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Image classification task results

ResNet

Paper:https://arxiv.org/abs/1512.03385

DarkNet

Paper:https://arxiv.org/abs/1804.02767?e05802c1_page=1

RepVGG

Paper:https://arxiv.org/abs/2101.03697

RegNet

Paper:https://arxiv.org/abs/2003.13678

ViT

Paper:https://arxiv.org/abs/2010.11929

VAN

Paper:https://arxiv.org/abs/2202.09741

ResNetCifar training from scratch on CIFAR100

ResNetCifar is different from ResNet in the first few layers.

Network macs params input size gpu num batch epochs Top-1
ResNet18Cifar 557.935M 11.220M 32x32 1 RTX A5000 128 200 77.110
ResNet34Cifar 1.164G 21.328M 32x32 1 RTX A5000 128 200 78.140
ResNet50Cifar 1.312G 23.705M 32x32 1 RTX A5000 128 200 75.610
ResNet101Cifar 2.531G 42.697M 32x32 1 RTX A5000 128 200 76.970
ResNet152Cifar 3.751G 58.341M 32x32 1 RTX A5000 128 200 77.710

You can find more model training details in classification_training/cifar100/.

ResNet training from scratch on ImageNet1K(ILSVRC2012)

Network macs params input size gpu num batch epochs Top-1
ResNet18 1.819G 11.690M 224x224 2 RTX A5000 256 100 70.512
ResNet34 3.671G 21.798M 224x224 2 RTX A5000 256 100 73.680
ResNet50 4.112G 25.557M 224x224 2 RTX A5000 256 100 76.300
ResNet101 7.834G 44.549M 224x224 2 RTX A5000 256 100 77.380
ResNet152 11.559G 60.193M 224x224 2 RTX A5000 256 100 77.542

You can find more model training details in classification_training/imagenet/.

DarkNet training from scratch on ImageNet1K(ILSVRC2012)

Network macs params input size gpu num batch epochs Top-1
DarkNetTiny 412.537M 2.087M 256x256 2 RTX A5000 256 100 57.786
DarkNet19 3.663G 20.842M 256x256 2 RTX A5000 256 100 74.248
DarkNet53 9.322G 41.610M 256x256 2 RTX A5000 256 100 76.352

You can find more model training details in classification_training/imagenet/.

RepVGG training from scratch on ImageNet1K(ILSVRC2012)

Network macs params input size gpu num batch epochs Top-1
RepVGG_A0_deploy 1.362G 8.309M 224x224 2 RTX A5000 256 120 72.010
RepVGG_A1_deploy 2.364G 12.790M 224x224 2 RTX A5000 256 120 74.032
RepVGG_A2_deploy 5.117G 25.500M 224x224 2 RTX A5000 256 120 76.078
RepVGG_B0_deploy 3.058G 14.339M 224x224 2 RTX A5000 256 120 74.880
RepVGG_B1_deploy 11.816G 51.829M 224x224 2 RTX A5000 256 120 77.790
RepVGG_B2_deploy 18.377G 80.315M 224x224 2 RTX A5000 256 120 78.120

You can find more model training details in classification_training/imagenet/.

RegNet training from scratch on ImageNet1K(ILSVRC2012)

Network macs params input size gpu num batch epochs Top-1
RegNetX_400MF 410.266M 5.158M 224x224 2 RTX A5000 4096 300 69.466
RegNetX_600MF 616.813M 6.196M 224x224 2 RTX A5000 4096 300 71.754
RegNetX_800MF 820.324M 7.260M 224x224 2 RTX A5000 4096 300 73.148
RegNetX_1_6GF 1.635G 9.190M 224x224 2 RTX A5000 4096 300 76.142
RegNetX_3_2GF 3.222G 15.297M 224x224 2 RTX A5000 4096 300 78.244
RegNetX_4_0GF 4.013G 22.118M 224x224 2 RTX A5000 4096 300 78.916

You can find more model training details in classification_training/imagenet/.

