diff --git a/README.md b/README.md
index e30600f..fc46d67 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,2 @@
 # Computer-Vision
-Repository to host the teams computer vision model training and testing.
+Repository to host the team's computer vision model training and testing.
diff --git a/test_programs/main.py b/test_programs/main.py
new file mode 100644
index 0000000..8f1baef
--- /dev/null
+++ b/test_programs/main.py
@@ -0,0 +1,75 @@
+import cv2
+import numpy as np
+
+# Load YOLO
+net = cv2.dnn.readNet("yolov4.weights", "yolov4.cfg")
+layer_names = net.getLayerNames()
+output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers().flatten()]
+
+# Loading video
+cap = cv2.VideoCapture('shape_test.mp4')
+
+# Define font
+font = cv2.FONT_HERSHEY_SIMPLEX
+
+# Define the codec and create a VideoWriter object
+fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # or use 'XVID'
+video_out = cv2.VideoWriter('output.mp4', fourcc, 20.0, (416, 416))
+classes = ["circle", "asterisk"]
+
+while(cap.isOpened()):
+    ret, img = cap.read()
+    if not ret:
+        break
+
+    height, width, channels = img.shape
+
+    # Detecting objects
+    blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
+    net.setInput(blob)
+    outs = net.forward(output_layers)
+
+    class_ids = []
+    confidences = []
+    boxes = []
+
+    for out in outs:
+        for detection in out:
+            scores = detection[5:]
+            class_id = np.argmax(scores)
+            confidence = scores[class_id]
+            if confidence > 0.2:  # Lowered confidence threshold
+                # Object detected
+                center_x = int(detection[0] * width)
+                center_y = int(detection[1] * height)
+                w = int(detection[2] * width)
+                h = int(detection[3] * height)
+
+                # Rectangle coordinates
+                x = int(center_x - w / 2)
+                y = int(center_y - h / 2)
+
+                boxes.append([x, y, w, h])
+                confidences.append(float(confidence))
+                class_ids.append(class_id)
+
+    indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
+
+    for i in range(len(boxes)):
+        if i in indexes:
+            x, y, w, h = boxes[i]
+            label = str(classes[class_ids[i]])
+            cv2.rectangle(img, (x, y), (x + w, y + h), (0,255,0), 2)
+            cv2.putText(img, label, (x, y + 30), font, 2, (0,255,0), 3)
+
+    # Write the frame into the file 'output.mp4'
+    video_out.write(img)
+
+    # cv2.imshow("Image", img)  # Commented out this line
+    key = cv2.waitKey(1)
+    if key == 27:
+        break
+
+cap.release()
+video_out.release()  # Save the output
+cv2.destroyAllWindows()
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diff --git a/test_programs/output.mp4 b/test_programs/output.mp4
new file mode 100644
index 0000000..c149548
Binary files /dev/null and b/test_programs/output.mp4 differ
diff --git a/test_programs/shape_test.mp4 b/test_programs/shape_test.mp4
new file mode 100644
index 0000000..baf006d
Binary files /dev/null and b/test_programs/shape_test.mp4 differ
diff --git a/test_programs/test.py b/test_programs/test.py
new file mode 100644
index 0000000..aa265d1
--- /dev/null
+++ b/test_programs/test.py
@@ -0,0 +1,38 @@
+import sys
+
+from expttools import Experiment
+
+class NamedObject():
+    def __init__(self,name,value):
+        '''
+        wrap value in an object with name
+
+        Parameters
+        -----------------
+        name : string
+            name of object
+        value : any
+             object to store
+        '''
+        self.name = name
+        self.value = value
+
+def my_expt_function(**kwargs):
+    for key, value in kwargs.items():
+        print(f"{key}: {value}")
+
+# Define a dictionary with parameters and their values
+param_grid = {
+    "param1": [1, 2, 3],
+    "param2": ["a", "b", "c"],
+    "param3": [True, False],
+    "param4": [NamedObject("obj1", 1), NamedObject("obj2", 2)]
+}
+
+for param, values in param_grid.items():
+    for value in values:
+        my_expt_function(**{param: value})
+        
+
+
+curr_experiment = Experiment(my_expt_function, param_grid)
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