Summary of Melnet: a Real-time Deep Learning Algorithm For Object Detection, by Yashar Azadvatan and Murat Kurt
MelNet: A Real-Time Deep Learning Algorithm for Object Detection
by Yashar Azadvatan, Murat Kurt
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study introduces MelNet, a novel deep learning algorithm for object detection that achieves impressive results. Trained on the KITTI dataset, MelNet reaches an mAP score of 0.732 after 300 epochs. For comparison, three alternative models -YOLOv5, EfficientDet, and Faster-RCNN-MobileNetv3- are also trained on the same dataset and evaluated against MelNet. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MelNet is a new way to detect objects using deep learning. It was tested on a big dataset called KITTI and did very well! After practicing for 300 “training sessions”, MelNet got an mAP score of 0.732, which means it’s really good at finding objects. Other models like YOLOv5, EfficientDet, and Faster-RCNN-MobileNetv3 were also tested to see how they compare. |
Keywords
* Artificial intelligence * Deep learning * Faster rcnn * Object detection