U2NetBackbone training from scratch on ImageNet1K(ILSVRC2012)

Network macs params input size gpu num batch epochs Top-1
U2NetBackbone 13.097G 26.181M 224x224 2 RTX A5000 256 100 76.038

You can find more model training details in classification_training/imagenet/.

ResNet finetune from ImageNet21k pretrain weight on ImageNet1K(ILSVRC2012)

Network macs params input size gpu num batch epochs Top-1
ResNet18 1.819G 11.690M 224x224 2 RTX A5000 4096 300 71.580
ResNet34 3.671G 21.798M 224x224 2 RTX A5000 4096 300 76.316
ResNet50 4.112G 25.557M 224x224 2 RTX A5000 4096 300 79.484
ResNet101 7.834G 44.549M 224x224 2 RTX A5000 4096 300 80.940
ResNet152 11.559G 60.193M 224x224 2 RTX A5000 4096 300 81.236

You can find more model training details in classification_training/imagenet/.

ResNet finetune from DINO pretrain weight on ImageNet1K(ILSVRC2012)

Network macs params input size gpu num batch epochs Top-1
ResNet18 1.819G 11.690M 224x224 1 RTX A5000 256 100 70.754
ResNet18 1.819G 11.690M 224x224 1 RTX A5000 4096 300 71.362
ResNet34 3.671G 21.798M 224x224 2 RTX A5000 256 100 74.218
ResNet34 3.671G 21.798M 224x224 2 RTX A5000 4096 300 75.916
ResNet50 4.112G 25.557M 224x224 2 RTX A5000 256 100 77.114
ResNet50 4.112G 25.557M 224x224 2 RTX A5000 4096 300 79.418

You can find more model training details in classification_training/imagenet/.

ViT finetune from self-trained MAE pretrain weight(400epoch) on ImageNet1K(ILSVRC2012)

Network macs params input size gpu num batch epochs Top-1
ViT-Tiny-Patch16 1.075G 5.670M 224x224 1 RTX A5000 4096 100 68.614
ViT-Small-Patch16 4.241G 21.955M 224x224 2 RTX A5000 4096 100 79.006
ViT-Base-Patch16 16.849G 86.377M 224x224 2 RTX A5000 4096 100 83.204
ViT-Large-Patch16 59.647G 304.024M 224x224 2 RTX A5000 4096 100 85.020

You can find more model training details in classification_training/imagenet/.

ViT finetune from offical MAE pretrain weight(800 epoch) on ImageNet1K(ILSVRC2012)

Network macs params input size gpu num batch epochs Top-1
ViT-Base-Patch16 16.849G 86.377M 224x224 2 RTX A5000 4096 100 83.290
ViT-Large-Patch16 59.647G 304.024M 224x224 2 RTX A5000 4096 100 85.876

You can find more model training details in classification_training/imagenet/.

ResNet train from ImageNet1K pretrain weight on ImageNet21K(Winter 2021 release)

Network macs params input size gpu num batch epochs Semantic Softmax Acc
ResNet18 1.819G 11.690M 224x224 2 RTX A5000 4096 80 68.639
ResNet34 3.671G 21.798M 224x224 2 RTX A5000 4096 80 71.873
ResNet50 4.112G 25.557M 224x224 2 RTX A5000 4096 80 74.664
ResNet101 7.834G 44.549M 224x224 2 RTX A5000 4096 80 76.136
ResNet152 11.559G 60.193M 224x224 2 RTX A5000 4096 80 75.731

You can find more model training details in classification_training/imagenet21k/.

ViT finetune from self-trained MAE pretrain weight(100epoch) on ACCV2022

Network macs params input size gpu num batch epochs Top-1
ViT-Large-Patch16 59.651G 308.124M 224x224 2 RTX 4090 4096 100 90.693

You can find more model training details in classification_training/accv2022/.

