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Functions275 in github.com/anil2k/smart-car-parking-yolov5

⨍ Functions 275◇ Types & classes 46↳ Endpoints 1
↓ 31 callersMethodinfo
(self, verbose=False, img_size=640)
yolov5/models/yolo.py:224
↓ 17 callersMethodplot
(self, save_dir='', names=())
yolov5/utils/metrics.py:160
↓ 17 callersFunctiontime_synchronized
()
yolov5/utils/torch_utils.py:89
↓ 15 callersMethod__init__
(self, c1, c2, n=1, shortcut=True, g=1, e=0.5)
yolov5/models/common.py:128
↓ 15 callersFunctioncolorstr
(*input)
yolov5/utils/general.py:241
↓ 13 callersMethodtolist
(self)
yolov5/models/common.py:371
↓ 12 callersMethodlog
(self, log_dict)
yolov5/utils/wandb_logging/wandb_utils.py:281
↓ 11 callersFunctionxyxy2xywh
(x)
yolov5/utils/general.py:305
↓ 10 callersFunctioncreate
Creates a specified YOLOv5 model Arguments: name (str): name of model, i.e. 'yolov5s' pretrained (bool): load pretrained weights
yolov5/hubconf.py:21
↓ 10 callersMethodsave
(self, save_dir='runs/hub/exp')
yolov5/models/common.py:348
↓ 7 callersFunctioncheck_requirements
(requirements='requirements.txt', exclude=())
yolov5/utils/general.py:110
↓ 7 callersFunctionincrement_path
(path, exist_ok=False, sep='', mkdir=False)
yolov5/utils/general.py:661
↓ 7 callersFunctionselect_device
(device='', batch_size=None)
yolov5/utils/torch_utils.py:63
↓ 7 callersFunctionset_logging
(rank=-1, verbose=True)
yolov5/utils/general.py:35
↓ 7 callersFunctionxywh2xyxy
(x)
yolov5/utils/general.py:315
↓ 6 callersFunctionattempt_load
(weights, map_location=None, inplace=True)
yolov5/models/experimental.py:113
↓ 6 callersFunctionfitness
(x)
yolov5/utils/metrics.py:12
↓ 6 callersFunctionletterbox
(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32)
yolov5/utils/datasets.py:821
↓ 6 callersFunctionscale_coords
(img1_shape, coords, img0_shape, ratio_pad=None)
yolov5/utils/general.py:369
↓ 5 callersMethod__init__
(self, n, weight=False)
yolov5/models/experimental.py:27
↓ 5 callersFunctioncheck_file
(file)
yolov5/utils/general.py:162
↓ 5 callersFunctioncheck_img_size
(img_size, s=32)
yolov5/utils/general.py:139
↓ 5 callersMethoddisplay
(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path(''))
yolov5/models/common.py:314
↓ 5 callersFunctionimg2label_paths
(img_paths)
yolov5/utils/datasets.py:341
↓ 5 callersFunctionnon_max_suppression
Runs Non-Maximum Suppression (NMS) on inference results Returns: list of detections, on (n,6) tensor per image [xyxy, conf, cls]
yolov5/utils/general.py:471
↓ 5 callersFunctionremove_prefix
(from_string, prefix=WANDB_ARTIFACT_PREFIX)
yolov5/utils/wandb_logging/wandb_utils.py:23
↓ 4 callersFunctionfile_size
(file)
yolov5/utils/general.py:73
↓ 4 callersFunctionis_parallel
(model)
yolov5/utils/torch_utils.py:135
↓ 4 callersFunctionload_image
(self, index)
yolov5/utils/datasets.py:632
↓ 4 callersFunctionmetric
(k)
yolov5/utils/autoanchor.py:31
↓ 4 callersFunctionplot_one_box
(x, im, color=None, label=None, line_thickness=3)
yolov5/utils/plots.py:69
↓ 3 callersMethod__init__
(self, loss_fcn, gamma=1.5, alpha=0.25)
