Bdd100k的标签是由Scalabel生成的JSON格式。
- labels [ ]:    - id: int32    - category: string (classification)    - manualShape: boolean (whether the shape of the label is created or modified manually)    - manualAttributes: boolean (whether the attribute of the label is created or modified manually)    - score: float (the confidence or some other ways of measuring the quality of the label.)    - attributes:        - occluded: boolean        - truncated: boolean        - trafficLightColor: "red|green|yellow|none"        - areaType: "direct | alternative" (for driving area)        - laneDirection: "parallel|vertical" (for lanes)        - laneStyle: "solid | dashed" (for lanes)        - laneTypes: (for lanes)    - box2d:       - x1: float       - y1: float       - x2: float       - y2: float

[    "bike",    "bus",    "car",    "motor",    "person",    "rider",    "traffic light",    "traffic sign",    "train",    "truck"]

for label in i['labels']:            annotation = dict()            category=label['category']            if (category == "traffic light"):                color = label['attributes']['trafficLightColor']                category = "tl_" + color            if category in id_dict.keys():                empty_image = False                annotation["iscrowd"] = 0                annotation["image_id"] = image['id']                x1 = label['box2d']['x1']                y1 = label['box2d']['y1']                x2 = label['box2d']['x2']                y2 = label['box2d']['y2']                annotation['bbox'] = [x1, y1, x2-x1, y2-y1]                annotation['area'] = float((x2 - x1) * (y2 - y1))                annotation['category_id'] = id_dict[category]                annotation['ignore'] = 0                annotation['id'] = label['id']                annotation['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]                annotations.append(annotation)

config_train ={        "datasets": "COCO",        "img_path": "bdd100k_images/bdd100k/images/100k/train",        "label": "labels/bdd100k_labels_images_det_coco_train.json",        "img_type": ".jpg",        "manipast_path": "./",        "output_path": "labels/trains/",        "cls_list": "bdd100k.names",    }    config_valid ={        "datasets": "COCO",        "img_path": "bdd100k_images/bdd100k/images/100k/val",        "label": "labels/bdd100k_labels_images_det_coco_val.json",        "img_type": ".jpg",        "manipast_path": "./",        "output_path": "labels/valids/",        "cls_list": "bdd100k.names",    }

personridercarbustruckbikemotortl_greentl_redtl_yellowtl_nonetraffic signtraintl_green

# pip install -r requirements.txt
# base ----------------------------------------Cythonmatplotlib>=3.2.2numpy>=1.18.5opencv-python>=4.1.2pillowPyYAML>=5.3scipy>=1.4.1tensorboard>=2.2torch>=1.6.0torchvision>=0.7.0tqdm>=4.41.0
# coco ----------------------------------------# pycocotools>=2.0
# export --------------------------------------# packaging  # for coremltools# coremltools==4.0b4# onnx>=1.7.0# scikit-learn==0.19.2  # for coreml quantization
# extras --------------------------------------# thop  # FLOPS computation# seaborn  # plotting

python train.py --img 640 --batch 32 --epochs 300 --data './data/bdd.yaml' --cfg ./models/custom_yolov5x.yaml --weights "./weights/yolov5x.pt" --name yolov5x_bdd_prew  --cache

python train.py --img 640 --batch 32 --epochs 300 --data './data/bdd.yaml' --cfg ./models/custom_yolov5x.yaml --weights "" --name yolov5x_bdd  --cache

python detect.py --source 0  # webcam                            file.jpg  # image                            file.mp4  # video                            path/  # directory                            path/*.jpg  # glob                            rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa  # rtsp stream                            rtmp://192.168.1.105/live/test  # rtmp stream                            http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8  # http stream

1. generate yolov5s.wts from pytorch with yolov5s.pt
git clone https://github.com/wang-xinyu/tensorrtx.gitgit clone https://github.com/ultralytics/yolov5.git// download its weights 'yolov5s.pt'// copy tensorrtx/yolov5/gen_wts.py into ultralytics/yolov5// ensure the file name is yolov5s.pt and yolov5s.wts in gen_wts.py// go to ultralytics/yolov5python gen_wts.py// a file 'yolov5s.wts' will be generated.

build tensorrtx/yolov5 and run
// put yolov5s.wts into tensorrtx/yolov5// go to tensorrtx/yolov5// ensure the macro NET in yolov5.cpp is smkdir buildcd buildcmake ..makesudo ./yolov5 -s             // serialize model to plan file i.e. 'yolov5s.engine'sudo ./yolov5 -d  ../samples // deserialize plan file and run inference, the images in samples will be processed.