VAN finetune from offical pretrain weight on ImageNet1K(ILSVRC2012)

Network macs params input size gpu num batch epochs Top-1
VAN-B0 880.224M 4.103M 224x224 2 RTX A5000 1024 300 75.618
VAN-B1 2.518G 13.856M 224x224 2 RTX 4090 1024 300 80.956
VAN-B2 5.033G 26.567M 224x224 4 RTX 4090 1024 300 82.322

You can find more model training details in classification_training/imagenet/.

Object detection task results

RetinaNet

Paper:https://arxiv.org/abs/1708.02002

FCOS

Paper:https://arxiv.org/abs/1904.01355

CenterNet

Paper:https://arxiv.org/abs/1904.07850

TTFNet

Paper:https://arxiv.org/abs/1909.00700

DETR

Paper:https://arxiv.org/abs/2005.12872

DINO-DETR

Paper:https://arxiv.org/abs/2203.03605

All detection models training from scratch on COCO2017

Trained on COCO2017 train dataset, tested on COCO2017 val dataset.

mAP is IoU=0.5:0.95,area=all,maxDets=100,mAP(COCOeval,stats[0]).

Network resize-style input size macs params gpu num batch epochs mAP
ResNet50-RetinaNet YoloStyle-640 640x640 95.558G 37.969M 2 RTX A5000 32 13 34.459
ResNet50-RetinaNet YoloStyle-800 800x800 149.522G 37.969M 2 RTX A5000 32 13 36.023
ResNet50-RetinaNet RetinaStyle-800 800x1333 250.069G 37.969M 2 RTX A5000 8 13 35.434
ResNet50-FCOS YoloStyle-640 640x640 81.943G 32.291M 2 RTX A5000 32 13 37.176
ResNet50-FCOS YoloStyle-800 800x800 128.160G 32.291M 2 RTX A5000 32 13 38.745
ResNet50-FCOS RetinaStyle-800 800x1333 214.406G 32.291M 2 RTX A5000 8 13 39.649
ResNet18DCN-CenterNet YoloStyle-512 512x512 14.854G 12.889M 2 RTX A5000 64 140 26.209
ResNet18DCN-TTFNet-3x YoloStyle-512 512x512 16.063G 13.737M 2 RTX A5000 64 39 27.054
ResNet50-DETR YoloStyle-1024 1024x1024 89.577G 30.440M 8 RTX A5000 64 500 36.941
ResNet50-DINO-DETR YoloStyle-1024 1024x1024 844.204G 47.082M 8 RTX A5000 16 13 42.870
ResNet50-DINO-DETR YoloStyle-1024 1024x1024 844.204G 47.082M 8 RTX A5000 16 39 45.445

You can find more model training details in detection_training/coco/.

All detection models finetune from objects365 pretrain weight on COCO2017

Trained on COCO2017 train dataset, tested on COCO2017 val dataset.

mAP is IoU=0.5:0.95,area=all,maxDets=100,mAP(COCOeval,stats[0]).

Network resize-style input size macs params gpu num batch epochs mAP
ResNet50-RetinaNet YoloStyle-640 640x640 95.558G 37.969M 2 RTX A5000 32 13 38.930
ResNet50-RetinaNet YoloStyle-800 800x800 149.522G 37.969M 2 RTX A5000 32 13 40.483
ResNet50-RetinaNet RetinaStyle-800 800x1333 250.069G 37.969M 2 RTX A5000 8 13 40.424
ResNet50-FCOS YoloStyle-640 640x640 81.943G 32.291M 2 RTX A5000 32 13 42.871
ResNet50-FCOS YoloStyle-800 800x800 128.160G 32.291M 2 RTX A5000 32 13 44.526
ResNet50-FCOS RetinaStyle-800 800x1333 214.406G 32.291M 2 RTX A5000 8 13 42.848

You can find more model training details in detection_training/coco/.

