Fine Grained object detection

[deepukr007]

I want to train an object detecor for detecting small birds and also classifying the birds to its species. Yolov8 is definitely a great choice and I am sure it works well with small object detection like detecting birds but I think it will fail to detect birds when the task to detect birds with species. What should I do in this case ? Is there any way to tackle this problem ?

[pderrenger]

@deepukr007 absolutely, YOLOv8 can be a solid choice for your fine-grained object detection task, including detecting small birds and classifying them by species. For enhanced performance on such specific tasks, consider the following tips:

1. High-Quality Dataset: Ensure your dataset is rich with diverse images of birds, covering various species, poses, and backgrounds. The more representative your dataset, the better YOLOv8 can learn to distinguish between different species.

2. Augmentation: Use image augmentation techniques to increase the diversity of your training data. This can include transformations like scaling, cropping, and rotation, which can help the model generalize better to small objects.

3. Model Tuning: Experiment with different model configurations and hyperparameters. Adjusting the anchor sizes or using a higher resolution during training (imgsz) can help in detecting smaller objects more accurately.

4. Post-Processing: After detection, consider employing additional classification models specifically fine-tuned for bird species recognition if you find the direct classification performance lacking.

Here's a simple example to start training with custom configurations:

yolo detect train data=your_dataset.yaml model=yolov8n.yaml imgsz=640 epochs=100

Remember, the key is in the data and fine-tuning the model to your specific needs. Good luck with your bird detection project! 🐦