added detection image and bounding boxes to run on bag

2023-03-21 12:03:10 +05:30
parent a312c2ae7d
commit fc5dbd85d3
145 changed files with 23 additions and 6 deletions
@@ -1,67 +0,0 @@
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
-# Example usage: python train.py --data Argoverse.yaml
-# parent
-# ├── yolov3
-# └── datasets
-#     └── Argoverse  ← downloads here
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/Argoverse  # dataset root dir
-train: Argoverse-1.1/images/train/  # train images (relative to 'path') 39384 images
-val: Argoverse-1.1/images/val/  # val images (relative to 'path') 15062 images
-test: Argoverse-1.1/images/test/  # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
-
-# Classes
-nc: 8  # number of classes
-names: ['person',  'bicycle',  'car',  'motorcycle',  'bus',  'truck',  'traffic_light',  'stop_sign']  # class names
-
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
-  import json
-
-  from tqdm import tqdm
-  from utils.general import download, Path
-
-
-  def argoverse2yolo(set):
-      labels = {}
-      a = json.load(open(set, "rb"))
-      for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv3 format..."):
-          img_id = annot['image_id']
-          img_name = a['images'][img_id]['name']
-          img_label_name = img_name[:-3] + "txt"
-
-          cls = annot['category_id']  # instance class id
-          x_center, y_center, width, height = annot['bbox']
-          x_center = (x_center + width / 2) / 1920.0  # offset and scale
-          y_center = (y_center + height / 2) / 1200.0  # offset and scale
-          width /= 1920.0  # scale
-          height /= 1200.0  # scale
-
-          img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]
-          if not img_dir.exists():
-              img_dir.mkdir(parents=True, exist_ok=True)
-
-          k = str(img_dir / img_label_name)
-          if k not in labels:
-              labels[k] = []
-          labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
-
-      for k in labels:
-          with open(k, "w") as f:
-              f.writelines(labels[k])
-
-
-  # Download
-  dir = Path('../datasets/Argoverse')  # dataset root dir
-  urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
-  download(urls, dir=dir, delete=False)
-
-  # Convert
-  annotations_dir = 'Argoverse-HD/annotations/'
-  (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images')  # rename 'tracking' to 'images'
-  for d in "train.json", "val.json":
-      argoverse2yolo(dir / annotations_dir / d)  # convert VisDrone annotations to YOLO labels
@@ -1,53 +0,0 @@
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# Global Wheat 2020 dataset http://www.global-wheat.com/
-# Example usage: python train.py --data GlobalWheat2020.yaml
-# parent
-# ├── yolov3
-# └── datasets
-#     └── GlobalWheat2020  ← downloads here
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/GlobalWheat2020  # dataset root dir
-train: # train images (relative to 'path') 3422 images
-  - images/arvalis_1
-  - images/arvalis_2
-  - images/arvalis_3
-  - images/ethz_1
-  - images/rres_1
-  - images/inrae_1
-  - images/usask_1
-val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
-  - images/ethz_1
-test: # test images (optional) 1276 images
-  - images/utokyo_1
-  - images/utokyo_2
-  - images/nau_1
-  - images/uq_1
-
-# Classes
-nc: 1  # number of classes
-names: ['wheat_head']  # class names
-
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
-  from utils.general import download, Path
-
-  # Download
-  dir = Path(yaml['path'])  # dataset root dir
-  urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
-          'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
-  download(urls, dir=dir)
-
-  # Make Directories
-  for p in 'annotations', 'images', 'labels':
-      (dir / p).mkdir(parents=True, exist_ok=True)
-
-  # Move
-  for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
-           'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
-      (dir / p).rename(dir / 'images' / p)  # move to /images
-      f = (dir / p).with_suffix('.json')  # json file
-      if f.exists():
-          f.rename((dir / 'annotations' / p).with_suffix('.json'))  # move to /annotations
@@ -1,52 +0,0 @@
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19
-# Example usage: python train.py --data SKU-110K.yaml
-# parent
-# ├── yolov3
-# └── datasets
-#     └── SKU-110K  ← downloads here
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/SKU-110K  # dataset root dir
-train: train.txt  # train images (relative to 'path')  8219 images
-val: val.txt  # val images (relative to 'path')  588 images
-test: test.txt  # test images (optional)  2936 images
-
-# Classes
-nc: 1  # number of classes
-names: ['object']  # class names
-
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
-  import shutil
-  from tqdm import tqdm
-  from utils.general import np, pd, Path, download, xyxy2xywh
-
-  # Download
-  dir = Path(yaml['path'])  # dataset root dir
-  parent = Path(dir.parent)  # download dir
-  urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
-  download(urls, dir=parent, delete=False)
-
-  # Rename directories
-  if dir.exists():
-      shutil.rmtree(dir)
-  (parent / 'SKU110K_fixed').rename(dir)  # rename dir
-  (dir / 'labels').mkdir(parents=True, exist_ok=True)  # create labels dir
-
-  # Convert labels
-  names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height'  # column names