All detection models train from COCO2017 pretrain weight on Objects365(v2,2020)

Trained on objects365 train dataset, tested on objects365 val dataset.

mAP is IoU=0.5:0.95,area=all,maxDets=100,mAP(COCOeval,stats[0]).

Network resize-style input size macs params gpu num batch epochs mAP
ResNet50-RetinaNet YoloStyle-800 800x800 149.522G 37.969M 8 RTX A5000 32 13 16.360
ResNet50-FCOS RetinaStyle-800 800x1333 214.406G 32.291M 8 RTX A5000 32 13 17.068

All detection models training from scratch on VOC2007 and VOC2012

Trained on VOC2007 trainval dataset + VOC2012 trainval dataset, tested on VOC2007 test dataset.

mAP is IoU=0.50,area=all,maxDets=100,mAP.

Network resize-style input size macs params gpu num batch epochs mAP
ResNet50-RetinaNet YoloStyle-640 640x640 84.947G 36.724M 2 RTX A5000 32 13 81.948
ResNet50-FCOS YoloStyle-640 640x640 80.764G 32.153M 2 RTX A5000 32 13 81.624

You can find more model training details in detection_training/voc/.

All detection models finetune from objects365 pretrain weight on VOC2007 and VOC2012

Trained on VOC2007 trainval dataset + VOC2012 trainval dataset, tested on VOC2007 test dataset.

mAP is IoU=0.50,area=all,maxDets=100,mAP.

Network resize-style input size macs params gpu num batch epochs mAP
ResNet50-RetinaNet YoloStyle-640 640x640 84.947G 36.724M 2 RTX A5000 32 13 90.220
ResNet50-FCOS YoloStyle-640 640x640 80.764G 32.153M 2 RTX A5000 32 13 90.371

You can find more model training details in detection_training/voc/.

Semantic Segmentation task results

DeepLabv3+

Paper:https://arxiv.org/abs/1802.02611

U2Net

Paper:https://arxiv.org/abs/2005.09007

All semantic segmentation models training from scratch on ADE20K

Network input size macs params gpu num batch epochs miou
ResNet50-DeepLabv3+ 512x512 25.548G 26.738M 2 RTX A5000 8 128 34.659
U2Net 512x512 219.012G 46.191M 2 RTX A5000 8 128 39.046

You can find more model training details in semantic_segmentation_training/ade20k/.

All semantic segmentation models training from scratch on COCO2017

Network input size macs params gpu num batch epochs miou
ResNet50-DeepLabv3+ 512x512 25.548G 26.738M 2 RTX A5000 32 64 64.176
U2Net 512x512 219.012G 46.191M 4 RTX A5000 32 64 66.529

You can find more model training details in semantic_segmentation_training/coco/.

Instance Segmentation task results

YOLACT

Paper:https://arxiv.org/abs/1904.02689

SOLOv2

Paper:https://arxiv.org/abs/2003.10152

All instance segmentation models training from scratch on COCO2017

Trained on COCO2017 train dataset, tested on COCO2017 val dataset.

mAP is IoU=0.5:0.95,area=all,maxDets=100,mAP(COCOeval,stats[0]).

Network resize-style input size macs params gpu num batch epochs mAP
ResNet50-YOLACT YoloStyle-800 800x800 123.095G 31.165M 4 RTX A5000 64 39 28.061
ResNet50-SOLOv2 YoloStyle-1024 1024x1024 248.546G 46.582M 4 RTX A5000 32 39 36.559

You can find more model training details in instance_segmentation_training/coco/.

