{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/skindistilvit-lightweight-vision-transformer","title":"SkinDistilViT: Lightweight Vision Transformer for Skin Lesion Classification","arxiv_id":"2308.08669","date":"2023-08-16","proceeding":null,"authors":["Vlad-Constantin Lungu-Stan","Dumitru-Clementin Cercel","Florin Pop"],"abstract":"Skin cancer is a treatable disease if discovered early. We provide a production-specific solution to the skin cancer classification problem that matches human performance in melanoma identification by training a vision transformer on melanoma medical images annotated by experts. Since inference cost, both time and memory wise is important in practice, we employ knowledge distillation to obtain a model that retains 98.33% of the teacher's balanced multi-class accuracy, at a fraction of the cost. Memory-wise, our model is 49.60% smaller than the teacher. Time-wise, our solution is 69.25% faster on GPU and 97.96% faster on CPU. By adding classification heads at each level of the transformer and employing a cascading distillation process, we improve the balanced multi-class accuracy of the base model by 2.1%, while creating a range of models of various sizes but comparable performance. We provide the code at https://github.com/Longman-Stan/SkinDistilVit.","url_abs":"https://arxiv.org/abs/2308.08669v1","url_pdf":"https://arxiv.org/pdf/2308.08669v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"skindistilvit-lightweight-vision-transformer","repo_url":"https://github.com/longman-stan/skindistilvit","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":"cancer-classification","task_name":"Cancer Classification"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"knowledge-distillation","task_name":"Knowledge Distillation"},{"task_slug":"lesion-classification","task_name":"Lesion Classification"},{"task_slug":"skin-cancer-classification","task_name":"Skin Cancer Classification"},{"task_slug":"skin-lesion-classification","task_name":"Skin Lesion Classification"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"base","method_name":"BASE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"knowledge-distillation","method_name":"Knowledge Distillation"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"vision-transformer","method_name":"Vision Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}