Which No-Code Tool Has Better Performance for Image Segementation?

Image Segmentation Use Case Example (image credit: kili-technology.com)

Image segmentation is a fundamental task in computer vision that involves partitioning an image into multiple semantically meaningful regions. 

Neucore and Landing AI's LandingLens are No-Code solutions for building custom image segmentation models. This article will discuss NeuCore's performance on both datasets and compare it with Landing AI's image segmentation model on LandingLens.

Methodology

NeuCore's image segmentation model used a deep convolutional neural network architecture with novel modifications to the traditional CNN / autoencoder architecture. NeuCore's model had an enhanced encoder module that utilized domain adaptation for feature refinement and generalization. NeuCore also employed new attention squeeze methods to improve the model's performance in capturing fine-grained details in the segmentation maps. 

To train NeuCore's model on the ADE20k and CityScapes datasets, NeuCore's proprietary training methodology combines supervised and unsupervised learning techniques. This methodology involves training the model on a small amount of labelled data and then fine-tuning it on a much larger unlabeled dataset using few-shot learning. This approach can leverage the massive amount of unlabeled data in both datasets and improve the model's performance. 

For both the ADE20k and CityScapes datasets, we used the image size of 512x512. 

Comparison Results

On the ADE20k dataset, NeuCore's model achieved an average intersection over union (IoU) score of 62.8, outperforming Landing AI's image segmentation model by 15.5 IoU points. On the CityScapes dataset, NeuCore's model achieved an average IoU score of 86.1, which outperformed Landing AI's model by 17.4 IoU points.

Comparison on the ADE20k dataset

Comparison on CityScapes dataset

NeuCore model's superior performance can be attributed to the proprietary technology that utilized domain adaptation to learn various attributes by itself and adapt them to new scenarios. Meanwhile, NeuCore's new squeeze attention method leveraged a few shot mechanisms that improved the model's ability to capture fine-grained details in the segmentation maps. Furthermore, NeuCore's proprietary training methodology that combined supervised and unsupervised learning techniques allowed us to leverage the massive amount of unlabeled data in both datasets and improve the model's performance.

Conclusion

 In conclusion, NeuCore's image segmentation model outperformed Landing AI's model on the ADE20k and CityScapes datasets, achieving significantly higher IoU scores. NeuCore's model demonstrated superior performance in segmenting complex scenes and objects, showcasing the potential of deep learning-based image segmentation models for various computer vision tasks. The use of proprietary technology that improved the model's feature extraction and feature refinement capabilities, as well as NeuCore's proprietary training methodology that combined supervised and unsupervised learning techniques, allowed it to achieve superior performance compared to Landing AI's model. Further improvements can be made to our model by incorporating more advanced techniques such as attention mechanisms, multi-scale feature fusion, and ensembling. Overall, the NeuCore model's performance on both datasets highlights its effectiveness in solving real-world computer vision problems using proprietary technology.