Knowledge distillation task results

KD loss

Paper:https://arxiv.org/abs/1503.02531

DML loss

Paper:https://arxiv.org/abs/1706.00384

ResNet training from pretrain weight on ImageNet1K(ILSVRC2012)

Teacher Network Student Network method Freeze Teacher input size gpu num batch epochs Teacher Top-1 Student Top-1
ResNet152 ResNet50 CE+KD True 224x224 2 RTX A5000 256 100 / 77.352
ResNet152 ResNet50 CE+DML False 224x224 2 RTX A5000 256 100 79.274 78.122
ResNet152 ResNet50 CE+KD+Vit Aug True 224x224 2 RTX A5000 4096 300 / 80.168
ResNet152 ResNet50 CE+DML+Vit Aug False 224x224 2 RTX A5000 4096 300 81.508 79.810

You can find more model training details in distillation_training/imagenet/.

Contrastive learning task results

DINO:Emerging Properties in Self-Supervised Vision Transformers

Paper:https://arxiv.org/abs/2104.14294

ResNet DINO pretrain on ImageNet1K(ILSVRC2012)

Network input size gpu num batch epochs Loss
ResNet18-DINO 224x224 4 RTX A5000 256 400 3.081
ResNet34-DINO 224x224 4 RTX A5000 256 400 2.425
ResNet50-DINO 224x224 4 RTX A5000 256 400 1.997

You can find more model training details in contrastive_learning_training/imagenet/.

ResNet finetune from DINO pretrain weight on ImageNet1K(ILSVRC2012)

Network macs params input size gpu num batch epochs Top-1
ResNet50 4.112G 25.557M 224x224 2 RTX A5000 256 100 77.114
ResNet50 4.112G 25.557M 224x224 2 RTX A5000 4096 300 79.418

You can find more model training details in classification_training/imagenet/.

Masked image modeling task results

MAE:Masked Autoencoders Are Scalable Vision Learners

Paper:https://arxiv.org/abs/2111.06377

ViT MAE pretrain on ImageNet1K(ILSVRC2012)

Network input size gpu num batch epochs Loss
ViT-Tiny-Patch16 224x224 1 RTX A5000 256 400 0.427
ViT-Small-Patch16 224x224 2 RTX A5000 256 400 0.414
ViT-Base-Patch16 224x224 2 RTX A5000 256 400 0.388
ViT-Large-Patch16 224x224 2 RTX A5000 256 400 0.378

You can find more model training details in masked_image_modeling_training/imagenet/.

ViT MAE pretrain on ACCV2022 from ImageNet1K pretrain

Network input size gpu num batch epochs Loss
ViT-Large-Patch16 224x224 2 RTX 4090 256 100 0.423

You can find more model training details in masked_image_modeling_training/accv2022/.

ViT finetune from self-trained MAE pretrain weight(400epoch) on ImageNet1K(ILSVRC2012)

Network macs params input size gpu num batch epochs Top-1
ViT-Tiny-Patch16 1.075G 5.670M 224x224 1 RTX A5000 4096 100 68.614
ViT-Small-Patch16 4.241G 21.955M 224x224 2 RTX A5000 4096 100 79.006
ViT-Base-Patch16 16.849G 86.377M 224x224 2 RTX A5000 4096 100 83.204
ViT-Large-Patch16 59.647G 304.024M 224x224 2 RTX A5000 4096 100 85.020

You can find more model training details in classification_training/imagenet/.

ViT finetune from offical MAE pretrain weight(800 epoch) on ImageNet1K(ILSVRC2012)

Network macs params input size gpu num batch epochs Top-1
ViT-Base-Patch16 16.849G 86.377M 224x224 2 RTX A5000 4096 100 83.290
ViT-Large-Patch16 59.647G 304.024M 224x224 2 RTX A5000 4096 100 85.876

You can find more model training details in classification_training/imagenet/.

ViT finetune from self-trained MAE pretrain weight(100epoch) on ACCV2022

Network macs params input size gpu num batch epochs Top-1
ViT-Large-Patch16 59.651G 308.124M 224x224 2 RTX 4090 4096 100 90.693

You can find more model training details in classification_training/accv2022/.

