |
-| 5           | YOLOv26m | 76.54 | 50.02 | 82.29 | 68.83 |
-| 6           | YOLOv8x | 75.4 | 48.39 | 81.53 | 65.86 |
-| 7           | YOLOv8l | 75.26 | 48.48 | 81.44 | 67.58 |
-| 8           | YOLOv8m | 74.69 | 48.05 | 78.4 | 69.17 |
-| 9           | YOLOv11x | 74.41 | 48.98 | 81.87 | 67.05 |
-| 10           | YOLOv9m | 74.17 | 47.38 | 76.27 | 67.94 |
-| 11           | YOLOv26s | 74.0 | 48.32 | 79.11 | 65.59 |
-| 12           | YOLOv11l | 73.44 | 47.56 | 78.57 | 67.09 |
-| 13           | YOLOv11s | 73.35 | 46.8 | 77.93 | 66.38 |
-| 14           | YOLOv11m | 73.17 | 47.16 | 74.83 | 67.23 |
-| 15           | YOLOv8s | 73.01 | 45.85 | 78.26 | 65.13 |
-| 16           | YOLOv26n | 72.7 | 46.27 | 81.0 | 62.67 |
-| 17           | YOLOv8n | 71.29 | 44.78 | 78.25 | 62.76 |
-| 18           | YOLOv11n | 70.36 | 44.72 | 76.18 | 61.15 |
+| Model                | mAP@50        | mAP@50-95       | Precision         | Recall         |
+| --------------------- | ------------- | --------------- | ----------------- | -------------- |
+| RF-DETR Small | 88.98 | 61.67 | 83.07 | 81.89 |
+| RF-DETR Medium | 88.64 | 62.55 | 86.6 | 79.46 |
+| RF-DETR Nano | 85.27 | 58.01 | 85.18 | 74.67 |
+| YOLOv26l | 77.51 | 50.88 | 80.71 | 70.72 |
+| YOLOv26m | 76.54 | 50.02 | 82.29 | 68.83 |
+| YOLOv8x | 75.4 | 48.39 | 81.53 | 65.86 |
+| YOLOv8l | 75.26 | 48.48 | 81.44 | 67.58 |
+| YOLOv8m | 74.69 | 48.05 | 78.4 | 69.17 |
+| YOLOv11x | 74.41 | 48.98 | 81.87 | 67.05 |
+| YOLOv9m | 74.17 | 47.38 | 76.27 | 67.94 |
+| YOLOv26s | 74.0 | 48.32 | 79.11 | 65.59 |
+| YOLOv11l | 73.44 | 47.56 | 78.57 | 67.09 |
+| YOLOv11s | 73.35 | 46.8 | 77.93 | 66.38 |
+| YOLOv11m | 73.17 | 47.16 | 74.83 | 67.23 |
+| YOLOv8s | 73.01 | 45.85 | 78.26 | 65.13 |
+| YOLOv26n | 72.7 | 46.27 | 81.0 | 62.67 |
+| YOLOv8n | 71.29 | 44.78 | 78.25 | 62.76 |
+| YOLOv11n | 70.36 | 44.72 | 76.18 | 61.15 |
 ---
 
 ## Per-Class Performance
@@ -270,8 +296,8 @@ If you use this model in your research, please consider citing:
 
 1. The ExDark dataset (see below)
 2. The original YOLOv9m architecture (see below)
-3. DetectionBench, the training/evaluation framework used to produce this checkpoint
-
+3. The other model architectures shown in the Model Zoo/External Comparison tables above, if you reference their results
+4. DetectionBench, the training/evaluation framework used to produce this checkpoint
 ```
 @article{Exdark,
   title = {Getting to Know Low-light Images with The Exclusively Dark Dataset},
@@ -292,7 +318,61 @@ If you use this model in your research, please consider citing:
   year={2024}
 }
 ```
+Other architectures compared against on ExDark in this model card:
+### RF-DETR
+
+```bibtex
+@inproceedings{robinson2026rfdetr,
+  title     = {RF-DETR: Real-Time Detection Transformer},
+  author    = {Robinson, Isaac and Robicheaux, Peter and Popov, Matvei and Ramanan, Deva and Peri, Neehar},
+  booktitle = {International Conference on Learning Representations (ICLR)},
+  year      = {2026},
+  url       = {https://arxiv.org/abs/2511.09554}
+}
 
+@article{oquab2023dinov2,
+  title={DINOv2: Learning Robust Visual Features without Supervision},
+  author={Oquab, Maxime and Darcet, Timoth{\'e}e and Moutakanni, Theo and Vo, Huy and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and others},
+  journal={arXiv preprint arXiv:2304.07193},
+  year={2023}
+}
+```
+### YOLOv11
+
+```bibtex
+No official YOLO11 research paper has been published by Ultralytics; the most commonly cited independent architectural analysis is used instead:
+
+@article{khanam2024yolov11,
+  title={YOLOv11: An Overview of the Key Architectural Enhancements},
+  author={Khanam, Rahima and Hussain, Muhammad},
+  journal={arXiv preprint arXiv:2410.17725},
+  year={2024}
+}
+```
+### YOLOv26
+
+```bibtex
+@article{jocher2026yolo26,
+  title={Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models},
+  author={Jocher, Glenn and Qiu, Jing and Liu, Mengyu and Lyu, Shuai and Akyon, Fatih Cagatay and Kalfaoglu, Muhammet Esat},
+  journal={arXiv preprint arXiv:2606.03748},
+  year={2026}
+}
+```
+### YOLOv8
+
+```bibtex
+No official YOLOv8 research paper has been published by Ultralytics; this is their own recommended software citation instead:
+
+@software{jocher2023yolov8,
+  author = {Glenn Jocher and Ayush Chaurasia and Jing Qiu},
+  title = {Ultralytics YOLOv8},
+  version = {8.0.0},
+  year = {2023},
+  url = {https://github.com/ultralytics/ultralytics},
+  license = {AGPL-3.0}
+}
+```
 ```bibtex
 @software{Saksena_DetectionBench_2026,
   author = {Saksena, Saumya Kumaar},