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best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a2ed7f935848ec90e985a008cb63b90e82e30e1615d93720cf6bbebccd762cf4
+size 14492776

data.yaml

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+train: F:\yolov5\images\train
+val: F:\yolov5\images\val
+nc: 10  # Number of classes (change to the actual number of object classes)
+names: ['Arbacia_lixula', 'Astropecten_spinulosus', 'Callistoctopus_macropus','Diadema_setosum','Echinaster_sepositus','Loligo_vulgaris','Octopus_vulgaris','Sphyraena_sphyraena','Seranus_scribba','Pterois_miles']
+
+

detect.py

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+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""
+Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
+
+Usage - sources:
+    $ python detect.py --weights yolov5s.pt --source 0                               # webcam
+                                                     img.jpg                         # image
+                                                     vid.mp4                         # video
+                                                     screen                          # screenshot
+                                                     path/                           # directory
+                                                     list.txt                        # list of images
+                                                     list.streams                    # list of streams
+                                                     'path/*.jpg'                    # glob
+                                                     'https://youtu.be/LNwODJXcvt4'  # YouTube
+                                                     'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
+
+Usage - formats:
+    $ python detect.py --weights yolov5s.pt                 # PyTorch
+                                 yolov5s.torchscript        # TorchScript
+                                 yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
+                                 yolov5s_openvino_model     # OpenVINO
+                                 yolov5s.engine             # TensorRT
+                                 yolov5s.mlpackage          # CoreML (macOS-only)
+                                 yolov5s_saved_model        # TensorFlow SavedModel
+                                 yolov5s.pb                 # TensorFlow GraphDef
+                                 yolov5s.tflite             # TensorFlow Lite
+                                 yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
+                                 yolov5s_paddle_model       # PaddlePaddle
+"""
+
+import argparse
+import csv
+import os
+import platform
+import sys
+from pathlib import Path
+
+import torch
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from ultralytics.utils.plotting import Annotator, colors, save_one_box
+
+from models.common import DetectMultiBackend
+from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
+from utils.general import (
+    LOGGER,
+    Profile,
+    check_file,
+    check_img_size,
+    check_imshow,
+    check_requirements,
+    colorstr,
+    cv2,
+    increment_path,
+    non_max_suppression,
+    print_args,
+    scale_boxes,
+    strip_optimizer,
+    xyxy2xywh,
+)
+from utils.torch_utils import select_device, smart_inference_mode
+
+
+@smart_inference_mode()
+def run(
+    weights=ROOT / "yolov5s.pt",  # model path or triton URL
+    source=ROOT / "data/images",  # file/dir/URL/glob/screen/0(webcam)
+    data=ROOT / "data/coco128.yaml",  # dataset.yaml path
+    imgsz=(640, 640),  # inference size (height, width)
+    conf_thres=0.25,  # confidence threshold
+    iou_thres=0.45,  # NMS IOU threshold
+    max_det=1000,  # maximum detections per image
+    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+    view_img=False,  # show results
+    save_txt=False,  # save results to *.txt
+    save_format=0,  # save boxes coordinates in YOLO format or Pascal-VOC format (0 for YOLO and 1 for Pascal-VOC)
+    save_csv=False,  # save results in CSV format
+    save_conf=False,  # save confidences in --save-txt labels
+    save_crop=False,  # save cropped prediction boxes
+    nosave=False,  # do not save images/videos
+    classes=None,  # filter by class: --class 0, or --class 0 2 3
+    agnostic_nms=False,  # class-agnostic NMS
+    augment=False,  # augmented inference
+    visualize=False,  # visualize features
+    update=False,  # update all models
+    project=ROOT / "runs/detect",  # save results to project/name
+    name="exp",  # save results to project/name
+    exist_ok=False,  # existing project/name ok, do not increment
+    line_thickness=3,  # bounding box thickness (pixels)
+    hide_labels=False,  # hide labels
+    hide_conf=False,  # hide confidences
+    half=False,  # use FP16 half-precision inference
+    dnn=False,  # use OpenCV DNN for ONNX inference
+    vid_stride=1,  # video frame-rate stride
+):
+    
+    """
+    Runs YOLOv5 detection inference on various sources like images, videos, directories, streams, etc.
