Omkar2118/datasets huggingface dataset:
Fish Counting

The researchers collected a dataset of 3,500 images of Tilapia fish in a small bowl containing three fish per image. These images were manually annotated using Roboflow, with four keypoints labeled on each fish: mouth, peduncle, belly, and back. While only the mouth and peduncle keypoints were needed for length measurement, the additional keypoints were included to support potential future research using girth for weight determination.
The dataset was used to train YOLOv8 models for both keypoint detection and fish counting tasks. For real-world validation, an additional test set of 100 frame pairs (200 images total) captured from two cameras at different angles in actual fish farm conditions was also used.

Introducing paper: Precision Aquaculture: An Integrated Computer Vision and IoT Approach for Optimized Tilapia Feeding
Fish Keypoints Detection

The researchers collected 3,500 images of Tilapia fish, with each image containing three fish in a small bowl. These images were manually annotated using Roboflow, a tool for creating and managing annotated datasets. Four keypoints were labeled on each fish: mouth, peduncle, belly, and back. While the primary goal was to measure fish length using the mouth and peduncle points, the additional keypoints (belly and back) were included to support potential future research, such as using girth to determine fish weight. This dataset was used to train a YOLOv8 model for keypoint detection, achieving high accuracy in identifying these crucial points on the Tilapia fish.

Introducing paper: Precision Aquaculture: An Integrated Computer Vision and IoT Approach for Optimized Tilapia Feeding