Summary of Transfer Learning For Wildlife Classification: Evaluating Yolov8 Against Densenet, Resnet, and Vggnet on a Custom Dataset, by Subek Sharma et al.
Transfer Learning for Wildlife Classification: Evaluating YOLOv8 against DenseNet, ResNet, and VGGNet on a Custom Dataset
by Subek Sharma, Sisir Dhakal, Mansi Bhavsar
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The study assesses the performance of various deep learning models, including DenseNet, ResNet, VGGNet, and YOLOv8, in classifying wildlife species using a custom dataset. The dataset consists of 575 images from 23 endangered species, sourced from reputable online repositories. By leveraging transfer learning, the researchers fine-tune pre-trained models on the dataset to reduce training time while improving classification accuracy. The results indicate that YOLOv8 outperforms other models, achieving a training accuracy of 97.39% and a validation F1-score of 96.50%. These findings suggest that YOLOv8’s advanced architecture and efficient feature extraction capabilities make it well-suited for automating wildlife monitoring and conservation efforts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wildlife species classification is important for conservation efforts, but it can be challenging to identify different species accurately. A new study uses special kinds of artificial intelligence called deep learning models to help classify wildlife species. The researchers used a custom dataset with 575 images of 23 endangered species and compared the performance of four different deep learning models: DenseNet, ResNet, VGGNet, and YOLOv8. They found that one model, YOLOv8, did much better than the others at correctly identifying species. This could be helpful for people who are trying to protect animals and their habitats. |
Keywords
» Artificial intelligence » Classification » Deep learning » F1 score » Feature extraction » Resnet » Transfer learning
