Shrimp_counter_TTS_2.0: Annotated Shrimp Images for Object Detection

5 college students spent 2 months annotating shrimps for use with the YOLO26 object detection model. The dataset is designed for computer vision tasks related to counting and detection. Its specific scale and annotation methodology are detailed in the provided description.

Use Cases

• Train an object detection model to identify and count shrimps based on the provided annotations.
• Benchmark the performance of the YOLO26 architecture on a custom biological dataset.
• Develop automated shrimp counting systems for aquaculture or research based on visual data.

Strengths

• Annotations were created by a team of 5 annotators over a 2-month period, suggesting a dedicated labeling effort.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Kaggle

Collection Method

Manual annotation by a team of 5 college students.