text: Title: `single_cls` training dies quietly during 1st epoch
  Body: ### Search before asking
  
  - [x] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and found no similar bug report.
  
  
  ### Ultralytics YOLO Component
  
  Train
  
  ### Bug
  
  I have a dataset consisting of ~95 categories that I'm trying to train a binary object detection model on for my tagging utility for it to automatically recommend bounding boxes. I'm utilizing the `single_cls` command line option to "flatten" my training data into a single class for classification.
  
  When running the training session on a `Yolo11n` model, the training process dies quietly while training the first epoch.
  
  I can confirm running training on the same dataset, using the same base model, without the `single_cls=true` option completes training successfully. 
  
  ### Environment
  
  ```
  Ultralytics 8.3.94 🚀 Python-3.12.3 torch-2.6.0+cu126 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
  Setup complete ✅ (32 CPUs, 31.3 GB RAM, 133.3/1831.7 GB disk)
  
  OS                  Linux-6.8.0-55-generic-x86_64-with-glibc2.39
  Environment         Linux
  Python              3.12.3
  Install             pip
  Path                /home/enusbaum/yolo/venv/lib/python3.12/site-packages/ultralytics
  RAM                 31.26 GB
  Disk                133.3/1831.7 GB
  CPU                 AMD Ryzen 9 5950X 16-Core Processor
  CPU count           32
  GPU                 NVIDIA GeForce RTX 3090, 24253MiB
  GPU count           1
  CUDA                12.6
  
  numpy               ✅ 2.1.1<=2.1.1,>=1.23.0
  matplotlib          ✅ 3.10.1>=3.3.0
  opencv-python       ✅ 4.11.0.86>=4.6.0
  pillow              ✅ 11.0.0>=7.1.2
  pyyaml              ✅ 6.0.2>=5.3.1
  requests            ✅ 2.32.3>=2.23.0
  scipy               ✅ 1.15.2>=1.4.1
  torch               ✅ 2.6.0+cu126>=1.8.0
  torch               ✅ 2.6.0+cu126!=2.4.0,>=1.8.0; sys_platform == "win32"
  torchvision         ✅ 0.21.0+cu126>=0.9.0
  tqdm                ✅ 4.67.1>=4.64.0
  psutil              ✅ 7.0.0
  py-cpuinfo          ✅ 9.0.0
  pandas              ✅ 2.2.3>=1.1.4
  seaborn             ✅ 0.13.2>=0.11.0
  ultralytics-thop    ✅ 2.0.14>=2.0.0
  ```
  
  ### Minimal Reproducible Example
  
  ```
  yolo detect train data=data.yaml model=yolo11n.pt epochs=200 imgsz=640 patience=25 batch=-1 plots=true workers=12 single_cls=true
  ```
  
  ### Additional
  
  While I can see the Python processes are still running:
  ```
  enusbaum    2302  5.6  2.1 13017132 696784 pts/1 Sl   11:32   0:31 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train data=data.yaml model=yolo11n.pt epochs=200 imgsz=640 patience=25 batch=-1 plots=true workers=12 single_cls=true
  enusbaum    2303  5.5  2.1 13015644 707028 pts/1 Sl   11:32   0:30 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train data=data.yaml model=yolo11n.pt epochs=200 imgsz=640 patience=25 batch=-1 plots=true workers=12 single_cls=true
  enusbaum    2304  5.4  2.1 13017552 714692 pts/1 Sl   11:32   0:29 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train data=data.yaml model=yolo11n.pt epochs=200 imgsz=640 patience=25 batch=-1 plots=true workers=12 single_cls=true
  enusbaum    2324  3.9  0.2 13093984 91624 pts/1  Sl   11:32   0:21 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train data=data.yaml model=yolo11n.pt epochs=200 imgsz=640 patience=25 batch=-1 plots=true workers=12 single_cls=true
  enusbaum    2327  2.7  0.2 13097044 92048 pts/1  Sl   11:32   0:15 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train data=data.yaml model=yolo11n.pt epochs=200 imgsz=640 patience=25 batch=-1 plots=true workers=12 single_cls=true
  enusbaum    2328  3.8  0.2 13094032 90900 pts/1  Sl   11:32   0:21 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train data=data.yaml model=yolo11n.pt epochs=200 imgsz=640 patience=25 batch=-1 plots=true workers=12 single_cls=true
  enusbaum    2329  3.8  0.2 13094044 92320 pts/1  Sl   11:32   0:20 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train data=data.yaml model=yolo11n.pt epochs=200 imgsz=640 patience=25 batch=-1 plots=true workers=12 single_cls=true
  enusbaum    2334  2.8  0.2 13097128 92176 pts/1  Sl   11:32   0:15 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train data=data.yaml model=yolo11n.pt epochs=200 imgsz=640 patience=25 batch=-1 plots=true workers=12 single_cls=true
  enusbaum    2335  3.8  0.2 13094116 92028 pts/1  Sl   11:32   0:21 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train data=data.yaml model=yolo11n.pt epochs=200 imgsz=640 patience=25 batch=-1 plots=true workers=12 single_cls=true
  enusbaum    2336  3.7  0.2 13094128 91428 pts/1  Sl   11:32   0:20 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train data=data.yaml model=yolo11n.pt epochs=200 imgsz=640 patience=25 batch=-1 plots=true workers=12 single_cls=true
  enusbaum    2338  3.9  0.2 13094152 91688 pts/1  Sl   11:32   0:21 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train data=data.yaml model=yolo11n.pt epochs=200 imgsz=640 patience=25 batch=-1 plots=true workers=12 single_cls=true
  enusbaum    2341  2.7  0.2 13097212 92076 pts/1  Sl   11:32   0:15 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train data=data.yaml model=yolo11n.pt epochs=200 imgsz=640 patience=25 batch=-1 plots=true workers=12 single_cls=true
  enusbaum    2342  3.7  0.2 13094200 91976 pts/1  Sl   11:32   0:20 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train data=data.yaml model=yolo11n.pt epochs=200 imgsz=640 patience=25 batch=-1 plots=true workers=12 single_cls=true
  enusbaum    2343  3.8  0.2 13094212 90960 pts/1  Sl   11:32   0:20 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train data=data.yaml model=yolo11n.pt epochs=200 imgsz=640 patience=25 batch=-1 plots=true workers=12 single_cls=true
  ```
  