-  for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv':
-      x = pd.read_csv(dir / 'annotations' / d, names=names).values  # annotations
-      images, unique_images = x[:, 0], np.unique(x[:, 0])
-      with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f:
-          f.writelines(f'./images/{s}\n' for s in unique_images)
-      for im in tqdm(unique_images, desc=f'Converting {dir / d}'):
-          cls = 0  # single-class dataset
-          with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f:
-              for r in x[images == im]:
-                  w, h = r[6], r[7]  # image width, height
-                  xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0]  # instance
-                  f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n")  # write label
@@ -1,61 +0,0 @@
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset
-# Example usage: python train.py --data VisDrone.yaml
-# parent
-# ├── yolov3
-# └── datasets
-#     └── VisDrone  ← downloads here
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/VisDrone  # dataset root dir
-train: VisDrone2019-DET-train/images  # train images (relative to 'path')  6471 images
-val: VisDrone2019-DET-val/images  # val images (relative to 'path')  548 images
-test: VisDrone2019-DET-test-dev/images  # test images (optional)  1610 images
-
-# Classes
-nc: 10  # number of classes
-names: ['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor']
-
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
-  from utils.general import download, os, Path
-
-  def visdrone2yolo(dir):
-      from PIL import Image
-      from tqdm import tqdm
-
-      def convert_box(size, box):
-          # Convert VisDrone box to YOLO xywh box
-          dw = 1. / size[0]
-          dh = 1. / size[1]
-          return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
-
-      (dir / 'labels').mkdir(parents=True, exist_ok=True)  # make labels directory
-      pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
-      for f in pbar:
-          img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
-          lines = []
-          with open(f, 'r') as file:  # read annotation.txt
-              for row in [x.split(',') for x in file.read().strip().splitlines()]:
-                  if row[4] == '0':  # VisDrone 'ignored regions' class 0
-                      continue
-                  cls = int(row[5]) - 1
-                  box = convert_box(img_size, tuple(map(int, row[:4])))
-                  lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
-                  with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
-                      fl.writelines(lines)  # write label.txt
-
-
-  # Download
-  dir = Path(yaml['path'])  # dataset root dir
-  urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
-          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
-          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
-          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
-  download(urls, dir=dir)
-
-  # Convert
-  for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
-      visdrone2yolo(dir / d)  # convert VisDrone annotations to YOLO labels
@@ -1,44 +0,0 @@
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# COCO 2017 dataset http://cocodataset.org
-# Example usage: python train.py --data coco.yaml
-# parent
-# ├── yolov3
-# └── datasets
-#     └── coco  ← downloads here
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/coco  # dataset root dir
-train: train2017.txt  # train images (relative to 'path') 118287 images
-val: val2017.txt  # train images (relative to 'path') 5000 images
-test: test-dev2017.txt  # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
-
-# Classes
-nc: 80  # number of classes
-names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
-        'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
-        'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
-        'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
-        'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
-        'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
-        'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
-        'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
-        'hair drier', 'toothbrush']  # class names
-
-
-# Download script/URL (optional)
-download: |
-  from utils.general import download, Path
-
-  # Download labels
-  segments = False  # segment or box labels
-  dir = Path(yaml['path'])  # dataset root dir
-  url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
-  urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')]  # labels
-  download(urls, dir=dir.parent)
-
-  # Download data
-  urls = ['http://images.cocodataset.org/zips/train2017.zip',  # 19G, 118k images
-          'http://images.cocodataset.org/zips/val2017.zip',  # 1G, 5k images
-          'http://images.cocodataset.org/zips/test2017.zip']  # 7G, 41k images (optional)
-  download(urls, dir=dir / 'images', threads=3)
@@ -1,101 +0,0 @@
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
-# Example usage: python train.py --data coco128.yaml
-# parent
-# ├── yolov5
-# └── datasets
-#     └── coco128-seg  ← downloads here (7 MB)
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/coco128-seg  # dataset root dir
-train: images/train2017  # train images (relative to 'path') 128 images
-val: images/train2017  # val images (relative to 'path') 128 images
