Artificial Intelligence

These datasets are gathered from an array of six gas sensors to be used for the odor recognition system. The sensors those used to create the data set are; Df-NH3, MQ-136, MQ-135, MQ-8, MQ-4, and MQ-2.

odors of different 10 samples are taken from these six sensors 

1- Natural Air

2- Fresh Onion

3- Fresh Garlic

4- Fresh Lemon

5- Tomato

6- Petrol

7- Gasoline

8- Coffee 1,2

9- Orange

10- Colonia Perfume 

The scaffold data splitting method categorizes molecules according to their scaffold (molecular substructure). Priori studies  have shown that scaffold data splitting provides a more realistic estimate of model performance in prospective evaluation compared to random data splitting approach. Our dataset is based on three benchmark public datasets, ZhangDDI, ChCh-Miner, and DeepDDI. We apply scaffold data splitting method on the three datasets to create training, validation, and test data.

This is a part of the Cityintrusion-Multicategory dataset for testing and training the network. This dataset contains 2502 training images and 429 validation images. Because our task is a joint task of segmentation and detection. Therefore, we provide the two different sub-dataset for segmentation and detection, respectively. In the seg folder, we provide the original images for training and validation. Besides, the corresponding labels also are provided. Training and validation have 2502 and 429, respectively.

This is a part of the Cityintrusion-Multicategory dataset for testing and training the network. This dataset contains 2502 training images and 429 validation images. Because our task is a joint task of segmentation and detection. Therefore, we provide the two different sub-dataset for segmentation and detection, respectively. In the seg folder, we provide the original images for training and validation. Besides, the corresponding labels also are provided. Training and validation have 2502 and 429, respectively.