An error occurred during YOLOX training

[SliverAward]

Initialize weights: model/backbone/backbone.pth
Starting training for 100 epochs...
0%| | 0/33 [00:15<?, ?it/s]
Traceback (most recent call last):
File "C:\Yolo-FastestV2\train.py", line 99, in
for imgs, targets in pbar:
File "C:\ProgramData\Anaconda3\lib\site-packages\tqdm\std.py", line 1195, in iter
for obj in iterable:
File "C:\Users\ESP\AppData\Roaming\Python\Python39\site-packages\torch\utils\data\dataloader.py", line 634, in next
data = self._next_data()
File "C:\Users\ESP\AppData\Roaming\Python\Python39\site-packages\torch\utils\data\dataloader.py", line 1346, in _next_data
return self._process_data(data)
File "C:\Users\ESP\AppData\Roaming\Python\Python39\site-packages\torch\utils\data\dataloader.py", line 1372, in _process_data
data.reraise()
File "C:\Users\ESP\AppData\Roaming\Python\Python39\site-packages\torch_utils.py", line 644, in reraise
raise exception
Exception: Caught Exception in DataLoader worker process 0.
Original Traceback (most recent call last):
File "C:\Users\ESP\AppData\Roaming\Python\Python39\site-packages\torch\utils\data_utils\worker.py", line 308, in _worker_loop
data = fetcher.fetch(index)
File "C:\Users\ESP\AppData\Roaming\Python\Python39\site-packages\torch\utils\data_utils\fetch.py", line 51, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\Users\ESP\AppData\Roaming\Python\Python39\site-packages\torch\utils\data_utils\fetch.py", line 51, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\Yolo-FastestV2\utils\datasets.py", line 127, in getitem
raise Exception("%s is not exist" % label_path)
Exception: C:\Yolo-FastestV2\datasets\train\KakaoTalk_20230330_195432795_03_jpg.txt is not exist

python train.py --data data/coco.data

I'm getting an error during YOLOX training,
Could you please help me with this?

[SliverAward]

from glob import glob
import random

all_img_list = glob('C:\Yolo-FastestV2\datasets\train\.jpg') + glob('C:\Yolo-FastestV2\datasets\val\.jpg')
test_img_list = glob('C:\Yolo-FastestV2\datasets\test\*.jpg')

각 데이터셋의 비율을 8:2으로 맞춤

train_ratio = 0.9
val_ratio = 0.1 * train_ratio / (1 - train_ratio)

train_val_img_list = [x for x in all_img_list if x not in test_img_list]

random.seed(2000)
random.shuffle(train_val_img_list)

num_train = int(len(train_val_img_list) * train_ratio)
train_img_list = train_val_img_list[:num_train]
val_img_list = train_val_img_list[num_train:]

print(len(train_img_list), len(val_img_list), len(test_img_list))

with open('C:\Yolo-FastestV2\datasets\train.txt', 'w') as f:
f.write('\n'.join(train_img_list) + '\n')

with open('C:\Yolo-FastestV2\datasets\val.txt', 'w') as f:
f.write('\n'.join(val_img_list) + '\n')

with open('C:\Yolo-FastestV2\datasets\test.txt', 'w') as f:
f.write('\n'.join(test_img_list) + '\n')

I use the code