edited README.md

README.md

@@ -65,6 +65,14 @@ df = pd.read_pickle("pmfeed_4_3_16_bboxes_and_labels.pkl")
 ```
 
 ## Usage
+### Dataset Download:
+
+Step 1: install git-lfs:
+`git lfs install`
+
+Step 2: 
+`git clone [email protected]:datasets/tonyFang04/8-calves`
+
 ### Object Detection
 - **Training/Validation**: Use the first 600 frames from `hand_labelled_frames/` (chronological split).  
 - **Testing**: Evaluate on the full video (`pmfeed_4_3_16.avi`) using the provided PKL annotations.  
@@ -77,6 +85,34 @@ df = pd.read_pickle("pmfeed_4_3_16_bboxes_and_labels.pkl")
 | Validation | 100    | Hyperparameter tuning |  
 | Test       | 67,760 | Final evaluation |  
 
+### Benchmarking YOLO Models:
+Step 1: install albumentations. Check out [Albumentations' website](https://www.albumentations.ai/docs/) for more information.
+
+Step 2: 
+`cd 8-calves/object_detector_benchmark`. Run
+`./create_yolo_dataset.sh` and
+`create_yolo_testset.py`. This creates a YOLO dataset with the 500/100/67760 train/val/test split recommended above.
+
+Step 3: install ultralytics. Check out [Ultralytics's website](https://github.com/ultralytics/ultralytics) for more information. Find the `Albumentations` class in the `data/augment.py` file in ultralytics source code. And replace the default transforms to:
+
+```
+# Transforms
+T = [
+    A.RandomRotate90(p=1.0),
+    A.HorizontalFlip(p=0.5),
+    A.RandomBrightnessContrast(p=0.4),
+    A.ElasticTransform(
+        alpha=100.0, 
+        sigma=5.0, 
+        p=0.5
+    ),
+]
+```
+
+Step 4:
+run the yolo detectors following ultralytic's documentations.
+
+
 ### Identity Classification
 - Use `tracklet_id` (1-8) from the PKL file as labels.  
 - **Temporal Split**: 30% train / 30% val / 40% test (chronological order).