Update README.md

README.md

@@ -10,4 +10,34 @@ pinned: false
 license: mit
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-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Stamp Detection using YoloV9 
+
+This repository shows how to custom train your model using YOLOV9.
+
+The script above uses a football players dataset to implement the process of custom training due to confidentiality with respect to the stamp dataset (PII information).
+However the same script can be used traing using custom dataset.
+
+## Steps
+
+1.) Firstly we will install labelme using python pip. Labelme is a graphical image annotation tool 
+  * pip install labelme
+    
+2.) Load your image directory in labelme and get to annotating
+
+3.) Now once annotation is complete, install labelme2yolo to convert your labelme annotation to yolov9 format
+  * labelme2yolo --json_dir /path/to/labelme_json_dir/ --val_size 0.15 --test_size 0.15
+    
+4.) Once done you can execute above script by replacing dataset with your custom path
+
+## Training Performance
+The model was trained for 24 epochs on the stamp dataset. The training logs show the following metrics:
+<img width="1161" alt="Screenshot 2024-05-17 at 8 56 30 PM" src="https://github.com/Himank-J/ERAV2/assets/55919214/dac6c631-fcf1-4931-a9d1-11e6f70a31b9">
+
+As you can see from above training logs:
+* Precision is 0.85
+* Recall is 0.67
+* mAp is 0.76
+
+<img width="1013" alt="Screenshot 2024-05-12 at 1 49 36 PM" src="https://github.com/Himank-J/ERAV2/assets/55919214/f44a98f2-77b8-4168-a52b-25292afec069">
+
+The above model is also available on huggingface for testing - https://huggingface.co/spaces/HimankJ/StampDetection