-test:  # test images (optional)
-
-# Classes
-names:
-  0: person
-  1: bicycle
-  2: car
-  3: motorcycle
-  4: airplane
-  5: bus
-  6: train
-  7: truck
-  8: boat
-  9: traffic light
-  10: fire hydrant
-  11: stop sign
-  12: parking meter
-  13: bench
-  14: bird
-  15: cat
-  16: dog
-  17: horse
-  18: sheep
-  19: cow
-  20: elephant
-  21: bear
-  22: zebra
-  23: giraffe
-  24: backpack
-  25: umbrella
-  26: handbag
-  27: tie
-  28: suitcase
-  29: frisbee
-  30: skis
-  31: snowboard
-  32: sports ball
-  33: kite
-  34: baseball bat
-  35: baseball glove
-  36: skateboard
-  37: surfboard
-  38: tennis racket
-  39: bottle
-  40: wine glass
-  41: cup
-  42: fork
-  43: knife
-  44: spoon
-  45: bowl
-  46: banana
-  47: apple
-  48: sandwich
-  49: orange
-  50: broccoli
-  51: carrot
-  52: hot dog
-  53: pizza
-  54: donut
-  55: cake
-  56: chair
-  57: couch
-  58: potted plant
-  59: bed
-  60: dining table
-  61: toilet
-  62: tv
-  63: laptop
-  64: mouse
-  65: remote
-  66: keyboard
-  67: cell phone
-  68: microwave
-  69: oven
-  70: toaster
-  71: sink
-  72: refrigerator
-  73: book
-  74: clock
-  75: vase
-  76: scissors
-  77: teddy bear
-  78: hair drier
-  79: toothbrush
-
-
-# Download script/URL (optional)
-download: https://ultralytics.com/assets/coco128-seg.zip
@@ -1,30 +0,0 @@
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
-# Example usage: python train.py --data coco128.yaml
-# parent
-# ├── yolov3
-# └── datasets
-#     └── coco128  ← downloads here
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/coco128  # dataset root dir
-train: images/train2017  # train images (relative to 'path') 128 images
-val: images/train2017  # val images (relative to 'path') 128 images
-test:  # test images (optional)
-
-# Classes
-nc: 80  # number of classes
-names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
-        'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
-        'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
-        'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
-        'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
-        'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
-        'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
-        'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
-        'hair drier', 'toothbrush']  # class names
-
-
-# Download script/URL (optional)
-download: https://ultralytics.com/assets/coco128.zip
@@ -1,18 +0,0 @@
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# COCO 2017 dataset http://cocodataset.org
-# Example usage: python train.py --data coco.yaml
-# parent
-# ├── yolov3
-# └── datasets
-#     └── coco  ← downloads here
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../pipe-dataset/  # dataset root dir
-train:  train/images # train images (relative to 'path') 118287 images
-val: val/images  # train images (relative to 'path') 5000 images
-# test: test-dev2017.txt  # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
-
-# Classes
-nc: 1  # number of classes
-names: ['pipe']  # class names
@@ -1,34 +0,0 @@
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# Hyperparameters for Objects365 training
-# python train.py --weights yolov5m.pt --data Objects365.yaml --evolve
-# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
-
-lr0: 0.00258
-lrf: 0.17
-momentum: 0.779
-weight_decay: 0.00058
-warmup_epochs: 1.33
-warmup_momentum: 0.86
-warmup_bias_lr: 0.0711
-box: 0.0539
-cls: 0.299
-cls_pw: 0.825
-obj: 0.632
-obj_pw: 1.0
-iou_t: 0.2
-anchor_t: 3.44
-anchors: 3.2
-fl_gamma: 0.0
-hsv_h: 0.0188
-hsv_s: 0.704
-hsv_v: 0.36
-degrees: 0.0
-translate: 0.0902
-scale: 0.491
-shear: 0.0
-perspective: 0.0
-flipud: 0.0
-fliplr: 0.5
-mosaic: 1.0
-mixup: 0.0
-copy_paste: 0.0
@@ -1,40 +0,0 @@
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# Hyperparameters for VOC training
-# python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve
-# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
-
-# YOLOv5 Hyperparameter Evolution Results
-# Best generation: 467
-# Last generation: 996
-#    metrics/precision,       metrics/recall,      metrics/mAP_0.5, metrics/mAP_0.5:0.95,         val/box_loss,         val/obj_loss,         val/cls_loss
-#              0.87729,              0.85125,              0.91286,              0.72664,            0.0076739,            0.0042529,            0.0013865
-
-lr0: 0.00334
-lrf: 0.15135
-momentum: 0.74832
-weight_decay: 0.00025
-warmup_epochs: 3.3835
-warmup_momentum: 0.59462
-warmup_bias_lr: 0.18657
-box: 0.02
-cls: 0.21638
-cls_pw: 0.5
-obj: 0.51728
-obj_pw: 0.67198
-iou_t: 0.2
-anchor_t: 3.3744
-fl_gamma: 0.0
-hsv_h: 0.01041
-hsv_s: 0.54703
-hsv_v: 0.27739
-degrees: 0.0
-translate: 0.04591
-scale: 0.75544
-shear: 0.0
-perspective: 0.0
-flipud: 0.0
-fliplr: 0.5
-mosaic: 0.85834
-mixup: 0.04266
-copy_paste: 0.0
-anchors: 3.412
@@ -1,35 +0,0 @@
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# Hyperparameters when using Albumentations frameworks
-# python train.py --hyp hyp.no-augmentation.yaml
-# See https://github.com/ultralytics/yolov5/pull/3882 for YOLOv5 + Albumentations Usage examples
-
-lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
-lrf: 0.1  # final OneCycleLR learning rate (lr0 * lrf)
-momentum: 0.937  # SGD momentum/Adam beta1
-weight_decay: 0.0005  # optimizer weight decay 5e-4
-warmup_epochs: 3.0  # warmup epochs (fractions ok)
-warmup_momentum: 0.8  # warmup initial momentum