+
+    Args:
+        weights (str | Path): Path to the model weights file or a Triton URL. Default is 'yolov5s.pt'.
+        source (str | Path): Input source, which can be a file, directory, URL, glob pattern, screen capture, or webcam
+            index. Default is 'data/images'.
+        data (str | Path): Path to the dataset YAML file. Default is 'data/coco128.yaml'.
+        imgsz (tuple[int, int]): Inference image size as a tuple (height, width). Default is (640, 640).
+        conf_thres (float): Confidence threshold for detections. Default is 0.25.
+        iou_thres (float): Intersection Over Union (IOU) threshold for non-max suppression. Default is 0.45.
+        max_det (int): Maximum number of detections per image. Default is 1000.
+        device (str): CUDA device identifier (e.g., '0' or '0,1,2,3') or 'cpu'. Default is an empty string, which uses the
+            best available device.
+        view_img (bool): If True, display inference results using OpenCV. Default is False.
+        save_txt (bool): If True, save results in a text file. Default is False.
+        save_csv (bool): If True, save results in a CSV file. Default is False.
+        save_conf (bool): If True, include confidence scores in the saved results. Default is False.
+        save_crop (bool): If True, save cropped prediction boxes. Default is False.
+        nosave (bool): If True, do not save inference images or videos. Default is False.
+        classes (list[int]): List of class indices to filter detections by. Default is None.
+        agnostic_nms (bool): If True, perform class-agnostic non-max suppression. Default is False.
+        augment (bool): If True, use augmented inference. Default is False.
+        visualize (bool): If True, visualize feature maps. Default is False.
+        update (bool): If True, update all models' weights. Default is False.
+        project (str | Path): Directory to save results. Default is 'runs/detect'.
+        name (str): Name of the current experiment; used to create a subdirectory within 'project'. Default is 'exp'.
+        exist_ok (bool): If True, existing directories with the same name are reused instead of being incremented. Default is
+            False.
+        line_thickness (int): Thickness of bounding box lines in pixels. Default is 3.
+        hide_labels (bool): If True, do not display labels on bounding boxes. Default is False.
+        hide_conf (bool): If True, do not display confidence scores on bounding boxes. Default is False.
+        half (bool): If True, use FP16 half-precision inference. Default is False.
+        dnn (bool): If True, use OpenCV DNN backend for ONNX inference. Default is False.
+        vid_stride (int): Stride for processing video frames, to skip frames between processing. Default is 1.
+
+    Returns:
+        None
+
+    Examples:
+        ```python
+        from ultralytics import run
+
+        # Run inference on an image
+        run(source='data/images/example.jpg', weights='yolov5s.pt', device='0')
+
+        # Run inference on a video with specific confidence threshold
+        run(source='data/videos/example.mp4', weights='yolov5s.pt', conf_thres=0.4, device='0')
+        ```
+    """
+    source = str(source)
+    save_img = not nosave and not source.endswith(".txt")  # save inference images
+    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
+    is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://"))
+    webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file)
+    screenshot = source.lower().startswith("screen")
+    if is_url and is_file:
+        source = check_file(source)  # download
+
+    # Directories
+    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
+    (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
+
+    # Load model
+    device = select_device(device)
+    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
+    stride, names, pt = model.stride, model.names, model.pt
+    imgsz = check_img_size(imgsz, s=stride)  # check image size
+
+    # Dataloader
+    bs = 1  # batch_size
+    if webcam:
+        view_img = check_imshow(warn=True)
+        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
+        bs = len(dataset)
+    elif screenshot:
+        dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
+    else:
+        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
+    vid_path, vid_writer = [None] * bs, [None] * bs
+
+    # Run inference
+    model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))  # warmup
+    seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device))
+    for path, im, im0s, vid_cap, s in dataset:
+        with dt[0]:
+            im = torch.from_numpy(im).to(model.device)
+            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
+            im /= 255  # 0 - 255 to 0.0 - 1.0
+            if len(im.shape) == 3:
+                im = im[None]  # expand for batch dim
+            if model.xml and im.shape[0] > 1:
+                ims = torch.chunk(im, im.shape[0], 0)
+
+        # Inference
+        with dt[1]:
+            visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
+            if model.xml and im.shape[0] > 1:
+                pred = None
+                for image in ims:
+                    if pred is None:
+                        pred = model(image, augment=augment, visualize=visualize).unsqueeze(0)
+                    else:
+                        pred = torch.cat((pred, model(image, augment=augment, visualize=visualize).unsqueeze(0)), dim=0)
+                pred = [pred, None]
+            else:
+                pred = model(im, augment=augment, visualize=visualize)
+        # NMS
+        with dt[2]:
+            pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
+
+        # Second-stage classifier (optional)
+        # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
+
+        # Define the path for the CSV file
+        csv_path = save_dir / "predictions.csv"
+
+        # Create or append to the CSV file
+        def write_to_csv(image_name, prediction, confidence):
+            """Writes prediction data for an image to a CSV file, appending if the file exists."""
