updates

.gitignore

@@ -8,17 +8,25 @@
 *.PNG
 *.TIF
 *.HEIC
+*.mp4
+*.mov
+*.MOV
+*.avi
 *.data
-*.cfg
 *.json
-data/*
+
-pycocotools/*
+*.cfg
-!cfg/coco.data
 !cfg/yolov3*.cfg
+
+data/*
 !data/samples/zidane.jpg
 !data/coco.names
 !data/coco_paper.names
+!data/coco.data
+!data/coco_1cls.data
+!data/coco_1img.data
 
+pycocotools/*
 results*.txt
 
 # MATLAB GitIgnore -----------------------------------------------------------------------------------------------------

cfg/coco.data

@@ -1,6 +0,0 @@
-classes=80
-train=../coco/trainvalno5k.txt
-valid=../coco/5k.txt
-names=data/coco.names
-backup=backup/
-eval=coco

detect.py

@@ -106,7 +106,7 @@ def detect(
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
-    parser.add_argument('--data-cfg', type=str, default='cfg/coco.data', help='coco.data file path')
+    parser.add_argument('--data-cfg', type=str, default='data/coco.data', help='coco.data file path')
     parser.add_argument('--weights', type=str, default='weights/yolov3.weights', help='path to weights file')
     parser.add_argument('--images', type=str, default='data/samples', help='path to images')
     parser.add_argument('--img-size', type=int, default=32 * 13, help='size of each image dimension')

test.py

@@ -165,7 +165,7 @@ if __name__ == '__main__':
     parser = argparse.ArgumentParser(prog='test.py')
     parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch')
     parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
-    parser.add_argument('--data-cfg', type=str, default='cfg/example_single_class.data', help='coco.data file path')
+    parser.add_argument('--data-cfg', type=str, default='data/coco.data', help='coco.data file path')
     parser.add_argument('--weights', type=str, default='weights/latesth.pt', help='path to weights file')
     parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
     parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')

train.py

@@ -62,8 +62,8 @@ def train(
             cutoff = load_darknet_weights(model, weights + 'yolov3-tiny.conv.15')
 
     # Transfer learning (train only YOLO layers)
-    # for i, (name, p) in enumerate(model.named_parameters()):
+    # for (name, p) in model.named_parameters():
-    #     p.requires_grad = True if (p.shape[0] == 255) else False
+    #     p.requires_grad = True if p.shape[0] == 255 else False
 
     # Set scheduler (reduce lr at epoch 250)
     scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[250], gamma=0.1, last_epoch=start_epoch - 1)
@@ -205,7 +205,7 @@ if __name__ == '__main__':
     parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch')
     parser.add_argument('--accumulate', type=int, default=1, help='accumulate gradient x batches before optimizing')
     parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
-    parser.add_argument('--data-cfg', type=str, default='cfg/coco.data', help='coco.data file path')
+    parser.add_argument('--data-cfg', type=str, default='data/coco.data', help='coco.data file path')
     parser.add_argument('--multi-scale', action='store_true', help='random image sizes per batch 320 - 608')
     parser.add_argument('--img-size', type=int, default=32 * 13, help='pixels')
     parser.add_argument('--resume', action='store_true', help='resume training flag')