diff --git a/our_scripts/detect.py b/our_scripts/detect.py
new file mode 100644
index 00000000..b4aaf229
--- /dev/null
+++ b/our_scripts/detect.py
@@ -0,0 +1,209 @@
+import argparse
+
+from models import *  # set ONNX_EXPORT in models.py
+from utils.datasets import *
+from utils.utils import *
+
+
+def detect(save_img=False):
+    imgsz = (320, 192) if ONNX_EXPORT else opt.img_size  # (320, 192) or (416, 256) or (608, 352) for (height, width)
+    out, source, weights, half, view_img, save_txt = opt.output, opt.source, opt.weights, opt.half, opt.view_img, opt.save_txt
+    webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
+
+    # Initialize
+    device = torch_utils.select_device(device='cpu' if ONNX_EXPORT else opt.device)
+    if os.path.exists(out):
+        shutil.rmtree(out)  # delete output folder
+    os.makedirs(out)  # make new output folder
+
+    # Initialize model
+    model = Darknet(opt.cfg, imgsz)
+
+    # Load weights
+    attempt_download(weights)
+    if weights.endswith('.pt'):  # pytorch format
+        model.load_state_dict(torch.load(weights, map_location=device)['model'])
+    else:  # darknet format
+        load_darknet_weights(model, weights)
+
+    # Second-stage classifier
+    classify = False
+    if classify:
+        modelc = torch_utils.load_classifier(name='resnet101', n=2)  # initialize
+        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model'])  # load weights
+        modelc.to(device).eval()
+
+    # Eval mode
+    model.to(device).eval()
+
+    # Fuse Conv2d + BatchNorm2d layers
+    # model.fuse()
+
+    # Export mode
+    if ONNX_EXPORT:
+        model.fuse()
+        img = torch.zeros((1, 3) + imgsz)  # (1, 3, 320, 192)
+        f = opt.weights.replace(opt.weights.split('.')[-1], 'onnx')  # *.onnx filename
+        torch.onnx.export(model, img, f, verbose=False, opset_version=11,
+                          input_names=['images'], output_names=['classes', 'boxes'])
+
+        # Validate exported model
+        import onnx
+        model = onnx.load(f)  # Load the ONNX model
+        onnx.checker.check_model(model)  # Check that the IR is well formed
+        print(onnx.helper.printable_graph(model.graph))  # Print a human readable representation of the graph
+        return
+
+    # Half precision
+    half = half and device.type != 'cpu'  # half precision only supported on CUDA
+    if half:
+        model.half()
+
+    # Set Dataloader
+    vid_path, vid_writer = None, None
+    if webcam:
+        view_img = True
+        torch.backends.cudnn.benchmark = True  # set True to speed up constant image size inference
+        dataset = LoadStreams(source, img_size=imgsz)
+    else:
+        save_img = True
+        dataset = LoadImages(source, img_size=imgsz)
+
+    # Get names and colors
+    names = load_classes(opt.names)
+    colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
+    output_json_file = open(opt.json, 'w')
+
+    # Run inference
+    t0 = time.time()
+    img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
+    _ = model(img.half() if half else img.float()) if device.type != 'cpu' else None  # run once
+    for i, (path, img, im0s, vid_cap) in enumerate(dataset):
+        json = {"detections": []}
+        img = torch.from_numpy(img).to(device)
+        img = img.half() if half else img.float()  # uint8 to fp16/32
+        img /= 255.0  # 0 - 255 to 0.0 - 1.0
+        if img.ndimension() == 3:
+            img = img.unsqueeze(0)
+
+        # Inference
+        t1 = torch_utils.time_synchronized()
+        pred = model(img, augment=opt.augment)[0]
+        t2 = torch_utils.time_synchronized()
+
+        # to float
+        if half:
+            pred = pred.float()
+
+        # Apply NMS
+        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres,
+                                   multi_label=False, classes=opt.classes, agnostic=opt.agnostic_nms)
+
+        # Apply Classifier
+        if classify:
+            pred = apply_classifier(pred, modelc, img, im0s)
+
+        # Process detections
+        for i, det in enumerate(pred):  # detections for image i
+            if webcam:  # batch_size >= 1
+                p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
+            else:
+                p, s, im0 = path, '', im0s
+
+            save_path = str(Path(out) / Path(p).name)
+            s += '%gx%g ' % img.shape[2:]  # print string
+            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  #  normalization gain whwh
+            if det is not None and len(det):
+                # Rescale boxes from imgsz to im0 size
+                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
