Swin-TUNA inserts layer-dependent depthwise-convolution adapters into a frozen Swin-L backbone and reports 50.56 mIoU on FoodSeg103 and 74.94 mIoU on UECFoodPix Complete with 8.13M trainable parameters.
Application of computer vision techniques to fermented foods: An overview
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Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation
Swin-TUNA inserts layer-dependent depthwise-convolution adapters into a frozen Swin-L backbone and reports 50.56 mIoU on FoodSeg103 and 74.94 mIoU on UECFoodPix Complete with 8.13M trainable parameters.