Traceback (most recent call last):
File "E:\Projects\1\yolov8_Distillation-master\train.py", line 30, in
model_s.train(data="ultralytics/cfg/datasets/ship_SSDD.yaml", epochs=100, device=0 ,imgsz=640,name="runs/train/SSDD_distillation", workers=0, Distillation = model_t.model)
File "E:\Projects\1\yolov8_Distillation-master\ultralytics\yolo\engine\model.py", line 376, in train
self.trainer.train()
File "E:\Projects\1\yolov8_Distillation-master\ultralytics\yolo\engine\trainer.py", line 406, in train
self._do_train(world_size)
File "E:\Projects\1\yolov8_Distillation-master\ultralytics\yolo\engine\trainer.py", line 559, in _do_train
preds = self.model(batch['img'])
File "E:\Anaconda\envs\yolov8\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, kwargs)
File "E:\Projects\1\yolov8_Distillation-master\ultralytics\nn\tasks.py", line 89, in forward
return self.predict(x, *args, *kwargs)
File "E:\Projects\1\yolov8_Distillation-master\ultralytics\nn\tasks.py", line 107, in predict
return self._predict_once(x, profile, visualize, embed)
File "E:\Projects\1\yolov8_Distillation-master\ultralytics\nn\tasks.py", line 128, in _predict_once
x = m(x) # run
File "E:\Anaconda\envs\yolov8\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(input, kwargs)
File "E:\Projects\1\yolov8_Distillation-master\ultralytics\nn\modules\block.py", line 232, in forward
return self.cv2(torch.cat(y, 1))
RuntimeError: CUDA out of memory. Tried to allocate 48.00 MiB (GPU 0; 8.00 GiB total capacity; 12.24 GiB already allocated; 0 bytes free; 12.30 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
Traceback (most recent call last): File "E:\Projects\1\yolov8_Distillation-master\train.py", line 30, in
model_s.train(data="ultralytics/cfg/datasets/ship_SSDD.yaml", epochs=100, device=0 ,imgsz=640,name="runs/train/SSDD_distillation", workers=0, Distillation = model_t.model)
File "E:\Projects\1\yolov8_Distillation-master\ultralytics\yolo\engine\model.py", line 376, in train
self.trainer.train()
File "E:\Projects\1\yolov8_Distillation-master\ultralytics\yolo\engine\trainer.py", line 406, in train
self._do_train(world_size)
File "E:\Projects\1\yolov8_Distillation-master\ultralytics\yolo\engine\trainer.py", line 559, in _do_train
preds = self.model(batch['img'])
File "E:\Anaconda\envs\yolov8\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, kwargs)
File "E:\Projects\1\yolov8_Distillation-master\ultralytics\nn\tasks.py", line 89, in forward
return self.predict(x, *args, *kwargs)
File "E:\Projects\1\yolov8_Distillation-master\ultralytics\nn\tasks.py", line 107, in predict
return self._predict_once(x, profile, visualize, embed)
File "E:\Projects\1\yolov8_Distillation-master\ultralytics\nn\tasks.py", line 128, in _predict_once
x = m(x) # run
File "E:\Anaconda\envs\yolov8\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(input, kwargs)
File "E:\Projects\1\yolov8_Distillation-master\ultralytics\nn\modules\block.py", line 232, in forward
return self.cv2(torch.cat(y, 1))
RuntimeError: CUDA out of memory. Tried to allocate 48.00 MiB (GPU 0; 8.00 GiB total capacity; 12.24 GiB already allocated; 0 bytes free; 12.30 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF