Is there a good solution strategy for similar targets in object detection tasks

[blue-q]

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Recently, I have been training on an object detection task where several targets have high similarity. Therefore, I want to improve the inter class distance between different classes in order to better identify these targets. Is there a good strategy within the Ultratics framework to address this issue? What I can think of is replacing the classification loss, and I have checked that bceloss is currently being used.Looking forward to your reply very much

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[glenn-jocher]

@blue-q hello! 👋

Indeed, distinguishing between highly similar objects is a common challenge in object detection tasks. Within the Ultralytics framework, one effective strategy to address this challenge is to experiment with different loss functions that may better capture the distinctions between your classes.

While BCELoss is the default, exploring alternatives like Focal Loss could potentially yield better results, as it assigns more weight to hard-to-classify examples and reduces the weight for easy-to-classify ones. Additionally, ensuring your dataset is diverse and contains representative samples of the classes, especially those that are similar, can greatly help. This might involve augmenting your dataset with more varied examples of the classes that are hard to distinguish.

Here's a quick snippet on how you might adjust the loss function if you're customizing the YOLOv8 source code:

# Example for replacing BCELoss with Focal Loss (not directly applicable, just a hint)
import torch.nn.functional as F

def focal_loss(inputs, targets, alpha=0.25, gamma=2):
    bce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction='none')
    pt = torch.exp(-bce_loss)
    loss = alpha * (1-pt)**gamma * bce_loss
    return loss.mean()

# Use this focal_loss function where the loss is computed

Remember, each dataset and task is unique, so it might require some experimentation to find what works best for your specific scenario.

Hope this helps and looking forward to seeing how your models improve! 😊

[blue-q]

Hi @glenn-jocher，thank you very much for your reply. I will try it out. Another question is, does the Ultralytics framework support hard example mining?

[glenn-jocher]

Hi there! 🚀

Indeed, integrating hard example mining into your training process can significantly improve model performance by focusing on difficult cases. While the Ultralytics framework doesn't explicitly offer a built-in hard example mining feature, you can effectively implement a strategy tailored to your needs. A common approach involves analyzing model predictions to identify hard examples and then emphasizing these instances in subsequent training rounds.

Batching these identified hard examples more frequently or adjusting their weights during training can simulate hard example mining. You might achieve this through custom dataset management or tweaking the loss computation.

Here's a quick pseudo-code illustrative of weighting difficult examples higher:

# Example of increasing weights for hard examples
loss_weights = torch.ones_like(targets)
loss_weights[hard_example_indices] = 1.5  # Increase weight for hard examples
loss = weighted_loss_function(preds, targets, loss_weights)

Feel free to experiment with this approach and tailor it to fit your dataset and objectives. Happy coding!

[blue-q]

Hi @glenn-jocher ， as you mentioned above, I have added weights to the loss [1] of easily misclassified categories. From the training map, 5 epochs achieve the same effect as the previous 100 epochs. Good luck to me.

[glenn-jocher]

Hi there! 🌟

That's fantastic news! Applying weights to the loss of easily misclassified categories and witnessing such a significant improvement in efficiency is a testament to the power of fine-tuning the training process. It's great to hear that you achieved the desired effect in just 5 epochs compared to the previous 100. Keep up the excellent work, and indeed, good luck on your continued progress! If you have more insights or questions down the line, feel free to share.

Happy coding! 😊

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