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Performance of computer vision algorithms for fine-grained classification using crowdsourced insect images
The field of computer vision offers a wide range of algorithms, each with its strengths and weaknesses; how do we identify the algorithm that is in line with our application?
Comparison between transformers and convolutional models for fine-grained classification of insects
Although we observe high performances with all three families of models, our analysis shows that the hybrid model outperforms the fully convolutional-base and fully transformer-base models on accuracy performance and the fully transformer-base model outperforms the others on inference speed and, these prove the transformer to be robust to the shortage of samples and to be faster at inference time.
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