Poultry-Data

Detailed Description of the DatasetThe dataset employed in this study has been divided into training, validation, and test sets according to the standard practice, with a ratio of 6:2:2, to ensure robust model training and evaluation of generalization capabilities. The dataset encompasses six distinct recognition categories, specifically including individual honeybees, cows, ducks, chickens, geese, and the faces of goats, aiming to comprehensively examine the model's performance across various poultry species and different recognition tasks.To elaborate, the image quantity allocation for each category is as follows: the honeybee category contains 2,806 images, the cow category comprises 3,370 images, and the duck category boasts the largest number with 6,904 images. Furthermore, there are 5,303 images of chickens, 3,602 images of geese, and a total of 2,412 images depicting goat faces. This data volume configuration not only ensures sufficient samples for model training in each category but also guarantees that the validation and test sets adequately represent the diversity of the overall data, thereby enabling accurate assessment of the model's recognition accuracy and generalization performance.