dog-qiuqiu/Yolo-FastestV2: Based on Yolo's low-power, ultra-lightweight universal target detection algorithm, the parameter is only 250k, and the speed of the smart phone mobile terminal can reach ~300fps+ (github.com)
저번에 라즈베리파이 4 욜로 구동 성공했습니다 프레임 약 1.4~1.8 나오는거 같아요 ㅎㅎ
정말 감사합니다
각설하고,,
해당 깃헙에서 무려 7프레임으로 돌릴 수 있는 초경량 욜로를 찾게되서 해보고 싶은데..
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\20230401_160320_jpg.txt is not exist
계속 해결이 안되네요..
라벨 파일을 못찾았다고 하는데 로컬에 있는거 확인했고 train .txt 파일 에도 확인했는데 참..이러네요
roboflow 로 라벨링했고 Yolo darknet 데이터셋 형식으로 다운 받았습니다.
파일 분할 및 데이터셋 이미지 , 라벨 경로 생성 코드 입니다.
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 9:1으로 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')
python train.py --data data/coco.data
실행 명령어 입니다
data를 coco 로 쓰길래 coco로 데이터셋 다시 받아보고했는데
저 맨위에 깃헙 작성자는 이렇게 안내하더라고요 .. 포기해야하나 싶습니다 ㅠ