-warmup_bias_lr: 0.1  # warmup initial bias lr
-box: 0.05  # box loss gain
-cls: 0.3  # cls loss gain
-cls_pw: 1.0  # cls BCELoss positive_weight
-obj: 0.7  # obj loss gain (scale with pixels)
-obj_pw: 1.0  # obj BCELoss positive_weight
-iou_t: 0.20  # IoU training threshold
-anchor_t: 4.0  # anchor-multiple threshold
-# anchors: 3  # anchors per output layer (0 to ignore)
-# this parameters are all zero since we want to use albumentation framework
-fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
-hsv_h: 0  # image HSV-Hue augmentation (fraction)
-hsv_s: 00  # image HSV-Saturation augmentation (fraction)
-hsv_v: 0  # image HSV-Value augmentation (fraction)
-degrees: 0.0  # image rotation (+/- deg)
-translate: 0  # image translation (+/- fraction)
-scale: 0  # image scale (+/- gain)
-shear: 0  # image shear (+/- deg)
-perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
-flipud: 0.0  # image flip up-down (probability)
-fliplr: 0.0  # image flip left-right (probability)
-mosaic: 0.0  # image mosaic (probability)
-mixup: 0.0  # image mixup (probability)
-copy_paste: 0.0  # segment copy-paste (probability)
@@ -1,34 +0,0 @@
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# Hyperparameters for high-augmentation COCO training from scratch
-# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
-# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
-
-lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
-lrf: 0.2  # final OneCycleLR learning rate (lr0 * lrf)
-momentum: 0.937  # SGD momentum/Adam beta1
-weight_decay: 0.0005  # optimizer weight decay 5e-4
-warmup_epochs: 3.0  # warmup epochs (fractions ok)
-warmup_momentum: 0.8  # warmup initial momentum
-warmup_bias_lr: 0.1  # warmup initial bias lr
-box: 0.05  # box loss gain
-cls: 0.3  # cls loss gain
-cls_pw: 1.0  # cls BCELoss positive_weight
-obj: 0.7  # obj loss gain (scale with pixels)
-obj_pw: 1.0  # obj BCELoss positive_weight
-iou_t: 0.20  # IoU training threshold
-anchor_t: 4.0  # anchor-multiple threshold
-# anchors: 3  # anchors per output layer (0 to ignore)
-fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
-hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
-hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
-hsv_v: 0.4  # image HSV-Value augmentation (fraction)
-degrees: 0.0  # image rotation (+/- deg)
-translate: 0.1  # image translation (+/- fraction)
-scale: 0.9  # image scale (+/- gain)
-shear: 0.0  # image shear (+/- deg)
-perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
-flipud: 0.0  # image flip up-down (probability)
-fliplr: 0.5  # image flip left-right (probability)
-mosaic: 1.0  # image mosaic (probability)
-mixup: 0.1  # image mixup (probability)
-copy_paste: 0.1  # segment copy-paste (probability)
@@ -1,34 +0,0 @@
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# Hyperparameters for low-augmentation COCO training from scratch
-# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
-# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
-
-lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
-lrf: 0.01  # final OneCycleLR learning rate (lr0 * lrf)
-momentum: 0.937  # SGD momentum/Adam beta1
-weight_decay: 0.0005  # optimizer weight decay 5e-4
-warmup_epochs: 3.0  # warmup epochs (fractions ok)
-warmup_momentum: 0.8  # warmup initial momentum
-warmup_bias_lr: 0.1  # warmup initial bias lr
-box: 0.05  # box loss gain
-cls: 0.5  # cls loss gain
-cls_pw: 1.0  # cls BCELoss positive_weight
-obj: 1.0  # obj loss gain (scale with pixels)
-obj_pw: 1.0  # obj BCELoss positive_weight
-iou_t: 0.20  # IoU training threshold
-anchor_t: 4.0  # anchor-multiple threshold
-# anchors: 3  # anchors per output layer (0 to ignore)
-fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
-hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
-hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
-hsv_v: 0.4  # image HSV-Value augmentation (fraction)
-degrees: 0.0  # image rotation (+/- deg)
-translate: 0.1  # image translation (+/- fraction)
-scale: 0.5  # image scale (+/- gain)
-shear: 0.0  # image shear (+/- deg)
-perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
-flipud: 0.0  # image flip up-down (probability)
-fliplr: 0.5  # image flip left-right (probability)
-mosaic: 1.0  # image mosaic (probability)
-mixup: 0.0  # image mixup (probability)
-copy_paste: 0.0  # segment copy-paste (probability)
@@ -1,34 +0,0 @@
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# Hyperparameters for medium-augmentation COCO training from scratch
-# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
-# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
-
-lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
-lrf: 0.1  # final OneCycleLR learning rate (lr0 * lrf)
-momentum: 0.937  # SGD momentum/Adam beta1
-weight_decay: 0.0005  # optimizer weight decay 5e-4
-warmup_epochs: 3.0  # warmup epochs (fractions ok)
-warmup_momentum: 0.8  # warmup initial momentum
-warmup_bias_lr: 0.1  # warmup initial bias lr
-box: 0.05  # box loss gain
-cls: 0.3  # cls loss gain
-cls_pw: 1.0  # cls BCELoss positive_weight
-obj: 0.7  # obj loss gain (scale with pixels)
-obj_pw: 1.0  # obj BCELoss positive_weight
-iou_t: 0.20  # IoU training threshold
-anchor_t: 4.0  # anchor-multiple threshold
-# anchors: 3  # anchors per output layer (0 to ignore)
-fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
-hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
-hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