+            data = {"Image Name": image_name, "Prediction": prediction, "Confidence": confidence}
+            with open(csv_path, mode="a", newline="") as f:
+                writer = csv.DictWriter(f, fieldnames=data.keys())
+                if not csv_path.is_file():
+                    writer.writeheader()
+                writer.writerow(data)
+
+        # Process predictions
+        for i, det in enumerate(pred):  # per image
+            seen += 1
+            if webcam:  # batch_size >= 1
+                p, im0, frame = path[i], im0s[i].copy(), dataset.count
+                s += f"{i}: "
+            else:
+                p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)
+
+            p = Path(p)  # to Path
+            save_path = str(save_dir / p.name)  # im.jpg
+            txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}")  # im.txt
+            s += "{:g}x{:g} ".format(*im.shape[2:])  # print string
+            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
+            imc = im0.copy() if save_crop else im0  # for save_crop
+            annotator = Annotator(im0, line_width=line_thickness, example=str(names))
+            if len(det):
+                # Rescale boxes from img_size to im0 size
+                det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
+
+                # Print results
+                for c in det[:, 5].unique():
+                    n = (det[:, 5] == c).sum()  # detections per class
+                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
+
+                # Write results
+                for *xyxy, conf, cls in reversed(det):
+                    c = int(cls)  # integer class
+                    label = names[c] if hide_conf else f"{names[c]}"
+                    confidence = float(conf)
+                    confidence_str = f"{confidence:.2f}"
+
+                    if save_csv:
+                        write_to_csv(p.name, label, confidence_str)
+
+                    if save_txt:  # Write to file
+                        if save_format == 0:
+                            coords = (
+                                (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()
+                            )  # normalized xywh
+                        else:
+                            coords = (torch.tensor(xyxy).view(1, 4) / gn).view(-1).tolist()  # xyxy
+                        line = (cls, *coords, conf) if save_conf else (cls, *coords)  # label format
+                        with open(f"{txt_path}.txt", "a") as f:
+                            f.write(("%g " * len(line)).rstrip() % line + "\n")
+
+                    if save_img or save_crop or view_img:  # Add bbox to image
+                        c = int(cls)  # integer class
+                        label = None if hide_labels else (names[c] if hide_conf else f"{names[c]} {conf:.2f}")
+                        annotator.box_label(xyxy, label, color=colors(c, True))
+                    if save_crop:
+                        save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True)
+
+            # Stream results
+            im0 = annotator.result()
+            if view_img:
+                if platform.system() == "Linux" and p not in windows:
+                    windows.append(p)
+                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
+                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
+                cv2.imshow(str(p), im0)
+                cv2.waitKey(1)  # 1 millisecond
+
+            # Save results (image with detections)
+            if save_img:
+                if dataset.mode == "image":
+                    cv2.imwrite(save_path, im0)
+                else:  # 'video' or 'stream'
+                    if vid_path[i] != save_path:  # new video
+                        vid_path[i] = save_path
+                        if isinstance(vid_writer[i], cv2.VideoWriter):
+                            vid_writer[i].release()  # release previous video writer
+                        if vid_cap:  # video
+                            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))
+                        else:  # stream
+                            fps, w, h = 30, im0.shape[1], im0.shape[0]
+                        save_path = str(Path(save_path).with_suffix(".mp4"))  # force *.mp4 suffix on results videos
+                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
+                    vid_writer[i].write(im0)
+
+        # Print time (inference-only)
+        LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
+
+    # Print results
+    t = tuple(x.t / seen * 1e3 for x in dt)  # speeds per image
+    LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t)
+    if save_txt or save_img:
+        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ""
+        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
+    if update:
+        strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)
+
+
+def parse_opt():
+    """
+    Parse command-line arguments for YOLOv5 detection, allowing custom inference options and model configurations.
