"Input resolution of the network. Increase to increase accuracy. Decrease to increase speed", default="416", type=str) return parser.parse_args() args = arg_parse() images = args.images batch_size = int(args.bs) confidence = float(args.confidence) nms_thesh = float(args.nms_thresh) start = 0 CUDA = torch.cuda.is_available() #***Loading Dataset Class File*** classes = load_classes("data/idd.names") #***Setting up the neural network*** model = Darknet(args.cfgfile) print('\033[0m' + "Input Data Passed Into YOLO Model..." + u'\N{check mark}') model.load_weights(args.weightsfile) print('\033[0m' + "YOLO Neural Network Successfully Loaded..." + u'\N{check mark}') print('\033[0m') model.hyperparams["height"] = args.reso inp_dim = int(model.hyperparams["height"]) assert inp_dim % 32 == 0 assert inp_dim > 32 num_classes = model.num_classes print('\033[1m' + '\033[94m' + "Performing Vehicle Detection with YOLO Neural Network..." + '\033[0m' +
default="448", type=str) return parser.parse_args() args = arg_parse() images = args.images outputs_names = args.outputs batch_size = int(args.bs) confidence = float(args.confidence) nms_thesh = float(args.nms_thresh) start = 0 CUDA = torch.cuda.is_available() classes = load_classes("data/coco.names") counter = 0 # Set up the neural network print("Loading network.....") model = Darknet(args.cfgfile) model.load_weights(args.weightsfile) print("Network successfully loaded") model.hyperparams["height"] = args.reso inp_dim = int(model.hyperparams["height"]) assert inp_dim % 32 == 0 assert inp_dim > 32 num_classes = model.num_classes
parser.add_argument("--nms_thres", type=float, default=0.4, help="iou thresshold for non-maximum suppression") parser.add_argument("--n_cpu", type=int, default=0, help="number of cpu threads to use during batch generation") parser.add_argument("--img_size", type=int, default=416, help="size of each image dimension") parser.add_argument("--checkpoint_interval", type=int, default=1, help="interval between saving model weights") parser.add_argument("--checkpoint_dir", type=str, default="checkpoints", help="directory where model checkpoints are saved") parser.add_argument("--use_cuda", type=bool, default=True, help="whether to use cuda if available") args = parser.parse_args() cuda = torch.cuda.is_available() and args.use_cuda os.makedirs("output", exist_ok=True) os.makedirs("checkpoints", exist_ok=True) classes = load_classes(args.class_path) # Get data configuration data_config = parse_data_config(args.data_config_path) train_path = data_config["train"] # Get hyper parameters hyperparams = parse_model_configuration(args.model_config_path)[0] learning_rate = float(hyperparams["learning_rate"]) momentum = float(hyperparams["momentum"]) decay = float(hyperparams["decay"]) burn_in = int(hyperparams["burn_in"]) # Initiate model model = Darknet(args.model_config_path)