Hi,
I have been trying to train a grasp model based on your work and would appreciate your help.
I experimented with two different datasets. The first dataset consisted of around 300 images of plastic vegetable toys against a black background, and I obtained pretty good results. This model also performs well in real-world scenarios.
However, when I moved on to the second dataset, which primarily includes building blocks and dolls (the dolls are almost spherical), the model achieves a high Intersection over Union (IoU) during evaluation but does not perform well in real-life situations.
I also attempted to incorporate additional augmentation methods, such as adding Gaussian noise and applying blur to the RGB images. While these techniques helped stabilize the training process and maintained a good IoU in evaluation, the real-world performance remains subpar.
Could you please provide some suggestions on how to improve the model's performance in real scenarios?
Thank you!
Hi,
I have been trying to train a grasp model based on your work and would appreciate your help.
I experimented with two different datasets. The first dataset consisted of around 300 images of plastic vegetable toys against a black background, and I obtained pretty good results. This model also performs well in real-world scenarios.
However, when I moved on to the second dataset, which primarily includes building blocks and dolls (the dolls are almost spherical), the model achieves a high Intersection over Union (IoU) during evaluation but does not perform well in real-life situations.
I also attempted to incorporate additional augmentation methods, such as adding Gaussian noise and applying blur to the RGB images. While these techniques helped stabilize the training process and maintained a good IoU in evaluation, the real-world performance remains subpar.
Could you please provide some suggestions on how to improve the model's performance in real scenarios?
Thank you!