Poor results and remove pretrained classes from training about ultralytics HOT 4 OPEN

There is an argument single_cls in the model.train() function that focuses the model towards detection instead of classification. You can set it to True. This will cause model to only detect the trash in images.
Keep in mind that this will remove distinction between any classes you have in your dataset.

Also, you can limit the scope of detection while using your existing model for inference by using classes argument in model.predict(). Obtain the class names using model.names in separate line, and look for the index of trash(say 0), then pass it into your inference code as model.predict(image, classes=[0]).

You can look at further arguments of model.train() here

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@pauldeveaux hello,

Thank you for your prompt response and for providing additional details. It's great to hear that using single_cls=True has improved your results. However, I understand the confusion regarding the model.names output.

The issue you're encountering is likely due to the model still retaining the class names from the pre-trained weights. When you fine-tune a model with a custom dataset, especially with a single class, it's essential to ensure that the model's class names are updated accordingly.

Here are a few steps to address this:

1. Ensure Data Configuration: Double-check your data.yaml file to ensure it correctly specifies the single class. It looks like your data.yaml is correctly set up, but just to confirm:

   names:
   - trash
   nc: 1
   train: /content/Trash-Detector-2/train
   val: /content/Trash-Detector-2/valid
   test: /content/Trash-Detector-2/test/images

2. Load Model with Custom Data: When loading the model for training, ensure it reads the custom data configuration. This can be done by specifying the data argument correctly:

   results = model.train(
       data='/content/Trash-Detector-2/data.yaml',
       epochs=150,
       imgsz=640,
       batch=-1,
       workers=16,
       patience=50,
       single_cls=True,
       resume=os.path.exists(weights_path)
   )

3. Update Class Names: After training, you can manually update the model.names to reflect your custom class:

   model.names = {0: 'trash'}

4. Check Model Names After Training: Ensure that the model's class names are updated after training:

   print(model.names)  # Should output {0: 'trash'}

Here is a revised version of your training script with these considerations:

model = YOLO("/content/yolov8n.pt")

# Check if GPU is available
if torch.cuda.is_available():
    print("GPU is available")
else:
    print("GPU is not available")

weights_path = "/content/runs/detect/train2/weights/last.pt"
# Start or resume model
if os.path.exists(weights_path) and not RESTART:
    print("Resuming training from saved weights...")
    model = YOLO(weights_path)
else:
    print("Starting new training...")
    model = YOLO("/content/yolov8n.pt")

def save_on_drive(trainer):
    print(trainer.__dir__())
    epoch = trainer.epoch
    save_dir = "/content/drive/MyDrive/Model"
    save_path = os.path.join(save_dir, f'yolo_model_{epoch+1}.pt')
    model.save(save_path)

# wandb logs
add_wandb_callback(model, enable_model_checkpointing=True)
model.add_callback("on_train_epoch_end", save_on_drive)

results = model.train(
    data='/content/Trash-Detector-2/data.yaml',
    epochs=150,
    imgsz=640,
    batch=-1,
    workers=16,
    patience=50,
    single_cls=True,
    resume=os.path.exists(weights_path)
)

# Update model names to reflect the custom class
model.names = {0: 'trash'}

# Validate the model
model.val()

# Finish the wandb run
wandb.finish()

By following these steps, you should be able to ensure that the model correctly recognizes and outputs the 'trash' class. If you continue to experience issues, please let us know, and we can further investigate.

Thank you for your patience and dedication to improving your model. Happy training! 🚀

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👋 Hello @pauldeveaux, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.

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Hello,

Thank you for your response. I did as you said (add the single_cls=True as argument in model.train() ). I had better results at the end of the training.

results = model.train(
          data=os.path.join(dataset.location, "/content/Trash-Detector-2/data.yaml"),
          epochs=150,
          imgsz=640,
          batch=-1,
          workers=16,
          patience=50,
          single_cls=True,
          resume=os.path.exists(weights_path)
      )

However, when I print model.names, the "trash" class does not appear in the output:

{0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}

Could you please help me understand why the class is missing and how I can ensure it is included in the model?

Thank you for your assistance!

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