Update README.md

README.md

@@ -1,4 +1,3 @@
- 
 ---
 tags:
 - yolov5
@@ -9,29 +8,26 @@ tags:
 library_name: yolov5
 library_version: 7.0.6
 inference: false
-
 datasets:
 - keremberke/license-plate-object-detection
-
+- mrhacker7599/ANPR
 model-index:
 - name: keremberke/yolov5n-license-plate
   results:
   - task:
       type: object-detection
-
     dataset:
       type: keremberke/license-plate-object-detection
       name: keremberke/license-plate-object-detection
       split: validation
-
     metrics:
-      - type: precision  # since mAP@0.5 is not available on hf.co/metrics
-        value: 0.9783431294995892  # min: 0.0 - max: 1.0
-        name: mAP@0.5
+    - type: precision
+      value: 0.9783431294995892
+      name: mAP@0.5
 ---
 
 <div align="center">
-  <img width="640" alt="keremberke/yolov5n-license-plate" src="https://huggingface.co/keremberke/yolov5n-license-plate/resolve/main/sample_visuals.jpg">
+  <img width="640" alt="mrhacker7599/yolov5n-license-plate" src="https://huggingface.co/mrhacker7599/yolov5n-license-plate/resolve/main/sample_visuals.jpg">
 </div>
 
 ### How to use
@@ -48,7 +44,7 @@ pip install -U yolov5
 import yolov5
 
 # load model
-model = yolov5.load('keremberke/yolov5n-license-plate')
+model = yolov5.load('mrhacker7599/yolov5n-license-plate')
   
 # set model parameters
 model.conf = 0.25  # NMS confidence threshold
@@ -82,7 +78,7 @@ results.save(save_dir='results/')
 - Finetune the model on your custom dataset:
 
 ```bash
-yolov5 train --data data.yaml --img 640 --batch 16 --weights keremberke/yolov5n-license-plate --epochs 10
+yolov5 train --data data.yaml --img 640 --batch 16 --weights mrhacker7599/yolov5n-license-plate --epochs 10
 ```
 
 **More models available at: [awesome-yolov5-models](https://github.com/keremberke/awesome-yolov5-models)**