+
+    Args:
+        --weights (str | list[str], optional): Model path or Triton URL. Defaults to ROOT / 'yolov5s.pt'.
+        --source (str, optional): File/dir/URL/glob/screen/0(webcam). Defaults to ROOT / 'data/images'.
+        --data (str, optional): Dataset YAML path. Provides dataset configuration information.
+        --imgsz (list[int], optional): Inference size (height, width). Defaults to [640].
+        --conf-thres (float, optional): Confidence threshold. Defaults to 0.25.
+        --iou-thres (float, optional): NMS IoU threshold. Defaults to 0.45.
+        --max-det (int, optional): Maximum number of detections per image. Defaults to 1000.
+        --device (str, optional): CUDA device, i.e., '0' or '0,1,2,3' or 'cpu'. Defaults to "".
+        --view-img (bool, optional): Flag to display results. Defaults to False.
+        --save-txt (bool, optional): Flag to save results to *.txt files. Defaults to False.
+        --save-csv (bool, optional): Flag to save results in CSV format. Defaults to False.
+        --save-conf (bool, optional): Flag to save confidences in labels saved via --save-txt. Defaults to False.
+        --save-crop (bool, optional): Flag to save cropped prediction boxes. Defaults to False.
+        --nosave (bool, optional): Flag to prevent saving images/videos. Defaults to False.
+        --classes (list[int], optional): List of classes to filter results by, e.g., '--classes 0 2 3'. Defaults to None.
+        --agnostic-nms (bool, optional): Flag for class-agnostic NMS. Defaults to False.
+        --augment (bool, optional): Flag for augmented inference. Defaults to False.
+        --visualize (bool, optional): Flag for visualizing features. Defaults to False.
+        --update (bool, optional): Flag to update all models in the model directory. Defaults to False.
+        --project (str, optional): Directory to save results. Defaults to ROOT / 'runs/detect'.
+        --name (str, optional): Sub-directory name for saving results within --project. Defaults to 'exp'.
+        --exist-ok (bool, optional): Flag to allow overwriting if the project/name already exists. Defaults to False.
+        --line-thickness (int, optional): Thickness (in pixels) of bounding boxes. Defaults to 3.
+        --hide-labels (bool, optional): Flag to hide labels in the output. Defaults to False.
+        --hide-conf (bool, optional): Flag to hide confidences in the output. Defaults to False.
+        --half (bool, optional): Flag to use FP16 half-precision inference. Defaults to False.
+        --dnn (bool, optional): Flag to use OpenCV DNN for ONNX inference. Defaults to False.
+        --vid-stride (int, optional): Video frame-rate stride, determining the number of frames to skip in between
+            consecutive frames. Defaults to 1.
+
+    Returns:
+        argparse.Namespace: Parsed command-line arguments as an argparse.Namespace object.