-hsv_v: 0.4  # image HSV-Value augmentation (fraction)
-degrees: 0.0  # image rotation (+/- deg)
-translate: 0.1  # image translation (+/- fraction)
-scale: 0.9  # image scale (+/- gain)
-shear: 0.0  # image shear (+/- deg)
-perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
-flipud: 0.0  # image flip up-down (probability)
-fliplr: 0.5  # image flip left-right (probability)
-mosaic: 1.0  # image mosaic (probability)
-mixup: 0.1  # image mixup (probability)
-copy_paste: 0.0  # segment copy-paste (probability)
@@ -1,34 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Hyperparameters for COCO training from scratch
-# python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300
-# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
-
-lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
-lrf: 0.1  # final OneCycleLR learning rate (lr0 * lrf)
-momentum: 0.937  # SGD momentum/Adam beta1
-weight_decay: 0.0005  # optimizer weight decay 5e-4
-warmup_epochs: 3.0  # warmup epochs (fractions ok)
-warmup_momentum: 0.8  # warmup initial momentum
-warmup_bias_lr: 0.1  # warmup initial bias lr
-box: 0.05  # box loss gain
-cls: 0.5  # cls loss gain
-cls_pw: 1.0  # cls BCELoss positive_weight
-obj: 1.0  # obj loss gain (scale with pixels)
-obj_pw: 1.0  # obj BCELoss positive_weight
-iou_t: 0.20  # IoU training threshold
-anchor_t: 4.0  # anchor-multiple threshold
-# anchors: 3  # anchors per output layer (0 to ignore)
-fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
-hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
-hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
-hsv_v: 0.4  # image HSV-Value augmentation (fraction)
-degrees: 0.0  # image rotation (+/- deg)
-translate: 0.1  # image translation (+/- fraction)
-scale: 0.5  # image scale (+/- gain)
-shear: 0.0  # image shear (+/- deg)
-perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
-flipud: 0.0  # image flip up-down (probability)
-fliplr: 0.5  # image flip left-right (probability)
-mosaic: 1.0  # image mosaic (probability)
-mixup: 0.0  # image mixup (probability)
-copy_paste: 0.0  # segment copy-paste (probability)
@@ -1,112 +0,0 @@
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# Objects365 dataset https://www.objects365.org/
-# Example usage: python train.py --data Objects365.yaml
-# parent
-# ├── yolov3
-# └── datasets
-#     └── Objects365  ← downloads here
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/Objects365  # dataset root dir
-train: images/train  # train images (relative to 'path') 1742289 images
-val: images/val # val images (relative to 'path') 80000 images
-test:  # test images (optional)
-
-# Classes
-nc: 365  # number of classes
-names: ['Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Glasses', 'Bottle', 'Desk', 'Cup',
-        'Street Lights', 'Cabinet/shelf', 'Handbag/Satchel', 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet', 'Book',
-        'Gloves', 'Storage box', 'Boat', 'Leather Shoes', 'Flower', 'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag',
-        'Pillow', 'Boots', 'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass', 'Belt', 'Monitor/TV',
-        'Backpack', 'Umbrella', 'Traffic Light', 'Speaker', 'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle',
-        'Stool', 'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Basket', 'Drum', 'Pen/Pencil', 'Bus', 'Wild Bird',
-        'High Heels', 'Motorcycle', 'Guitar', 'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck',
-        'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy', 'Candle', 'Sailboat', 'Laptop', 'Awning',
-        'Bed', 'Faucet', 'Tent', 'Horse', 'Mirror', 'Power outlet', 'Sink', 'Apple', 'Air Conditioner', 'Knife',
-        'Hockey Stick', 'Paddle', 'Pickup Truck', 'Fork', 'Traffic Sign', 'Balloon', 'Tripod', 'Dog', 'Spoon', 'Clock',
-        'Pot', 'Cow', 'Cake', 'Dinning Table', 'Sheep', 'Hanger', 'Blackboard/Whiteboard', 'Napkin', 'Other Fish',
-        'Orange/Tangerine', 'Toiletry', 'Keyboard', 'Tomato', 'Lantern', 'Machinery Vehicle', 'Fan',
-        'Green Vegetables', 'Banana', 'Baseball Glove', 'Airplane', 'Mouse', 'Train', 'Pumpkin', 'Soccer', 'Skiboard',
-        'Luggage', 'Nightstand', 'Tea pot', 'Telephone', 'Trolley', 'Head Phone', 'Sports Car', 'Stop Sign',
-        'Dessert', 'Scooter', 'Stroller', 'Crane', 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck', 'Baseball Bat',
-        'Surveillance Camera', 'Cat', 'Jug', 'Broccoli', 'Piano', 'Pizza', 'Elephant', 'Skateboard', 'Surfboard',
-        'Gun', 'Skating and Skiing shoes', 'Gas stove', 'Donut', 'Bow Tie', 'Carrot', 'Toilet', 'Kite', 'Strawberry',
-        'Other Balls', 'Shovel', 'Pepper', 'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks',
-        'Microwave', 'Pigeon', 'Baseball', 'Cutting/chopping Board', 'Coffee Table', 'Side Table', 'Scissors',
-        'Marker', 'Pie', 'Ladder', 'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball', 'Zebra', 'Grape',
-        'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin', 'Egg', 'Fire Extinguisher', 'Candy', 'Fire Truck',
-        'Billiards', 'Converter', 'Bathtub', 'Wheelchair', 'Golf Club', 'Briefcase', 'Cucumber', 'Cigar/Cigarette',
-        'Paint Brush', 'Pear', 'Heavy Truck', 'Hamburger', 'Extractor', 'Extension Cord', 'Tong', 'Tennis Racket',
-        'Folder', 'American Football', 'earphone', 'Mask', 'Kettle', 'Tennis', 'Ship', 'Swing', 'Coffee Machine',
