YOCO : You Only Cook Once

YOCO is a cooking AI based on the Unbiased teacher v2 network. It helps users cook easily by predicting doneness of the ingredients.

Model: Unbiased Teacher v2 (Anchor-free)

We are benchmarking the model from this paper:
Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectors
Yen-Cheng Liu, Chih-Yao Ma, Zsolt Kira
The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2022

[Paper] [Project]

You can check the original PyTorch implementation of the paper here.

Train

Data

We used our own dataset with 3 dishes: bacon, egg, and pancake. It has 12 classes including food, pan, others, bacon_raw, bacon_cooked, bacon_overcooked, egg_raw, egg_cooked, egg_overcooked, pancake_raw, pancake_cooked, and pancake_overcooked.

food	pan	others
bacon_raw	bacon_cooked	bacon_overcooked
egg_raw	egg_cooked	egg_overcooked
pancake_raw	pancake_cooked	pancake_overcooked

Training FCOS

• Train Unbiased Teacher v2 under 40% COCO-supervision (adjust SUP_PERCENT for different ratio )

python train_net_yoco.py\
      --num-gpus 1 \
      --config configs/FCOS/coco-standard/yoco_fcos_R_50_ut2_run0.yaml \
       SOLVER.IMG_PER_BATCH_LABEL 4 SOLVER.IMG_PER_BATCH_UNLABEL 4  \
       SOLVER.MAX_ITER 50000 SEMISUPNET.BURN_UP_STEP 20000  \
       TEST.EVAL_PERIOD 500 DATALOADER.SUP_PERCENT 40.0

Resume the training

python train_net_yoco.py \
      --resume \
      --num-gpus 1 \
      --config configs/FCOS/coco-standard/yoco_fcos_R_50_ut2_run0.yaml \
       SOLVER.IMG_PER_BATCH_LABEL 4 SOLVER.IMG_PER_BATCH_UNLABEL 4  \
       SOLVER.MAX_ITER 50000 SEMISUPNET.BURN_UP_STEP 20000  \
       TEST.EVAL_PERIOD 500 DATALOADER.SUP_PERCENT 40.0  \
       MODEL.WEIGHTS <weight_file_name>.pth

Inference Only

• Adjust INFERENCE_TH_TEST for different threshold

python train_net.py \
      --test-only \
      --num-gpus 1 \
      --config configs/FCOS/coco-standard/yoco_fcos_R_50_ut2_run0.yaml \
      --output_dir <output_file_directory> \
      --video_input <input_file_path> \
      MODEL.WEIGHTS <weight_file_name>.pth \
      MODEL.FCOS.INFERENCE_TH_TEST 0.4

License

This project is licensed under MIT License, as found in the LICENSE file.

Contributors

Jisoo	Jiwoo	Minkyun	Soyeong