Using YOLOv8 for Fish Detection in Underwater Videos about ultralytics HOT 2 CLOSED

Hello! It's great to hear about your project using YOLOv8 for detecting different fish breeds in underwater videos. Here are some concise answers to your questions:

1. YOLOv8 can handle images of non-square proportions. You can resize your 2592x1944 images to 1280 pixels in width. To preserve the aspect ratio, the corresponding height should be about 960 pixels (1280/2592 * 1944).

2. Image processing, like adjusting brightness or contrast, can sometimes help the model generalize better, especially if it matches the conditions during which you'll be using the trained model. Just ensure the processing doesn't obscure or alter the key features of the fish.

3. For the fishes appearing in schools, it's a good approach to create a distinct class like 'school of fish,' especially when individual fish are not discernible. This could help in improving the model's performance by reducing misclassifications of tightly grouped entities.

4. If a fish’s breed is not identifiable in an image, it's generally advisable not to annotate it as the model might learn incorrect features. Including such ambiguous images without annotations can help in teaching the model to recognize only clear examples, thereby potentially excluding them during inference.

I hope these answers help! Dive deep and happy modeling with YOLOv8! 🐟📹

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👋 Hello @ValentinB16, 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.

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

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

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