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Deep Plastic Enhanced Object Detection for Epipelagic Plastic. This repository contains source code for the method developed in DeepPlastic: Identifying Marine Plastic In The Epipelagic Zone using Computer Vision and Deep Learning Information: Paper: [Coming Soon] YouTube video of Results: https://youtu.be/8zBdFxaK4Os Object Detection Model Four models: YOLOv4, YOLOv5, MobileSSD, Faster RCNN Inception V2 Small efficient and high precision models can be used for real-time object detection. Model architecture and implementation details: https://arxiv.org/ Weights for YOLOv4 and YOLOv5 are provided in the model/ YOLOv4: best. weights; use best.weights YOLOv5: best.pt; use best.pt Google Colab Links Note: Click on File and Save Copy in Drive. If you try to edit my file it'll ask you for permission and send me an email. Please make your own copy. YOLOv5: https://colab.research.google.com/drive/1_qzbpBWkNfxQ0ny-DvsKicCeM0aFU4eW?usp=sharing DeepTrash DataSet 1900 training images, 637 test images, 637 validation images (60, 20, 20 split) Field images taken from Lake Tahoe, San Francisco Bay and Bodega Bay in CA. Deep Sea images are from JAMSTEK JEDI dataset: http://www.godac.jamstec.go.jp/
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
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influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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