drone resolutions for input yolov5

[pepsodent72]

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halo

I am using a drone and trying to detect rice fields using yolov5. after successfully detecting the segmentation, I used the Ground Sampling Distance method to calculate the area of the rice field segmentation based on the number of pixels. my problem now is that the drone image input is about 20MP, but I have tried yolov5 and resized it automatically to 3008 x 2272 pixels. is there any suggestion for yolo to maintain the number of 20 mp pixels?

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[UltralyticsAssistant]

👋 Hello @pepsodent72, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.

If this is a custom training or implementation ❓ Question, such as your scenario involving drone imagery and high-resolution input, please provide as much detail as possible, including:

• Example drone images or data (if shareable)
• Any specific YOLOv5 configuration or preprocessing steps you are using
• Logs or output details during your tests
• How resizing is being handled or any relevant code snippets

Additionally, it is worth noting that YOLOv5 automatically resizes inputs to the model's default training resolution. To work with high-resolution images (like 20MP), you may need to experiment with custom input sizes by modifying the --img parameter during training or inference. This could have implications on memory usage and performance, so adjustments may be necessary.

Requirements

Python>=3.8.0 with all requirements.txt installed including PyTorch>=1.8. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

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• Docker Image. See Docker Quickstart Guide

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This is an automated response to help guide you. An Ultralytics engineer will assist you further as soon as possible. Let us know if you have additional details to share in the meantime! 😊

[pderrenger]

@pepsodent72 for drone imagery at native 20MP resolution, we recommend:

1. Tile Inference: Split large images into smaller tiles (e.g., 640x640) using sliding window detection. This maintains original resolution while accommodating YOLOv5's default input size. Example:

model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
results = model('large_image.jpg', tile=512)  # tile size 512

2. Adjust Input Size: Train with higher resolution using --img argument (requires retraining):

python train.py --img 1280  # maximum size depends on GPU memory

3. Verify GSD Calibration: Ensure your Ground Sampling Distance calculations account for drone altitude and sensor specs, as demonstrated in our AI in agriculture solutions.

For commercial deployments requiring high-res processing, consider an Ultralytics Enterprise License for optimized large-scale implementations.