i’m a newbie to object detection, we wrote a script to identify gender and age of a person using the deepface library and then adds trackers using the Sort library.

The code works well on the CPU and Google Collabs but when i run it on the GPU (RTX 3050) it raises this error:

Error processing frame: {{function_node _wrapped__AddV2_device/job:localhost/replica:0/task:0/device:GPU:0}} failed to allocate memory [Op:AddV2]

I tried setting the experimental.set_memory_growth to True, but it still raised the same error. Task manager showed that the GPU wasn’t being used a 100%, and oddly enough whenever the error occurs the GPU usage goes down to 0%.

Can my GPU not run object detection of this magnitude ? Are there any optimizations that i must employ ?

Source Code:

results = DeepFace.analyze(resized_frame, actions=['gender', 'age'], enforce_detection=True,
                                   detector_backend='retinaface')

        # Process each detected face
        detections = []
        for result in results:
            # Extract bounding box and convert to integers
            x = int(result['region']['x'])
            y = int(result['region']['y'])
            w = int(result['region']['w'])
            h = int(result['region']['h'])
            # confidence = float(result.get('face_confidence', 0.5))  # Default to 0.5 if missing

            # Add detection for SORT tracker
            if is_within_roi(x, y, w, h):
                confidence = float(result.get('face_confidence', 0.5))
                detections.append([x, y, x + w, y + h, confidence])

        # Ensure detections array is not empty
        if detections:
            # Update tracker with valid detections
            tracked_objects = tracker.update(np.array(detections))
        else:
            tracked_objects = []

        # Iterate through tracked objects
        for track in tracked_objects:
            track_id = int(track[4])  # Unique ID assigned by tracker
            x1, y1, x2, y2 = map(int, track[:4])

            # Match tracker object with DeepFace result (basic IOU/position check)
            for result in results:
                rx, ry, rw, rh = map(int, [result['region']['x'], result['region']['y'], result['region']['w'],
                                           result['region']['h']])
                if abs(x1 - rx) < 20 and abs(y1 - ry) < 20:  # Adjust threshold if necessary
                    # Retrieve gender confidence scores
                    male_confidence = result['gender']['Man']
                    female_confidence = result['gender']['Woman']

                    # Retrieve age from DeepFace result
                    age = int(result.get('age', -1))  # Default to -1 if age is missing
                    age_range = get_age_range(age)
                    gender = "Male" if male_confidence > 99.8 else "Female" if female_confidence > 60 else "Unknown"


                    if track_id not in tracking_data:
                        tracking_data[track_id] = {
                            "Ages": [],
                            "Genders": set(),
                            "Entry Time": formatted_time,
                            "Exit Time": None
                        }
                    tracking_data[track_id]["Ages"].append(age)
                    tracking_data[track_id]["Genders"].add(gender)

                    if is_within_roi(x1, y1, x2 - x1, y2 - y1):
                        tracking_data[track_id]["Exit Time"] = formatted_time

                    if gender != "Unknown":
                        label = f"{gender}, Age: {age_range}"
                        color = (0, 255, 0)
                        draw_label(resized_frame, x1, y1, x2 - x1, y2 - y1, label, color)

                    if gender == "Male":
                        global_male_count.add(track_id)
                        male_ages.append(age)
                    elif gender == "Female":
                        global_female_count.add(track_id)
                        female_ages.append(age)

                    # Draw the label on the frame
                    draw_label(resized_frame, x1, y1, x2 - x1, y2 - y1, label, color)

Thank you, and any insights would be super helpful