+
+    Example:
+        ```python
+        from ultralytics import YOLOv5
+        args = YOLOv5.parse_opt()
+        ```
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model path or triton URL")
+    parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)")
+    parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path")
+    parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w")
+    parser.add_argument("--conf-thres", type=float, default=0.25, help="confidence threshold")
+    parser.add_argument("--iou-thres", type=float, default=0.45, help="NMS IoU threshold")
+    parser.add_argument("--max-det", type=int, default=1000, help="maximum detections per image")
+    parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
+    parser.add_argument("--view-img", action="store_true", help="show results")
+    parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
+    parser.add_argument(
+        "--save-format",
+        type=int,
+        default=0,
+        help="whether to save boxes coordinates in YOLO format or Pascal-VOC format when save-txt is True, 0 for YOLO and 1 for Pascal-VOC",
+    )
+    parser.add_argument("--save-csv", action="store_true", help="save results in CSV format")
+    parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels")
+    parser.add_argument("--save-crop", action="store_true", help="save cropped prediction boxes")
+    parser.add_argument("--nosave", action="store_true", help="do not save images/videos")
+    parser.add_argument("--classes", nargs="+", type=int, help="filter by class: --classes 0, or --classes 0 2 3")
+    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("--visualize", action="store_true", help="visualize features")
+    parser.add_argument("--update", action="store_true", help="update all models")
+    parser.add_argument("--project", default=ROOT / "runs/detect", help="save results to project/name")
+    parser.add_argument("--name", default="exp", help="save results to project/name")
+    parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
+    parser.add_argument("--line-thickness", default=3, type=int, help="bounding box thickness (pixels)")
+    parser.add_argument("--hide-labels", default=False, action="store_true", help="hide labels")
+    parser.add_argument("--hide-conf", default=False, action="store_true", help="hide confidences")
+    parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
+    parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
+    parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride")
+    opt = parser.parse_args()
+    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
+    print_args(vars(opt))
+    return opt
+
+
+def main(opt):
+    """
+    Executes YOLOv5 model inference based on provided command-line arguments, validating dependencies before running.
+
+    Args:
+        opt (argparse.Namespace): Command-line arguments for YOLOv5 detection. See function `parse_opt` for details.
+
+    Returns:
+        None
+
+    Note:
+        This function performs essential pre-execution checks and initiates the YOLOv5 detection process based on user-specified
+        options. Refer to the usage guide and examples for more information about different sources and formats at:
+        https://github.com/ultralytics/ultralytics
+
+    Example usage:
+
+    ```python
+    if __name__ == "__main__":
+        opt = parse_opt()
+        main(opt)
+    ```
+    """
+    check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
+    run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

requirements.txt

@@ -0,0 +1,49 @@
+# YOLOv5 requirements
+# Usage: pip install -r requirements.txt
+
+# Base ------------------------------------------------------------------------
+gitpython>=3.1.30
+matplotlib>=3.3
+numpy>=1.23.5
+opencv-python>=4.1.1
+pillow>=10.3.0
+psutil  # system resources
+PyYAML>=5.3.1
+requests>=2.32.2
+scipy>=1.4.1
+thop>=0.1.1  # FLOPs computation
+torch>=1.8.0  # see https://pytorch.org/get-started/locally (recommended)
+torchvision>=0.9.0
+tqdm>=4.66.3
+ultralytics>=8.2.34  # https://ultralytics.com
+# protobuf<=3.20.1  # https://github.com/ultralytics/yolov5/issues/8012
+
+# Logging ---------------------------------------------------------------------
+# tensorboard>=2.4.1
+# clearml>=1.2.0
+# comet
+
+# Plotting --------------------------------------------------------------------
+pandas>=1.1.4
+seaborn>=0.11.0
+
+# Export ----------------------------------------------------------------------
+# coremltools>=6.0  # CoreML export
+# onnx>=1.10.0  # ONNX export
+# onnx-simplifier>=0.4.1  # ONNX simplifier
+# nvidia-pyindex  # TensorRT export
+# nvidia-tensorrt  # TensorRT export
+# scikit-learn<=1.1.2  # CoreML quantization
+# tensorflow>=2.4.0,<=2.13.1  # TF exports (-cpu, -aarch64, -macos)
+# tensorflowjs>=3.9.0  # TF.js export
+# openvino-dev>=2023.0  # OpenVINO export
+
+# Deploy ----------------------------------------------------------------------
+setuptools>=70.0.0 # Snyk vulnerability fix
+# tritonclient[all]~=2.24.0
+
+# Extras ----------------------------------------------------------------------
+# ipython  # interactive notebook
+# mss  # screenshots
+# albumentations>=1.0.3
+# pycocotools>=2.0.6  # COCO mAP