OCR text detection task results

DBNet

Paper:https://arxiv.org/abs/1911.08947

Use combine dataset include ICDAR2017RCTW/ICDAR2019ART/ICDAR2019LSVT/ICDAR2019MLT to train and test.

Network macs params input size gpu num batch epochs precision recall f1
repvgg_dbnet 11.806G 726.338K 960x960 2 RTX A5000 128 200 88.756 74.205 80.831
resnet50_dbnet 139.141G 24.784M 960x960 2 RTX A5000 64 100 92.973 86.316 89.521
vanb1_dbnet 108.596G 14.439M 960x960 2 RTX A5000 64 100 93.049 86.881 89.859

You can find more model training details in ocr_text_detection_training/.

OCR text recognition task results

CRNN+LSTM+CTC

Paper:https://arxiv.org/abs/1507.05717

Use combine dataset aistudio_baidu_street/chinese_dataset/synthetic_chinese_string_dataset/meta_self_learning_dataset to train and test.

Network macs params input size gpu num batch epochs lcs_precision lcs_recall
repvgg_ctc_model 951.804M 6.865M 32x512 2 RTX A5000 512 50 98.079 97.564
resnet50_ctc_model 12.474G 179.864M 32x512 2 RTX A5000 1024 50 99.368 99.135
van_b1_ctc_model 2.410G 27.954 M 32x512 2 RTX A5000 1024 50 98.868 97.597

You can find more model training details in ocr_text_recognition_training/.

Human matting task results

PFAN+Matting

Paper1:https://arxiv.org/abs/1903.00179

Paper2:https://arxiv.org/abs/2104.14222

Paper3:https://arxiv.org/abs/2202.09741

Use combine dataset Deep_Automatic_Portrait_Matting/RealWorldPortrait636/P3M10K to train and test.

Network macs params input size gpu num batch epochs iou precision recall sad mae mse grad conn
resnet50_pfan_matting 85.638G 29.654M 832x832 2 RTX 3090 32 50 0.9818 0.9879 0.9937 5.9215 0.0085 0.0048 7.5277 5.6842
van_b2_pfan_matting 85.926G 27.854M 832x832 2 RTX 3090 32 50 0.9850 0.9900 0.9948 5.0200 0.0072 0.0038 5.5563 4.7644

You can find more model training details in human_matting_training/.

Salient object detection task results

PFAN+Segmentation

Paper1:https://arxiv.org/abs/1903.00179

Paper2:https://arxiv.org/abs/2202.09741

Use combine dataset DIS5K/HRS10K/HRSOD/UHRSD to train and test.

Network macs params input size gpu num batch epochs iou precision recall f_squared_beta
resnet50_pfan_segmentation 70.921G 26.580M 832x832 8 RTX 4090D 64 100 0.8501 0.8977 0.9389 0.9068
van_b2_pfan_segmentation 77.433G 26.953M 832x832 8 RTX 4090D 64 100 0.8904 0.9292 0.9527 0.9345

You can find more model training details in salient_object_detection_training/.

Interactive segmentation task results

SAM(segment-anything)

Paper:https://arxiv.org/pdf/2304.02643

using random noise prompt box to test model.

Network dataset input size gpu num batch epochs iou precision recall
sam_b salient_object_detection 1024x1024 8 RTX 4090D 16 500 0.9486 0.9676 0.9783
sam_b sobav2 1024x1024 2 RTX 3090 4 500 0.9871 0.9935 0.9930
sam_b desobav2 1024x1024 8 RTX 4090D 16 200 0.9862 0.9919 0.9941

You can find more model training details in interactive_segmentation_training/.

Image inpainting model task results

Aggregated Contextual Transformations for High-Resolution Image Inpainting

Paper:https://arxiv.org/abs/2104.01431

All image inpainting model training from scratch on CelebA-HQ

Trained image inpainting model on CelebA-HQ dataset.Test image num=2000.