-        'Slide', 'Carriage', 'Onion', 'Green beans', 'Projector', 'Frisbee', 'Washing Machine/Drying Machine',
-        'Chicken', 'Printer', 'Watermelon', 'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', 'Hot-air balloon',
-        'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog', 'Blender', 'Peach', 'Rice', 'Wallet/Purse',
-        'Volleyball', 'Deer', 'Goose', 'Tape', 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple', 'Golf Ball',
-        'Ambulance', 'Parking meter', 'Mango', 'Key', 'Hurdle', 'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin',
-        'Megaphone', 'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion', 'Sandwich', 'Nuts',
-        'Speed Limit Sign', 'Induction Cooker', 'Broom', 'Trombone', 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit',
-        'Router/modem', 'Poker Card', 'Toaster', 'Shrimp', 'Sushi', 'Cheese', 'Notepaper', 'Cherry', 'Pliers', 'CD',
-        'Pasta', 'Hammer', 'Cue', 'Avocado', 'Hamimelon', 'Flask', 'Mushroom', 'Screwdriver', 'Soap', 'Recorder',
-        'Bear', 'Eggplant', 'Board Eraser', 'Coconut', 'Tape Measure/Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips',
-        'Steak', 'Crosswalk Sign', 'Stapler', 'Camel', 'Formula 1', 'Pomegranate', 'Dishwasher', 'Crab',
-        'Hoverboard', 'Meat ball', 'Rice Cooker', 'Tuba', 'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal',
-        'Butterfly', 'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill', 'Hair Dryer', 'Egg tart',
-        'Jellyfish', 'Treadmill', 'Lighter', 'Grapefruit', 'Game board', 'Mop', 'Radish', 'Baozi', 'Target', 'French',
-        'Spring Rolls', 'Monkey', 'Rabbit', 'Pencil Case', 'Yak', 'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell',
-        'Scallop', 'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Tennis paddle', 'Cosmetics Brush/Eyeliner Pencil',
-        'Chainsaw', 'Eraser', 'Lobster', 'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling', 'Table Tennis']
-
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
-  from pycocotools.coco import COCO
-  from tqdm import tqdm
-
-  from utils.general import Path, download, np, xyxy2xywhn
-
-  # Make Directories
-  dir = Path(yaml['path'])  # dataset root dir
-  for p in 'images', 'labels':
-      (dir / p).mkdir(parents=True, exist_ok=True)
-      for q in 'train', 'val':
-          (dir / p / q).mkdir(parents=True, exist_ok=True)
-
-  # Train, Val Splits
-  for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
-      print(f"Processing {split} in {patches} patches ...")
-      images, labels = dir / 'images' / split, dir / 'labels' / split
-
-      # Download
-      url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
-      if split == 'train':
-          download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False)  # annotations json
-          download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8)
-      elif split == 'val':
-          download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False)  # annotations json
-          download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8)
-          download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8)
-
-      # Move
-      for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
-          f.rename(images / f.name)  # move to /images/{split}
-
-      # Labels
-      coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
-      names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
-      for cid, cat in enumerate(names):
-          catIds = coco.getCatIds(catNms=[cat])
-          imgIds = coco.getImgIds(catIds=catIds)
-          for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
-              width, height = im["width"], im["height"]
-              path = Path(im["file_name"])  # image filename
-              try:
-                  with open(labels / path.with_suffix('.txt').name, 'a') as file:
-                      annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
-                      for a in coco.loadAnns(annIds):
-                          x, y, w, h = a['bbox']  # bounding box in xywh (xy top-left corner)
-                          xyxy = np.array([x, y, x + w, y + h])[None]  # pixels(1,4)
-                          x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0]  # normalized and clipped
-                          file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
-              except Exception as e:
-                  print(e)
@@ -1,18 +0,0 @@
-#!/bin/bash
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# Download latest models from https://github.com/ultralytics/yolov3/releases
-# Example usage: bash path/to/download_weights.sh
-# parent
-# └── yolov3
-#     ├── yolov3.pt  ← downloads here
-#     ├── yolov3-spp.pt
-#     └── ...
-
-python - <<EOF
-from utils.downloads import attempt_download
-
-models = ['yolov3', 'yolov3-spp', 'yolov3-tiny']
-for x in models:
-    attempt_download(f'{x}.pt')
-
-EOF
@@ -1,27 +0,0 @@
-#!/bin/bash
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# Download COCO 2017 dataset http://cocodataset.org
-# Example usage: bash data/scripts/get_coco.sh
-# parent
-# ├── yolov3
-# └── datasets
-#     └── coco  ← downloads here
-
-# Download/unzip labels
-d='../datasets' # unzip directory
-url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
-f='coco2017labels.zip' # or 'coco2017labels-segments.zip', 68 MB
-echo 'Downloading' $url$f ' ...'
-curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
-
-# Download/unzip images
-d='../datasets/coco/images' # unzip directory
-url=http://images.cocodataset.org/zips/
-f1='train2017.zip' # 19G, 118k images
-f2='val2017.zip'   # 1G, 5k images
-f3='test2017.zip'  # 7G, 41k images (optional)
-for f in $f1 $f2; do
-  echo 'Downloading' $url$f '...'
-  curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
-done
-wait # finish background tasks
@@ -1,17 +0,0 @@
-#!/bin/bash
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
-# Example usage: bash data/scripts/get_coco128.sh
-# parent
-# ├── yolov3
-# └── datasets
-#     └── coco128  ← downloads here
-
-# Download/unzip images and labels
-d='../datasets' # unzip directory
-url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
-f='coco128.zip' # or 'coco128-segments.zip', 68 MB
-echo 'Downloading' $url$f ' ...'
-curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
-
-wait # finish background tasks
@@ -1,51 +0,0 @@
-#!/bin/bash
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# Download ILSVRC2012 ImageNet dataset https://image-net.org
-# Example usage: bash data/scripts/get_imagenet.sh
-# parent
-# ├── yolov5
-# └── datasets
-#     └── imagenet  ← downloads here
-
-# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
-if [ "$#" -gt 0 ]; then
-  for opt in "$@"; do
-    case "${opt}" in
-    --train) train=true ;;
-    --val) val=true ;;
-    esac
-  done
-else
-  train=true
-  val=true
-fi
-
-# Make dir
-d='../datasets/imagenet' # unzip directory
-mkdir -p $d && cd $d
-
-# Download/unzip train
-if [ "$train" == "true" ]; then
-  wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_train.tar # download 138G, 1281167 images
-  mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
-  tar -xf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
-  find . -name "*.tar" | while read NAME; do
-    mkdir -p "${NAME%.tar}"
-    tar -xf "${NAME}" -C "${NAME%.tar}"
-    rm -f "${NAME}"
-  done
-  cd ..
-fi
-
-# Download/unzip val
-if [ "$val" == "true" ]; then
-  wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar # download 6.3G, 50000 images
-  mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xf ILSVRC2012_img_val.tar
-  wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash # move into subdirs
-fi
-
-# Delete corrupted image (optional: PNG under JPEG name that may cause dataloaders to fail)
-# rm train/n04266014/n04266014_10835.JPEG
-
-# TFRecords (optional)
-# wget https://raw.githubusercontent.com/tensorflow/models/master/research/slim/datasets/imagenet_lsvrc_2015_synsets.txt
@@ -1,80 +0,0 @@
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC
-# Example usage: python train.py --data VOC.yaml
-# parent
-# ├── yolov3
-# └── datasets
-#     └── VOC  ← downloads here
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/VOC
-train: # train images (relative to 'path')  16551 images
-  - images/train2012
-  - images/train2007
-  - images/val2012
-  - images/val2007
-val: # val images (relative to 'path')  4952 images
-  - images/test2007
-test: # test images (optional)
-  - images/test2007
-
-# Classes
-nc: 20  # number of classes
-names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
-        'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']  # class names
-
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
-  import xml.etree.ElementTree as ET
-
-  from tqdm import tqdm
-  from utils.general import download, Path
-
-
-  def convert_label(path, lb_path, year, image_id):
-      def convert_box(size, box):
-          dw, dh = 1. / size[0], 1. / size[1]
-          x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
-          return x * dw, y * dh, w * dw, h * dh
-
-      in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')
-      out_file = open(lb_path, 'w')
-      tree = ET.parse(in_file)
-      root = tree.getroot()
-      size = root.find('size')
-      w = int(size.find('width').text)
-      h = int(size.find('height').text)
-
-      for obj in root.iter('object'):
-          cls = obj.find('name').text
-          if cls in yaml['names'] and not int(obj.find('difficult').text) == 1:
-              xmlbox = obj.find('bndbox')
-              bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
-              cls_id = yaml['names'].index(cls)  # class id
-              out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n')
-
-
-  # Download
-  dir = Path(yaml['path'])  # dataset root dir
-  url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
-  urls = [url + 'VOCtrainval_06-Nov-2007.zip',  # 446MB, 5012 images
-          url + 'VOCtest_06-Nov-2007.zip',  # 438MB, 4953 images
-          url + 'VOCtrainval_11-May-2012.zip']  # 1.95GB, 17126 images
-  download(urls, dir=dir / 'images', delete=False)
-
-  # Convert
-  path = dir / f'images/VOCdevkit'
-  for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
-      imgs_path = dir / 'images' / f'{image_set}{year}'
-      lbs_path = dir / 'labels' / f'{image_set}{year}'
-      imgs_path.mkdir(exist_ok=True, parents=True)
-      lbs_path.mkdir(exist_ok=True, parents=True)
-
-      image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').read().strip().split()
-      for id in tqdm(image_ids, desc=f'{image_set}{year}'):