yolov5/utils/loss.py:34
↓ 3 callersFunctionattempt_download
(file, repo='ultralytics/yolov5')
yolov5/utils/google_utils.py:19
↓ 3 callersFunctioncheck_dataset
(dict)
yolov5/utils/general.py:173
↓ 3 callersFunctioncreate_dataloader
(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False,
yolov5/utils/datasets.py:59
↓ 3 callersFunctionmake_divisible
(x, divisor)
yolov5/utils/general.py:226
↓ 3 callersFunctionplot_mc_curve
(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric')
yolov5/utils/metrics.py:206
↓ 3 callersFunctionprint_results
(k)
yolov5/utils/autoanchor.py:93
↓ 3 callersFunctionrandom_perspective
(img, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0,
yolov5/utils/datasets.py:854
↓ 3 callersFunctionsave_one_box
(xyxy, im, file='image.jpg', gain=1.02, pad=10, square=False, BGR=False)
yolov5/utils/general.py:648
↓ 3 callersFunctionstrip_optimizer
(f='best.pt', s='')
yolov5/utils/general.py:566
↓ 3 callersFunctiontest
(data, weights=None, batch_size=32, imgsz=640, conf_thres=0.001,
yolov5/test.py:21
↓ 3 callersFunctiontorch_distributed_zero_first
Decorator to make all processes in distributed training wait for each local_master to do something.
yolov5/utils/torch_utils.py:28
↓ 3 callersFunctionxywhn2xyxy
(x, w=640, h=640, padw=0, padh=0)
yolov5/utils/general.py:325
↓ 2 callersFunctionDWConv
(c1, c2, k=1, s=1, act=True)
yolov5/models/common.py:28
↓ 2 callersMethod__init__
(self, c1, k=3)
yolov5/utils/activations.py:49
↓ 2 callersFunctionanchor_fitness
(k)
yolov5/utils/autoanchor.py:89
↓ 2 callersFunctionapply_classifier
(x, model, img, im0)
yolov5/utils/general.py:613
↓ 2 callersFunctionautopad
(k, p=None)
yolov5/models/common.py:21
↓ 2 callersMethodbackward
(ctx, grad_output)
yolov5/utils/activations.py:37
↓ 2 callersFunctionbox_area
(box)
yolov5/utils/general.py:451
↓ 2 callersFunctionbox_iou
Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. Arguments:
yolov5/utils/general.py:438
↓ 2 callersMethodcache_labels
(self, path=Path('./labels.cache'), prefix='')
yolov5/utils/datasets.py:454
↓ 2 callersFunctioncheck_anchor_order
(m)
yolov5/utils/autoanchor.py:11
↓ 2 callersMethodcheck_and_upload_dataset
(self, opt)
yolov5/utils/wandb_logging/wandb_utils.py:115
↓ 2 callersFunctioncheck_imshow
()
yolov5/utils/general.py:147
↓ 2 callersFunctionclip_coords
(boxes, img_shape)
yolov5/utils/general.py:385
↓ 2 callersFunctioncopy_attr
(a, b, include=(), exclude=())
yolov5/utils/torch_utils.py:260
↓ 2 callersMethodcreate_dataset_table
(self, dataset, class_to_id, name='dataset')
yolov5/utils/wandb_logging/wandb_utils.py:228
↓ 2 callersFunctiondetect
(opt)
yolov5/detect.py:18
↓ 2 callersFunctiondetect
(opt)
yolov5/detect_customize.py:18
↓ 2 callersMethoddownload_dataset_artifact
(self, path, alias)
yolov5/utils/wandb_logging/wandb_utils.py:159
↓ 2 callersFunctiondownload_one
(url, dir)
yolov5/utils/general.py:198
↓ 2 callersFunctionemojis
(str='')
yolov5/utils/general.py:68
↓ 2 callersMethodforward_once
(self, x, profile=False)
yolov5/models/yolo.py:132
↓ 2 callersFunctionget_hash
(files)
yolov5/utils/datasets.py:39