  They seem to be dead as they're not consuming any CPU, and are still holding on to GPU memory:
  
  ```
      PID USER      PR  NI    VIRT    RES    SHR S  %CPU  %MEM     TIME+ COMMAND
     2302 enusbaum  20   0   12.4g 696784  94344 S   0.0   2.1   0:31.06 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train +
     2303 enusbaum  20   0   12.4g 707028  96184 S   0.0   2.2   0:30.38 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train +
     2304 enusbaum  20   0   12.4g 714692  96904 S   0.0   2.2   0:29.89 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train +
     2324 enusbaum  20   0   12.5g  91624  75944 S   0.0   0.3   0:21.43 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train +
     2327 enusbaum  20   0   12.5g  92048  76424 S   0.0   0.3   0:15.26 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train +
     2328 enusbaum  20   0   12.5g  90900  75912 S   0.0   0.3   0:21.05 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train +
     2329 enusbaum  20   0   12.5g  92320  76168 S   0.0   0.3   0:20.93 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train +
     2334 enusbaum  20   0   12.5g  92176  75912 S   0.0   0.3   0:15.43 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train +
     2335 enusbaum  20   0   12.5g  92028  76168 S   0.0   0.3   0:21.06 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train +
     2336 enusbaum  20   0   12.5g  91428  75912 S   0.0   0.3   0:20.39 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train +
     2338 enusbaum  20   0   12.5g  91688  75912 S   0.0   0.3   0:21.45 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train +
     2341 enusbaum  20   0   12.5g  92076  75912 S   0.0   0.3   0:15.17 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train +
     2342 enusbaum  20   0   12.5g  91976  75912 S   0.0   0.3   0:20.60 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train +
     2343 enusbaum  20   0   12.5g  90960  75656 S   0.0   0.3   0:20.81 /home/enusbaum/yolo/venv/bin/python3 /home/enusbaum/yolo/venv/bin/yolo detect train +
  ```
  
  ```
  +-----------------------------------------------------------------------------------------+
  | NVIDIA-SMI 550.120                Driver Version: 550.120        CUDA Version: 12.4     |
  |-----------------------------------------+------------------------+----------------------+
  | GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
  | Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
  |                                         |                        |               MIG M. |
  |=========================================+========================+======================|
  |   0  NVIDIA GeForce RTX 3090        Off |   00000000:0A:00.0 Off |                  N/A |
  | 30%   44C    P2            109W /  350W |   17529MiB /  24576MiB |      0%      Default |
  |                                         |                        |                  N/A |
  +-----------------------------------------+------------------------+----------------------+
  
  +-----------------------------------------------------------------------------------------+
  | Processes:                                                                              |
  |  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
  |        ID   ID                                                               Usage      |
  |=========================================================================================|
  +-----------------------------------------------------------------------------------------+
  ```
  
  ### Are you willing to submit a PR?
  
  - [ ] Yes I'd like to help by submitting a PR!