diff --git a/frigate/test/__init__.py b/frigate/test/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/frigate/test/test_false_positives.py b/frigate/test/test_false_positives.py
deleted file mode 100644
index afba06fa2..000000000
--- a/frigate/test/test_false_positives.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import datetime
-from unittest import TestCase, main
-from frigate.video import process_frames, start_or_restart_ffmpeg, capture_frames
-from frigate.util import DictFrameManager, EventsPerSecond, draw_box_with_label
-from frigate.motion import MotionDetector
-from frigate.edgetpu import LocalObjectDetector
-from frigate.objects import ObjectTracker
-import multiprocessing as mp
-import numpy as np
-import cv2
-from frigate.object_processing import COLOR_MAP
-
-class FalsePositiveTests(TestCase):
-
-    def test_back_1594395958_675351_0(self):
-        ### load in frames
-        frame_shape = (1080,1920,3)
-        frame_manager = DictFrameManager()
-        frame_queue = mp.Queue()
-        fps = EventsPerSecond()
-        skipped_fps = EventsPerSecond()
-        stop_event = mp.Event()
-        detection_frame = mp.Value('d', datetime.datetime.now().timestamp()+100000)
-        ffmpeg_cmd = "ffmpeg -hide_banner -loglevel panic -i /debug/false_positives/back-1595647759.228381-0.mp4 -f rawvideo -pix_fmt rgb24 pipe:".split(" ")
-        ffmpeg_process = start_or_restart_ffmpeg(ffmpeg_cmd, frame_shape[0]*frame_shape[1]*frame_shape[2])
-        capture_frames(ffmpeg_process, "back", frame_shape, frame_manager, frame_queue, 1, fps, skipped_fps, stop_event, detection_frame)
-        ffmpeg_process.wait()
-        ffmpeg_process.communicate()
-        assert(frame_queue.qsize() > 0)
-        
-        ### process frames
-        mask = np.zeros((frame_shape[0], frame_shape[1], 1), np.uint8)
-        mask[:] = 255
-        motion_detector = MotionDetector(frame_shape, mask)
-
-        object_detector = LocalObjectDetector(labels='/labelmap.txt')
-        object_tracker = ObjectTracker(10)
-        detected_objects_queue = mp.Queue()
-        process_fps = EventsPerSecond()
-        current_frame = mp.Value('d', 0.0)
-
-        process_frames("back", frame_queue, frame_shape, frame_manager, motion_detector, object_detector, object_tracker, detected_objects_queue, 
-            process_fps, current_frame, ['person'], {}, mask, stop_event, exit_on_empty=True)
-        assert(detected_objects_queue.qsize() > 0)
-
-        ### check result
-        while(not detected_objects_queue.empty()):
-            camera_name, frame_time, current_tracked_objects = detected_objects_queue.get()
-
-            current_frame = frame_manager.get(f"{camera_name}{frame_time}")
-            # draw the bounding boxes on the frame
-            for obj in current_tracked_objects.values():
-                thickness = 2
-                color = COLOR_MAP[obj['label']]
-                
-                if obj['frame_time'] != frame_time:
-                    thickness = 1
-                    color = (255,0,0)
-
-                # draw the bounding boxes on the frame
-                box = obj['box']
-                draw_box_with_label(current_frame, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
-                # draw the regions on the frame
-                region = obj['region']
-                draw_box_with_label(current_frame, region[0], region[1], region[2], region[3], 'region', f"{region[2]-region[0]}", thickness=1, color=(0,255,0))
-            
-            cv2.imwrite(f"/debug/frames/{int(frame_time*1000000)}.jpg", cv2.cvtColor(current_frame, cv2.COLOR_RGB2BGR))
-
-
-if __name__ == '__main__':
-    main()
\ No newline at end of file