Segmentation for anime character
Integrated into Huggingface Spaces 🤗 using Gradio. Try it out
ISNet, U2Net, MODNet, InSPyReNet
Models can be downloaded here
You need to install pytorch first
Then pip install -r requirements.txt
python train.py --net isnet_is --data-dir path/to/dataset --epoch 1000 --batch-size-train 10 --batch-size-val 4 --workers-train 10 --workers-val 5 --acc-step 3 --benchmark --log-step 10 --val-epoch 3 --img-size 1024
detail
arguments:
-h, --help show this help message and exit
--net {isnet_is,isnet,u2net,u2netl,modnet,inspyrnet_res,inspyrnet_swin}
isnet_is: Train ISNet with intermediate feature supervision,
isnet: Train ISNet,
u2net: Train U2Net full,
u2netl: Train U2Net lite,
modnet: Train MODNet
inspyrnet_res: Train InSPyReNet_Res2Net50
inspyrnet_swin: Train InSPyReNet_SwinB
--pretrained-ckpt PRETRAINED_CKPT
load form pretrained ckpt of net
--resume-ckpt RESUME_CKPT
resume training from ckpt
--img-size IMG_SIZE image size for training and validation,
1024 recommend for ISNet,
384 recommend for InSPyReNet,
640 recommend for others,
--data-dir DATA_DIR root dir of dataset
--fg-dir FG_DIR relative dir of foreground
--bg-dir BG_DIR relative dir of background
--img-dir IMG_DIR relative dir of images
--mask-dir MASK_DIR relative dir of masks
--fg-ext FG_EXT extension name of foreground
--bg-ext BG_EXT extension name of background
--img-ext IMG_EXT extension name of images
--mask-ext MASK_EXT extension name of masks
--data-split DATA_SPLIT
split rate for training and validation
--lr LR learning rate
--epoch EPOCH epoch num
--gt-epoch GT_EPOCH epoch for training ground truth encoder when net is isnet_is
--batch-size-train BATCH_SIZE_TRAIN
batch size for training
--batch-size-val BATCH_SIZE_VAL
batch size for val
--workers-train WORKERS_TRAIN
workers num for training dataloader
--workers-val WORKERS_VAL
workers num for validation dataloader
--acc-step ACC_STEP gradient accumulation step
--accelerator {cpu,gpu,tpu,ipu,hpu,auto}
accelerator
--devices DEVICES devices num
--fp32 disable mix precision
--benchmark enable cudnn benchmark
--log-step LOG_STEP log training loss every n steps
--val-epoch VAL_EPOCH
valid and save every n epoch
--cache-epoch CACHE_EPOCH
update cache every n epoch
--cache CACHE ratio (cache to entire training dataset), higher
value require more memory, set 0 to disable cache
python inference.py --net isnet_is --ckpt path/to/isnet_is.ckpt --data-dir path/to/input_data --out out --img-size 1024 --only-matted
python export.py --net isnet_is --ckpt path/to/isnet_is.ckpt --to onnx --out isnet.onnx --img-size 1024
This dataset is a combined dataset of AniSeg and character_bg_seg_data.
I clean the dataset using DeepDanbooru first then manually, to make sue all mask is anime character.
git lfs install git clone https://huggingface.co/datasets/skytnt/anime-segmentation cd anime-segmentation unzip -q 'data/*.zip'
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