text: Title: Wrong Confusion Matrix Results when Training Yolov5 with a custom Dataset
  Body: ### Search before asking
  
  - [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions.
  
  
  ### Question
  
  Hello
  I trained yolov5s on a custom dataset with 50 epochs and batch size of 16 but after training the model I evaluated its performance on the test set and noticed that the mAP was 94% which was a bit weird for me. So I checked the confusion matrix and noticed that the results were wrong.
  Here are my training specifications:
  
  1. **dataset:** The dataset was mainly published for pose estimation but I did some preprocessing on RoboFlow to make it suitable for object detection (I am not sure if this can be an issue or not). The data contains a single class and is divided to 25000 images for training, 4600 images for validation and 1198 images for testing.
  2. **Model Configuration:** I have used yolov5s model configuration
  3. **Training Parameters:** I have used the default training parameters except for the number of classes I set it to 1 and the activation function. Concerning the weights, I started the training from the petrained weightsof ultralytics (yolov5s.pt)
  4. **Activation Function:** For the activation function I have changed the SiLU() to LeakyReLU(0.1015625, inplace=True). The reason I have done this is because my model will deployed later on an FPGA board and the SiLU activation function is not supported by the board. 
  5. **Training Platform:** I trained the model on my laptop with RTX4060 GPU with size 8GB
   
  I hope you can help me fix this issue as I am confused what I can try to resolve the problem. Thank you very much in advance for guidance and support.
  
  
  ![confusion_matrix](https://github.com/ultralytics/yolov5/assets/63944119/114b55b3-02b0-4d25-a50f-a8963e8c8962)
  
  
  ### Additional
  
  _No response_