First, make sure your dataset is sufficiently diverse. The following measures can help improve the recognition performance:
1. The dataset should include images of the target from various angles so that the model can properly learn the features of the target.
2. The target should be located in different areas of the images and at different distances from the camera in order to control for lens distortion.
3. The images in the dataset should have environments with diverse textures, colors, and lighting conditions to control for environmental factors.
4. When a single image has multiple instances of a target or multiple targets of different types (such as 2 apples and 1 pear), all the targets must be labeled. Otherwise, unlabeled targets will be considered negative samples, which will affect the training results.
In addition, when creating a new training task, enable the data enhancement and background enhancement options to increase the diversity of the dataset.
Finally, add additional model training steps as appropriate to improve model convergence and achieve better training results.However, more training steps is not always better. Too many steps can significantly increase the training time or lead to overfitting, which reduces the training performance.