-          f = path / f'VOC{year}/JPEGImages/{id}.jpg'  # old img path
-          lb_path = (lbs_path / f.name).with_suffix('.txt')  # new label path
-          f.rename(imgs_path / f.name)  # move image
-          convert_label(path, lb_path, year, id)  # convert labels to YOLO format
@@ -1,102 +0,0 @@
-# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
-# xView 2018 dataset https://challenge.xviewdataset.org
-# --------  DOWNLOAD DATA MANUALLY from URL above and unzip to 'datasets/xView' before running train command!  --------
-# Example usage: python train.py --data xView.yaml
-# parent
-# ├── yolov3
-# └── datasets
-#     └── xView  ← downloads here
-
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/xView  # dataset root dir
-train: images/autosplit_train.txt  # train images (relative to 'path') 90% of 847 train images
-val: images/autosplit_val.txt  # train images (relative to 'path') 10% of 847 train images
-
-# Classes
-nc: 60  # number of classes
-names: ['Fixed-wing Aircraft', 'Small Aircraft', 'Cargo Plane', 'Helicopter', 'Passenger Vehicle', 'Small Car', 'Bus',
-        'Pickup Truck', 'Utility Truck', 'Truck', 'Cargo Truck', 'Truck w/Box', 'Truck Tractor', 'Trailer',
-        'Truck w/Flatbed', 'Truck w/Liquid', 'Crane Truck', 'Railway Vehicle', 'Passenger Car', 'Cargo Car',
-        'Flat Car', 'Tank car', 'Locomotive', 'Maritime Vessel', 'Motorboat', 'Sailboat', 'Tugboat', 'Barge',
-        'Fishing Vessel', 'Ferry', 'Yacht', 'Container Ship', 'Oil Tanker', 'Engineering Vehicle', 'Tower crane',
-        'Container Crane', 'Reach Stacker', 'Straddle Carrier', 'Mobile Crane', 'Dump Truck', 'Haul Truck',
-        'Scraper/Tractor', 'Front loader/Bulldozer', 'Excavator', 'Cement Mixer', 'Ground Grader', 'Hut/Tent', 'Shed',
-        'Building', 'Aircraft Hangar', 'Damaged Building', 'Facility', 'Construction Site', 'Vehicle Lot', 'Helipad',
-        'Storage Tank', 'Shipping container lot', 'Shipping Container', 'Pylon', 'Tower']  # class names
-
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
-  import json
-  import os
-  from pathlib import Path
-
-  import numpy as np
-  from PIL import Image
-  from tqdm import tqdm
-
-  from utils.datasets import autosplit
-  from utils.general import download, xyxy2xywhn
-
-
-  def convert_labels(fname=Path('xView/xView_train.geojson')):
-      # Convert xView geoJSON labels to YOLO format
-      path = fname.parent
-      with open(fname) as f:
-          print(f'Loading {fname}...')
-          data = json.load(f)
-
-      # Make dirs
-      labels = Path(path / 'labels' / 'train')
-      os.system(f'rm -rf {labels}')
-      labels.mkdir(parents=True, exist_ok=True)
-
-      # xView classes 11-94 to 0-59
-      xview_class2index = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 1, 2, -1, 3, -1, 4, 5, 6, 7, 8, -1, 9, 10, 11,
-                           12, 13, 14, 15, -1, -1, 16, 17, 18, 19, 20, 21, 22, -1, 23, 24, 25, -1, 26, 27, -1, 28, -1,
-                           29, 30, 31, 32, 33, 34, 35, 36, 37, -1, 38, 39, 40, 41, 42, 43, 44, 45, -1, -1, -1, -1, 46,
-                           47, 48, 49, -1, 50, 51, -1, 52, -1, -1, -1, 53, 54, -1, 55, -1, -1, 56, -1, 57, -1, 58, 59]
-
-      shapes = {}
-      for feature in tqdm(data['features'], desc=f'Converting {fname}'):
-          p = feature['properties']
-          if p['bounds_imcoords']:
-              id = p['image_id']
-              file = path / 'train_images' / id
-              if file.exists():  # 1395.tif missing
-                  try:
-                      box = np.array([int(num) for num in p['bounds_imcoords'].split(",")])
-                      assert box.shape[0] == 4, f'incorrect box shape {box.shape[0]}'
-                      cls = p['type_id']
-                      cls = xview_class2index[int(cls)]  # xView class to 0-60
-                      assert 59 >= cls >= 0, f'incorrect class index {cls}'
-
-                      # Write YOLO label
-                      if id not in shapes:
-                          shapes[id] = Image.open(file).size
-                      box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True)
-                      with open((labels / id).with_suffix('.txt'), 'a') as f:
-                          f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n")  # write label.txt
-                  except Exception as e:
-                      print(f'WARNING: skipping one label for {file}: {e}')
-
-
-  # Download manually from https://challenge.xviewdataset.org
-  dir = Path(yaml['path'])  # dataset root dir
-  # urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip',  # train labels
-  #         'https://d307kc0mrhucc3.cloudfront.net/train_images.zip',  # 15G, 847 train images
-  #         'https://d307kc0mrhucc3.cloudfront.net/val_images.zip']  # 5G, 282 val images (no labels)
-  # download(urls, dir=dir, delete=False)
-
-  # Convert labels
-  convert_labels(dir / 'xView_train.geojson')
-
-  # Move images
-  images = Path(dir / 'images')
-  images.mkdir(parents=True, exist_ok=True)
-  Path(dir / 'train_images').rename(dir / 'images' / 'train')
-  Path(dir / 'val_images').rename(dir / 'images' / 'val')
-
-  # Split
-  autosplit(dir / 'images' / 'train')