add metrics

README.md

@@ -1,3 +1,38 @@
+---
+license: mit
+tags:
+- yolov8n
+- object-detection
+- computer-vision
+- rubber-duck-detection
+- hackathon
+library_name: yolov8
+base_model: ultralytics/yolov8n
+datasets:
+- Norod78/Rubber-Duck-blip-captions
+- linoyts/rubber_ducks
+model-index:
+- name: YOLOv8n Rubber Duck Detection
+  results:
+  - task:
+      type: object-detection
+    dataset:
+      name: Custom Rubber Duck Dataset
+      type: custom
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.523
+    - name: Recall
+      type: recall
+      value: 0.638
+    - name: mAP50
+      type: mAP
+      value: 0.598
+language:
+- en
+---
+
 # Model Card for YOLOv8n Rubber Duck Detection
 
 This model is a fine-tuned version of YOLOv8n specifically optimized for rubber duck detection. It was developed after inspiration to improve rubber duck detection on a course setup for the [HackerBot Industries HB 0x01 hackathon](https://www.hackerbot.co/) with the specific goal of detecting coordinates for rubber ducks in live video feeds. 
@@ -109,5 +144,4 @@ Carbon Emitted: 0.5 kg of CO2eq
   - Model training
 
 ## Model Card Contact
-For more information about this model, please contact the Brain Wave Collective. You may reach [Daniel by email](https://brainwavecollective.ai/~email-daniel?utm_source=hf&utm_campaign=duckcard) or use the [website form](https://brainwavecollective.ai/~form?utm_source=hf&utm_campaign=duckcard).
-
+For more information about this model, please contact the Brain Wave Collective. You may reach [Daniel by email](https://brainwavecollective.ai/~email-daniel?utm_source=hf&utm_campaign=duckcard) or use the [website form](https://brainwavecollective.ai/~form?utm_source=hf&utm_campaign=duckcard).