The identification and counting of fish are important tools for managing the stock, production, and marketing of aquaculture fish. In commercial establishments, the counting of fingerlings has traditionally been done manually, which can cause stress to the animals and to the labor, in addition to low precision. Automation using computer vision and deep learning models is being increasingly explored. In machine learning for fish detection and counting, a dataset with images of specimens is needed to train the neural network. This dataset with species from the Panga family will be relevant in studies involving learning and training on different types of neural networks for counting and detection. The images were captured at a fish retailer. The company provided the location, the container, and the specimens, with an average length of 3.5 cm. 10 Panga fry were placed in a water container with a blue bottom, measuring 40 cm in diameter, 20 cm in height, and with a total capacity of 25 liters. The camera used to capture the images was fixed at the top, at a height of 60 cm from the container. The camera used was an iPhone XR with 12 megapixels and a resolution of 4608 × 2592 to capture the images. A total of 1,000 images were captured. The images present in the dataset are comprehensive, some have obstructions, low sharpness, and the water used in the container comes from the very recirculation system where the fry were raised, making the database more robust and closer to reality.

The identification and counting of fish are important tools for managing the stock, production, and marketing of aquaculture fish. In commercial establishments, the counting of fingerlings has traditionally been done manually, which can cause stress to the animals and to the labor, in addition to low precision. Automation using computer vision and deep learning models is being increasingly explored. In machine learning for fish detection and counting, a dataset with images of specimens is needed to train the neural network. This dataset with species from the Panga family will be relevant in studies involving learning and training on different types of neural networks for counting and detection. The images were captured at a fish retailer. The company provided the location, the container, and the specimens, with an average length of 3.5 cm. 10 Panga fry were placed in a water container with a blue bottom, measuring 40 cm in diameter, 20 cm in height, and with a total capacity of 25 liters. The camera used to capture the images was fixed at the top, at a height of 60 cm from the container. The camera used was an iPhone XR with 12 megapixels and a resolution of 4608 × 2592 to capture the images. A total of 1,000 images were captured. The images present in the dataset are comprehensive, some have obstructions, low sharpness, and the water used in the container comes from the very recirculation system where the fry were raised, making the database more robust and closer to reality.

Fish identification and counting are important tools for managing the stock, production and commercialisation of farmed fish. In commercial establishments, counting fish fingerlings has traditionally been carried out manually, which can cause stress to the animals and labour, as well as low accuracy. Automation using computer vision and deep learning models is increasingly being explored. In machine learning for fish detection and counting, a data set with images of the specimens is needed to train the neural network. This dataset with species from the Serrasalmidae family (round fish) will be relevant in studies involving learning and training in different types of neural networks for counting and detection. The images were captured at a fish retailer. The company provided the location, the container and the specimens, with an average length of 3.5 cm. 10 Serrasalmidae fingerlings were placed in a blue-bottomed water container measuring 40 cm in diameter, 20 cm high and with a total capacity of 25 litres. The camera used to capture the images was fixed at the top, at a height of 60 cm from the container. The camera used was an iPhone XR with 12 megapixels and a resolution of 4608 × 2592 to capture the images. A total of 1,000 images were captured. The images present in the dataset are comprehensive, some have obstructions, low sharpness, and the water used in the container comes from the very water recirculation system where the fry were grown, which makes the database more robust and closer to reality.

Fish identification and counting are important tools for managing the stock, production and commercialisation of farmed fish. In commercial establishments, counting fish fingerlings has traditionally been carried out manually, which can cause stress to the animals and labour, as well as low accuracy. Automation using computer vision and deep learning models is increasingly being explored. In machine learning for fish detection and counting, a data set with images of the specimens is needed to train the neural network. This dataset with species from the Serrasalmidae family (round fish) will be relevant in studies involving learning and training in different types of neural networks for counting and detection. The images were captured at a fish retailer. The company provided the location, the container and the specimens, with an average length of 3.5 cm. 10 Serrasalmidae fingerlings were placed in a blue-bottomed water container measuring 40 cm in diameter, 20 cm high and with a total capacity of 25 litres. The camera used to capture the images was fixed at the top, at a height of 60 cm from the container. The camera used was an iPhone XR with 12 megapixels and a resolution of 4608 × 2592 to capture the images. A total of 1,000 images were captured. The images present in the dataset are comprehensive, some have obstructions, low sharpness, and the water used in the container comes from the very water recirculation system where the fry were grown, which makes the database more robust and closer to reality.