+
+                # Print results
+                for c in det[:, -1].unique():
+                    n = (det[:, -1] == c).sum()  # detections per class
+                    s += '%g %ss, ' % (n, names[int(c)])  # add to string
+
+                # Write results
+                for *xyxy, conf, cls in det:
+                    if save_txt:  # Write to file
+                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
+                        with open(save_path[:save_path.rfind('.')] + '.txt', 'a') as file:
+                            file.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format
+
+                    label_to_display = '%s %.2f' % (names[int(cls)], conf)
+                    name = '%s' % names[int(cls)]
+                    probability = float('%.3f' % conf)
+                    left, top, right, bottom = plot_one_box(xyxy, im0, label=label_to_display, color=colors[int(cls)])
+                    json["detections"].append({"classes": [{"name": name, "probability": probability}],
+                                               "rect": {"bottom": bottom, "left": left, "right": right, "top": top}})
+
+                    if save_img or view_img:  # Add bbox to image
+                        label = '%s %.2f' % (names[int(cls)], conf)
+                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)])
+
+            # Print time (inference + NMS)
+            print('%sDone. (%.3fs)' % (s, t2 - t1))
+
+            json["frameId"] = i
+            line = str(json).replace("\'", "\"")
+
+            output_json_file.write(line)
+            output_json_file.write("\n")
+
+            # Stream results
+            if view_img:
+                cv2.imshow(p, im0)
+                if cv2.waitKey(1) == ord('q'):  # q to quit
+                    raise StopIteration
+
+            # Save results (image with detections)
+            if save_img:
+                if dataset.mode == 'images':
+                    cv2.imwrite(save_path, im0)
+                else:
+                    if vid_path != save_path:  # new video
+                        vid_path = save_path
+                        if isinstance(vid_writer, cv2.VideoWriter):
+                            vid_writer.release()  # release previous video writer
+
+                        fps = vid_cap.get(cv2.CAP_PROP_FPS)
+                        w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+                        h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                        vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*opt.fourcc), fps, (w, h))
+                    vid_writer.write(im0)
+
+    output_json_file.close()
+    if save_txt or save_img:
+        print('Results saved to %s' % os.getcwd() + os.sep + out)
+        if platform == 'darwin':  # MacOS
+            os.system('open ' + save_path)
+
+    print('Done. (%.3fs)' % (time.time() - t0))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path')
+    parser.add_argument('--names', type=str, default='data/coco.names', help='*.names path')
+    parser.add_argument('--weights', type=str, default='weights/yolov3-spp-ultralytics.pt', help='weights path')
+    parser.add_argument('--source', type=str, default='data/samples', help='source')  # input file/folder, 0 for webcam
+    parser.add_argument('--output', type=str, default='output', help='output folder')  # output folder
+    parser.add_argument('--img-size', type=int, default=512, help='inference size (pixels)')
+    parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
+    parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS')
+    parser.add_argument('--fourcc', type=str, default='mp4v', help='output video codec (verify ffmpeg support)')
+    parser.add_argument('--half', action='store_true', help='half precision FP16 inference')
+    parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')
+    parser.add_argument('--view-img', action='store_true', help='display results')
+    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
+    parser.add_argument('--classes', nargs='+', type=int, help='filter by class')
+    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
+    parser.add_argument('--augment', action='store_true', help='augmented inference')
+    parser.add_argument('--json', type=str, default='output.txt', help='output json file ')
+    parser.add_argument('--save-video', type=bool, default=True, help='saving output video')
+    opt = parser.parse_args()
+    opt.cfg = check_file(opt.cfg)  # check file
+    opt.names = check_file(opt.names)  # check file
+    print(opt)
+
+    with torch.no_grad():
+        detect(opt.save_video)