Automated Author ProfileAli, Ammar
ITMO University0000-0002-3073-9506
Ali, Ammar
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.9 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
We collected the data from ten drivers that drove vehicles in St. Petersburg, Russia, for a few months. The image size from the road cameras is 480x640. For training, we scaled them down to 420x420 and applied center cropping to a 416x416 area to exclude border areas that contain noise and distortion. To annotate the data, we used pseudo-labeling and an ensemble of different open-source models that were trained on the KITTI dataset. The ensemble is based on a weighted mean average using the following weights [0.4, 0.3, 0.2, 0.1] from top to bottom. For each image in the dataset, we used the following four models (LapDept, DTP Hybrid, BTS, and VNL) to obtain the predictions. After that, we took the weighted average between all four masks and saved the results as our ground truth.
Authors
- Kashevnik, Alexey ;
- Ali, Ammar
We collected the data from ten drivers that drove vehicles in St. Petersburg, Russia, for a few months. The image size from the road cameras is 480x640. For training, we scaled them down to 420x420 and applied center cropping to a 416x416 area to exclude border areas that contain noise and distortion. To annotate the data, we used pseudo-labeling and an ensemble of different open-source models that were trained on the KITTI dataset. The ensemble is based on a weighted mean average using the following weights [0.4, 0.3, 0.2, 0.1] from top to bottom. For each image in the dataset, we used the following four models (LapDept, DTP Hybrid, BTS, and VNL) to obtain the predictions. After that, we took the weighted average between all four masks and saved the results as our ground truth.
Authors
- Kashevnik, Alexey ;
- Ali, Ammar