runs

scripts/train_val_generator.py

+import os
+
+root_path = "./data/images/"
+dirs = ["VID_20220208_150059", 'drones_labels']
+
+train_file = "./data/train.txt"
+
+with open(train_file, "w") as file:
+    for dd in dirs:
+        for root_dir, directories, files in os.walk(root_path + dd):
+            for f in files:
+
+                file_path = root_dir + "/" + f
+                file_path = file_path.replace('./data/', './')
+                if str(file_path).endswith('jpg'):
+                    print(file_path)
+                    file.writelines(file_path + '\n')

src/yolov5/data/my-drone.yaml

 
 # 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
-train: images/VID_20220208_150059 # train images (relative to 'path') 118287 images
-val: images/VID_20220208_150059 # train images (relative to 'path') 5000 images
+train: train.txt # train images (relative to 'path') 118287 images
+val: val.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

wandb/run-20220221_193329-32rw10gx/files/config.yaml

@@ -20,7 +20,12 @@ _wandb:
       - 1
       - 2
       - 3
+      2:
+      - 1
+      - 2
+      - 3
       3:
+      - 2
       - 16
       4: 3.8.12
       5: 0.12.10