Network input size epochs Mask mae psnr ssim fid
AOT-GAN 512x512 100 0.01-0.1 0.0023 40.368 0.9853 0.8003
AOT-GAN 512x512 100 0.1-0.2 0.0064 33.724 0.9592 2.1704
AOT-GAN 512x512 100 0.2-0.3 0.0122 29.996 0.9245 3.8093
AOT-GAN 512x512 100 0.3-0.4 0.0192 27.343 0.8860 5.4981
AOT-GAN 512x512 100 0.4-0.5 0.0279 25.154 0.8426 8.3303
AOT-GAN 512x512 100 0.5-0.6 0.0486 21.576 0.7704 14.553

You can find more model training details in image_inpainting_training/celebahq/.

All image inpainting model training from scratch on Places365-standard

Trained image inpainting model on Places365-standard dataset.Test image num=36500.

Network input size epochs Mask mae psnr ssim fid
AOT-GAN 512x512 5 0.01-0.1 0.0041 35.505 0.9772 0.1412
AOT-GAN 512x512 5 0.1-0.2 0.0114 29.250 0.9374 0.4833
AOT-GAN 512x512 5 0.2-0.3 0.0214 25.802 0.8855 1.1973
AOT-GAN 512x512 5 0.3-0.4 0.0331 23.391 0.8291 2.5272
AOT-GAN 512x512 5 0.4-0.5 0.0469 21.504 0.7677 5.0670
AOT-GAN 512x512 5 0.5-0.6 0.0737 18.904 0.6795 14.951
Network input size epochs Mask mae psnr ssim fid
AOT-GAN-light 512x512 5 0.01-0.1 0.0043 35.023 0.9757 0.1680
AOT-GAN-light 512x512 5 0.1-0.2 0.0121 28.824 0.9338 0.6524
AOT-GAN-light 512x512 5 0.2-0.3 0.0227 25.423 0.8798 1.7831
AOT-GAN-light 512x512 5 0.3-0.4 0.0350 23.052 0.8218 4.0379
AOT-GAN-light 512x512 5 0.4-0.5 0.0494 21.199 0.7590 8.2494
AOT-GAN-light 512x512 5 0.5-0.6 0.0768 18.690 0.6719 22.745
TRANSX-LKA-AOT-GAN-light 512x512 1 0.01-0.1 0.0042 35.287 0.9763 0.1611
TRANSX-LKA-AOT-GAN-light 512x512 1 0.1-0.2 0.0117 29.148 0.9356 0.5648
TRANSX-LKA-AOT-GAN-light 512x512 1 0.2-0.3 0.0217 25.790 0.8835 1.3744
TRANSX-LKA-AOT-GAN-light 512x512 1 0.3-0.4 0.0331 23.451 0.8281 2.7966
TRANSX-LKA-AOT-GAN-light 512x512 1 0.4-0.5 0.0464 21.599 0.7681 5.1937
TRANSX-LKA-AOT-GAN-light 512x512 1 0.5-0.6 0.0718 19.034 0.6838 12.825

You can find more model training details in image_inpainting_training/places365_standard.

All image inpainting model training from scratch on Places365-challenge

Trained image inpainting model on Places365-challenge dataset.Test image num=36500.

