init

.gitignore

@@ -1,2 +1,7 @@
 private
 .DS_Store
+LICENSE
+data
+wdata
+yolov5l.pt
+env

README.md

@@ -22,7 +22,7 @@ YOLTv5 is built to execute on a GPU-enabled machine.
 
 	# update with geo packages
 	conda install -c conda-forge gdal
-	conda install -c conda-forge osmnx=0.12 
+	conda install -c conda-forge osmnx==0.12 
 	conda install  -c conda-forge scikit-image
 	conda install  -c conda-forge statsmodels
 	pip install torchsummary
@@ -36,8 +36,7 @@ ___
 
 Training preparation is accomplished via [prep_train.py](https://github.com/avanetten/yoltv5/blob/main/yoltv5/prep_train.py).  To train a model, run:
 
-	cd /yoltv5
-    python yolov5/train.py --img 640 --batch 16 --epochs 100 --data yoltv5_train_vehicles_8cat.yaml --weights yolov5l.pt
+    python yoltv5/yolov5/train.py --img 512 --batch 16 --epochs 100 --data configs/yoltv5_rareplanes_train_roles.yaml --weights yolov5l.pt
 
 ___
 

configs/yoltv5_rareplanes_train_roles.yaml

@@ -0,0 +1,9 @@
+# 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: /home/sait/yoltv5/wdata/  # dataset root dir
+train: train/images/  # train images (relative to 'path')
+val: valid/images/  # val images (relative to 'path')
+test:  # test images (optional)
+
+# Classes
+nc: 7  # number of classes
+names: ['Small Civil Transport/Utility', 'Medium Civil Transport/Utility', 'Large Civil Transport/Utility', 'Military Transport/Utility/AWAC', 'Military Bomber', 'Military Fighter/Interceptor/Attack', 'Military Trainer']

configs/yoltv5_train_vehicles_8cat.yaml

@@ -1,5 +1,5 @@
 # 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: /data/  # dataset root dir
+path: /wdata/  # dataset root dir
 train: train/images  # train images (relative to 'path')
 val: val/images  # val images (relative to 'path')
 test:  # test images (optional)