Abstract
Indian Food Dishes are famous around the globe. Object detection in an image is a well-known task in computer vision. Indian Food Dishes detection using deep learning-based models will maximize the impact of computer vision-based models in the food domain. Deep learning-based model usage is still limited in recognizing and detecting Indian Food Items due to the lack of datasets on Indian Food. We introduce the IndianFood-7 Dataset, which contains images of more than 800 and having 1700 + annotations spreading beyond seven special Indian food items. We report the comparative study of numerous variants which are having current Avant-grade for object detection models, YOLOR and YOLOv5. Furthermore, we have inspected and evaluated the model performance by revising the predictions verified on the images of the test dataset.
All authors contributed equally to this work and all are the first author.
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Agarwal, R., Bansal, N., Choudhury, T., Sarkar, T., Ahuja, N.J. (2023). IndianFood-7: Detecting Indian Food Items Using Deep Learning-Based Computer Vision. In: Unhelkar, B., Pandey, H.M., Agrawal, A.P., Choudhary, A. (eds) Advances and Applications of Artificial Intelligence & Machine Learning. ICAAAIML 2022. Lecture Notes in Electrical Engineering, vol 1078. Springer, Singapore. https://doi.org/10.1007/978-981-99-5974-7_2
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