fix annotation txtx file creation

cfg/training/yolov7-tiny.yaml

@@ -1,6 +1,6 @@
 # parameters
-nc: 80  # number of classes
-depth_multiple: 1.0  # model depth multiple
+nc: 2  # number of classes
+depth_multiple: 1.0  # model depth multiple  @@ HK TODO:
 width_multiple: 1.0  # layer channel multiple
 
 # anchors

data/hyp.scratch.tiny.yaml

@@ -14,12 +14,12 @@ 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)
+hsv_h: 0.0  # image HSV-Hue augmentation (fraction)
+hsv_s: 0.0  # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.0  # image HSV-Value augmentation (fraction)
 degrees: 0.0  # image rotation (+/- deg)
-translate: 0.1  # image translation (+/- fraction)
-scale: 0.5  # image scale (+/- gain)
+translate: 0.0  # image translation (+/- fraction)
+scale: 0.0  # 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)

detect.py

@@ -194,3 +194,7 @@ if __name__ == '__main__':
                 strip_optimizer(opt.weights)
         else:
             detect()
+
+"""
+python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg
+"""

train.py

@@ -703,3 +703,8 @@ if __name__ == '__main__':
         plot_evolution(yaml_file)
         print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n'
               f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}')
+
+
+"""
+python train.py --workers 8 --device 'cpu' --batch-size 32 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights 'v7' --name yolov7 --hyp data/hyp.scratch.p5.yaml
+"""

train_aux.py

@@ -558,6 +558,8 @@ if __name__ == '__main__':
     parser.add_argument('--save_period', type=int, default=-1, help='Log model after every "save_period" epoch')
     parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used')
     parser.add_argument('--v5-metric', action='store_true', help='assume maximum recall as 1.0 in AP calculation')
+    parser.add_argument('--tir-od', action='store_true', help='TIR Object Detection')
+
     opt = parser.parse_args()
 
     # Set DDP variables
@@ -697,3 +699,10 @@ if __name__ == '__main__':
         plot_evolution(yaml_file)
         print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n'
               f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}')
+
+
+"""
+python train.py --workers 8 --device 0 --batch-size 32 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml
+
+
+"""

utils/datasets.py

@@ -29,7 +29,7 @@ from torchvision.ops import roi_pool, roi_align, ps_roi_pool, ps_roi_align
 from utils.general import check_requirements, xyxy2xywh, xywh2xyxy, xywhn2xyxy, xyn2xy, segment2box, segments2boxes, \
     resample_segments, clean_str
 from utils.torch_utils import torch_distributed_zero_first
-
+# @@HK :  pip install torch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 resolve h\lib\fbgemm.dll" or one of its dependencies on Windows
 # Parameters
 help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
 img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp', 'mpo']  # acceptable image suffixes
@@ -358,7 +358,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         self.hyp = hyp
         self.image_weights = image_weights
         self.rect = False if image_weights else rect
-        self.mosaic = self.augment and not self.rect  # load 4 images at a time into a mosaic (only during training)
+        self.mosaic = self.augment and not self.rect  # load 4 images at a time into a mosaic (only during training)  @@ HK TODO: disable mosaic
         self.mosaic_border = [-img_size // 2, -img_size // 2]
         self.stride = stride
         self.path = path        
@@ -570,12 +570,13 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         if self.augment:
             # Augment imagespace
             if not mosaic:
-                img, labels = random_perspective(img, labels,
-                                                 degrees=hyp['degrees'],
-                                                 translate=hyp['translate'],
-                                                 scale=hyp['scale'],
-                                                 shear=hyp['shear'],
-                                                 perspective=hyp['perspective'])
+                if not hyp['tir_od']:
+                    img, labels = random_perspective(img, labels,
+                                                     degrees=hyp['degrees'],
+                                                     translate=hyp['translate'],
+                                                     scale=hyp['scale'],
+                                                     shear=hyp['shear'],
+                                                     perspective=hyp['perspective'])
             
             
             #img, labels = self.albumentations(img, labels)