Summary of Benchmark Analysis Of Various Pre-trained Deep Learning Models on Assira Cats and Dogs Dataset, by Galib Muhammad Shahriar Himel et al.
Benchmark Analysis of Various Pre-trained Deep Learning Models on ASSIRA Cats and Dogs Dataset
by Galib Muhammad Shahriar Himel, Md. Masudul Islam
First submitted to arxiv on: 9 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 The paper presents a comparative study on various pre-trained models for image classification, utilizing the ASSIRA Cats & Dogs dataset as a benchmark. The authors demonstrate the effectiveness of different optimizers and loss functions in achieving high accuracy without significant modifications to the training model. By experimenting with hyper-parameters and computer architectures (NVIDIA GeForce GTX 1070, RTX 3080Ti, and RTX 3090), they achieve an impressive accuracy of 99.65% using the NASNet Large model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers tested different deep learning models to see which one works best for classifying cat or dog images. They used a special dataset and tried out different combinations of settings to find the perfect combination. By doing this, they were able to get an accuracy rate of almost 100% using a specific model called NASNet Large. |
Keywords
* Artificial intelligence * Deep learning * Image classification
