The dataset is associated with the paper: Høgstedt, E. U., Schellewald, C., Stahl, A., and Mester, R., Patch Ensembles for Robust Salmon Re-Identification with Weak Trajectory Labels, 2026 IEEE International Conference on Image Processing (ICIP), 2026.This dataset accompanies the salmon re-identification framework presented in the repository: salmon-reid-patch-ensemble GitHub repository The upload contains datasets, segmentation annotations, trained model outputs, evaluation results, and cross-camera matching information used for salmon re-identification research in aquaculture environments. The data was collected within the cAIge project and supports development of computer vision and AI methods for long-term monitoring of salmon in sea cages. Contents reid_dataset.zip Contains the salmon re-identification dataset.Folders 1–14 contain training data, folder 15 contains validation data, and folder 16 contains test data recorded from another camera for cross-camera evaluation. segmentation.zip Contains manually annotated segmentation data in LabelMe format, including annotations of Q1, Q2, and operculum regions. analysis1.zip – analysis8.zip Contain analysis data, embeddings, trajectories, intermediate evaluation outputs, and/or experiment results used in the associated re-identification analyses. test.zip Contains cross-camera test results for the main experiments, including ViT baseline and sliced-patch models. a15_a16_idmatch_with_traj_IDs.xlsx Contains manually verified identity matches between analysis folders 15 and 16 for cross-camera evaluation. ap_per_query_all_models.zip Contains per-query Average Precision (AP) values for all evaluated models, used for bootstrap statistical analysis. Intended use The dataset is intended for research in: fish re-identification aquaculture monitoring underwater computer vision fish tracking AI-based welfare assessment