"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)
