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Food2K-TW101

All the accuracy (ACC) are the trained models do inference on un-seen validation data, not the training accuracy.

Dataset Model Epochs Optimizer Top-1 ACC Top-5 ACC Pretrain Augmentation type
FOOD2K PRENet-ResNet50 N/A ongoing 83.03 97.21 ongoing ongoing
FOOD2K Inception V4 N/A ongoing 82.02 96.45 ongoing ongoing
FOOD2K VGG16 N/A ongoing 78.96 95.26 ongoing ongoing
Dataset Model Epochs Optimizer Top-1 ACC Top-5 ACC Pretrain Augmentation type
Taiwanese Food-101 MobileFormer_508M 100 AdamW 95.15 99.52 imagenet-1k Basic
Taiwanese Food-101 PRENet-ResNet50 100 SGD 94.59 99.49 food2K Basic
Taiwanese Food-101 PVTv2-B2-Linear 100 AdamW 94.51 99.45 imagenet-1k Basic
Taiwanese Food-101 ConvNeXtv2_nano 100 AdamW 94.04 99.29 imagenet-1k Basic
Taiwanese Food-101 ResNet50 100 AdamW 93.96 99.58 imagenet-1k Basic
Taiwanese Food-101 ConvNeXtv2_femto 100 AdamW 93.21 99.19 imagenet-1k Basic
Taiwanese Food-101 MobileFormer_96M 100 AdamW 93.01 99.25 imagenet-1k Basic
Taiwanese Food-101 Inception V4 100 SGD 92.14 99.01 imagenet-1k Basic
Taiwanese Food-101 mobilenetv3_large_100 100 AdamW 91.66 98.97 imagenet-1k Basic
Taiwanese Food-101 mobileViT v2 100 AdamW 89.98 98.46 imagenet-1k Basic
Taiwanese Food-101 efficientNetv3 Large 100 AdamW 84.89 96.40 imagenet-1k Basic
Taiwanese Food-101 efficient ViT_MIT 100 AdamW 82.32 95.78 imagenet-1k Basic
Taiwanese Food-101 RepViT_m2.3 100 AdamW 76.53 93.80 imagenet-1k Basic
Taiwanese Food-101 RepViT_m0.9 100 AdamW 75.01 93.49 imagenet-1k Basic
Taiwanese Food-101 VGG16 N/A SGD 67.65 89.33 imagenet-1k Basic
Dataset Model Epochs Optimizer Top-1 ACC Top-5 ACC Pretrain Augmentation type
FOOD2K-TW PRENet-ResNet50 N/A ongoing ongoing ongoing ongoing ongoing
FOOD2K-TW Inception V4 100 SGD 81.43 96.28 imagenet-1k Basic
FOOD2K-TW VGG16 N/A ongoing ongoing ongoing ongoing ongoing

Do not trainable

Dataset Model Epochs Optimizer Top-1 ACC Top-5 ACC Pretrain Augmentation type
Taiwanese Food-101 convnextv2_huge.fcmae_ft_in1k 15 AdamW 1.02 0.99 imagenet-1k Normal

Augmentation type

Type Detail
Basic RandomHorizontalFlip(p=0.5)
+ RandomRotation(degrees=15)
+ ColorJitter(brightness=0.126, saturation=0.5)
+ Resize((550, 550))
+ RandomCrop(448)

To-Do List

Dataset Classes/Images paper dataset Data Aviliable?
Taiwanese-Food-101 101/20,200 N/A (only master Thesis) dataset Yes
UEC Food256 256/25,088 paper dataset Yes
ETH Food-101 101/101,000 paper dataset Yes
Vireo Food-172 172/110,241 paper dataset Need to email
Food524DB 524/247,636 paper dataset Yes, but .mat format
CNFOOD-241 241/191,811 paper dataset Yes
ChineseFoodNet 208/192,000 paper dataset Link is dead, email didnot response
Sushi-50 50/3,963 paper dataset Yes
ISIA Food-500 500/399,726 paper dataset Yes
Food2K 2,000/1,036,564 paper dataset Need to email. Yes

TaiwaneseFood101 https://www.kaggle.com/datasets/kuantinglai/taiwanese-food-101/data

To-Play List

https://arxiv.org/abs/2301.10936 https://www.kaggle.com/datasets/zachaluza/cnfood-241 https://universe.roboflow.com/search?q=class%3Adumpling https://universe.roboflow.com/search?q=class%253Apork&p=1 https://ieeexplore.ieee.org/document/9214438 https://www.researchgate.net/publication/372133738_Deep_Learning_for_Food_Image_Recognition_and_Nutrition_Analysis_Towards_Chronic_Diseases_Monitoring_A_Systematic_Review https://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22111NYPI0294006%22.&searchmode=basic

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