↓ 2 callersFunctionget_run_info
(run_path)
yolov5/utils/wandb_logging/wandb_utils.py:34
↓ 2 callersFunctionis_docker
()
yolov5/utils/general.py:54
↓ 2 callersFunctionload_classifier
(name='resnet101', n=2)
yolov5/utils/torch_utils.py:228
↓ 2 callersFunctionload_mosaic
(self, index)
yolov5/utils/datasets.py:674
↓ 2 callersMethodmap_val_table_path
(self)
yolov5/utils/wandb_logging/wandb_utils.py:222
↓ 2 callersMethodnew_video
(self, path)
yolov5/utils/datasets.py:193
↓ 2 callersFunctiontrain
(hyp, opt, device, tb_writer=None)
yolov5/train.py:41
↓ 2 callersMethodupdate
(self, model)
yolov5/utils/torch_utils.py:289
↓ 2 callersFunctionxyn2xy
(x, w=640, h=640, padw=0, padh=0)
yolov5/utils/general.py:335
↓ 1 callersMethod__init__
(self, path, img_size=640, stride=32)
yolov5/utils/datasets.py:123
↓ 1 callersMethod__init__
(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None)
yolov5/models/yolo.py:73
↓ 1 callersMethod__iter__
(self)
yolov5/utils/datasets.py:151
↓ 1 callersMethod_descale_pred
(self, p, flips, scale, img_size)
yolov5/models/yolo.py:155
↓ 1 callersMethod_initialize_biases
(self, cf=None)
yolov5/models/yolo.py:172
↓ 1 callersMethod_make_grid
(nx=20, ny=20)
yolov5/models/yolo.py:67
↓ 1 callersFunctionap_per_class
Compute the average precision, given the recall and precision curves. Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. # Ar
yolov5/utils/metrics.py:18
↓ 1 callersFunctionaugment_hsv
(img, hgain=0.5, sgain=0.5, vgain=0.5)
yolov5/utils/datasets.py:649
↓ 1 callersMethodautoshape
(self)
yolov5/models/yolo.py:218
↓ 1 callersFunctionbbox_ioa
(box1, box2)
yolov5/utils/datasets.py:955
↓ 1 callersFunctionbbox_iou
(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7)
yolov5/utils/general.py:393
↓ 1 callersFunctionbox_candidates
(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16)
yolov5/utils/datasets.py:943
↓ 1 callersMethodbuild_targets
(self, p, targets)
yolov5/utils/loss.py:163
↓ 1 callersFunctionbutter_lowpass
(cutoff, fs, order)
yolov5/utils/plots.py:60
↓ 1 callersFunctioncheck_anchors
(dataset, model, thr=4.0, imgsz=640)
yolov5/utils/autoanchor.py:22
↓ 1 callersFunctioncheck_git_status
()
yolov5/utils/general.py:88
↓ 1 callersFunctioncheck_online
()
yolov5/utils/general.py:78
↓ 1 callersFunctioncheck_wandb_resume
(opt)
yolov5/utils/wandb_logging/wandb_utils.py:42
↓ 1 callersFunctionclean_str
(s)
yolov5/utils/general.py:231
↓ 1 callersFunctioncoco80_to_coco91_class
()
yolov5/utils/general.py:293
↓ 1 callersFunctioncompute_ap
Compute the average precision, given the recall and precision curves # Arguments recall: The recall curve (list) precision: Th
yolov5/utils/metrics.py:81
↓ 1 callersFunctioncreate_dataset_artifact
(opt)
yolov5/utils/wandb_logging/log_dataset.py:10
↓ 1 callersFunctioncreate_folder
(path='./new')
yolov5/utils/datasets.py:997
↓ 1 callersFunctiondate_modified
(path=__file__)
yolov5/utils/torch_utils.py:48
↓ 1 callersMethoddownload_model_artifact
(self, opt)
yolov5/utils/wandb_logging/wandb_utils.py:167
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