Network input size epochs Mask mae psnr ssim fid
AOT-GAN 512x512 1 0.01-0.1 0.0039 35.807 0.9781 0.1318
AOT-GAN 512x512 1 0.1-0.2 0.0110 29.499 0.9395 0.4493
AOT-GAN 512x512 1 0.2-0.3 0.0207 26.021 0.8890 1.0881
AOT-GAN 512x512 1 0.3-0.4 0.0320 23.586 0.8338 2.2785
AOT-GAN 512x512 1 0.4-0.5 0.0454 21.674 0.7734 4.4948
AOT-GAN 512x512 1 0.5-0.6 0.0715 19.039 0.6848 13.475
Network input size epochs Mask mae psnr ssim fid
AOT-GAN-light 512x512 1 0.01-0.1 0.0042 35.263 0.9762 0.1609
AOT-GAN-light 512x512 1 0.1-0.2 0.0118 29.043 0.9349 0.6028
AOT-GAN-light 512x512 1 0.2-0.3 0.0221 25.609 0.8814 1.6013
AOT-GAN-light 512x512 1 0.3-0.4 0.0340 23.209 0.8235 3.5484
AOT-GAN-light 512x512 1 0.4-0.5 0.0480 21.332 0.7606 7.2095
AOT-GAN-light 512x512 1 0.5-0.6 0.0745 18.778 0.6714 20.031
TRANSX-LKA-AOT-GAN-light 512x512 1 0.01-0.1 0.0041 35.466 0.9769 0.1392
TRANSX-LKA-AOT-GAN-light 512x512 1 0.1-0.2 0.0114 29.275 0.9369 0.4566
TRANSX-LKA-AOT-GAN-light 512x512 1 0.2-0.3 0.0212 25.888 0.8854 1.0463
TRANSX-LKA-AOT-GAN-light 512x512 1 0.3-0.4 0.0325 23.530 0.8300 2.0431
TRANSX-LKA-AOT-GAN-light 512x512 1 0.4-0.5 0.0457 21.667 0.7699 3.8222
TRANSX-LKA-AOT-GAN-light 512x512 1 0.5-0.6 0.0711 19.104 0.6836 9.9868

You can find more model training details in image_inpainting_training/places365_challenge.

Diffusion model task results

Denoising Diffusion Probabilistic Models

Paper:https://arxiv.org/abs/2006.11239

Denoising Diffusion Implicit Models

Paper:https://arxiv.org/abs/2010.02502

High-Resolution Image Synthesis with Latent Diffusion Models

Paper:https://arxiv.org/abs/2112.10752

All diffusion model with different sampling methods on CIFAR10

Trained diffusion unet on CIFAR10 dataset(DDPM method).Test image num=50000.

sampling method input size steps condition label(train/test) FID IS score(mean/std)
DDPM 32x32 1000 False/False 5.394 8.684/0.169
DDIM 32x32 50 False/False 7.644 8.642/0.129
PLMS 32x32 20 False/False 7.027 8.834/0.200
DDPM 32x32 1000 True/True 3.949 8.985/0.139

You can find more model training details in diffusion_model_training/cifar10/.

All diffusion model with different sampling methods on CIFAR100

Trained diffusion unet on CIFAR100 dataset(DDPM method).Test image num=50000.

sampling method input size steps condition label(train/test) FID IS score(mean/std)
DDPM 32x32 1000 False/False 9.620 9.399/0.138
DDIM 32x32 50 False/False 13.250 8.946/0.150
PLMS 32x32 20 False/False 11.854 9.391/0.202
DDPM 32x32 1000 True/True 5.209 10.880/0.180

You can find more model training details in diffusion_model_training/cifar100/.

All diffusion model with different sampling methods on CelebA-HQ

Trained diffusion unet on CelebA-HQ dataset(DDPM method).Test image num=28000.

sampling method input size steps condition label(train/test) FID IS score(mean/std)
DDPM 64x64 1000 False/False 6.491 2.577/0.035
DDIM 64x64 50 False/False 15.195 2.625/0.028
PLMS 64x64 20 False/False 18.061 2.701/0.040

You can find more model training details in diffusion_model_training/celebahq/.

All diffusion model with different sampling methods on FFHQ

Trained diffusion unet on FFHQ dataset(DDPM method).Test image num=60000.

sampling method input size steps condition label(train/test) FID IS score(mean/std)
DDPM 64x64 1000 False/False 6.671 3.399/0.055
DDIM 64x64 50 False/False 10.479 3.431/0.044
PLMS 64x64 20 False/False 12.387 3.462/0.034

You can find more model training details in diffusion_model_training/ffhq/.