English | 简体中文
| Image (212) | Text (130) | Audio (15) | Video (8) | Industrial Application (1) |
|---|---|---|---|---|
| Image Classification (108) | Text Generation (17) | Voice Cloning (2) | Video Classification (5) | Meter Detection (1) |
| Image Generation (26) | Word Embedding (62) | Text to Speech (5) | Video Editing (1) | - |
| Keypoint Detection (5) | Machine Translation (2) | Automatic Speech Recognition (5) | Multiple Object tracking (2) | - |
| Semantic Segmentation (25) | Language Model (30) | Audio Classification (3) | - | - |
| Face Detection (7) | Sentiment Analysis (7) | - | - | - |
| Text Recognition (17) | Syntactic Analysis (1) | - | - | - |
| Image Editing (8) | Simultaneous Translation (5) | - | - | - |
| Instance Segmentation (1) | Lexical Analysis (2) | - | - | - |
| Object Detection (13) | Punctuation Restoration (1) | - | - | - |
| Depth Estimation (2) | Text Review (3) | - | - | - |
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| module | Network | Dataset | Introduction |
|---|---|---|---|
| DriverStatusRecognition | MobileNetV3_small_ssld | Drivers | |
| mobilenet_v2_animals | MobileNet_v2 | Animals | |
| repvgg_a1_imagenet | RepVGG | ImageNet-2012 | |
| repvgg_a0_imagenet | RepVGG | ImageNet-2012 | |
| resnext152_32x4d_imagenet | ResNeXt | ImageNet-2012 | |
| resnet_v2_152_imagenet | ResNet V2 | ImageNet-2012 | |
| resnet50_vd_animals | ResNet50_vd | Animals | |
| food_classification | ResNet50_vd_ssld | dishes | |
| mobilenet_v3_large_imagenet_ssld | Mobilenet_v3_large | ImageNet-2012 | |
| resnext152_vd_32x4d_imagenet | |||
| ghostnet_x1_3_imagenet_ssld | GhostNet | ImageNet-2012 | |
| rexnet_1_5_imagenet | ReXNet | ImageNet-2012 | |
| resnext50_64x4d_imagenet | ResNeXt | ImageNet-2012 | |
| resnext101_64x4d_imagenet | ResNeXt | ImageNet-2012 | |
| efficientnetb0_imagenet | EfficientNet | ImageNet-2012 | |
| efficientnetb1_imagenet | EfficientNet | ImageNet-2012 | |
| mobilenet_v2_imagenet_ssld | Mobilenet_v2 | ImageNet-2012 | |
| resnet50_vd_dishes | ResNet50_vd | dishes | |
| pnasnet_imagenet | PNASNet | ImageNet-2012 | |
| rexnet_2_0_imagenet | ReXNet | ImageNet-2012 | |
| SnakeIdentification | ResNet50_vd_ssld | snakes | |
| hrnet40_imagenet | HRNet | ImageNet-2012 | |
| resnet_v2_34_imagenet | ResNet V2 | ImageNet-2012 | |
| mobilenet_v2_dishes | MobileNet_v2 | dishes | |
| resnext101_vd_32x4d_imagenet | ResNeXt | ImageNet-2012 | |
| repvgg_b2g4_imagenet | RepVGG | ImageNet-2012 | |
| fix_resnext101_32x48d_wsl_imagenet | ResNeXt | ImageNet-2012 | |
| vgg13_imagenet | VGG | ImageNet-2012 | |
| se_resnext101_32x4d_imagenet | SE_ResNeXt | ImageNet-2012 | |
| hrnet30_imagenet | HRNet | ImageNet-2012 | |
| ghostnet_x1_3_imagenet | GhostNet | ImageNet-2012 | |
| dpn107_imagenet | DPN | ImageNet-2012 | |
| densenet161_imagenet | DenseNet | ImageNet-2012 | |
| vgg19_imagenet | vgg19_imagenet | ImageNet-2012 | |
| mobilenet_v2_imagenet | Mobilenet_v2 | ImageNet-2012 | |
| resnet50_vd_10w | ResNet_vd | private | |
| resnet_v2_101_imagenet | ResNet V2 101 | ImageNet-2012 | |
| darknet53_imagenet | DarkNet | ImageNet-2012 | |
| se_resnext50_32x4d_imagenet | SE_ResNeXt | ImageNet-2012 | |
| se_hrnet64_imagenet_ssld | HRNet | ImageNet-2012 | |
| resnext101_32x16d_wsl | ResNeXt_wsl | ImageNet-2012 | |
| hrnet18_imagenet | HRNet | ImageNet-2012 | |
| spinalnet_res101_gemstone | resnet101 | gemstone | |
| densenet264_imagenet | DenseNet | ImageNet-2012 | |
| resnext50_vd_32x4d_imagenet | ResNeXt_vd | ImageNet-2012 | |
| SpinalNet_Gemstones | |||
| spinalnet_vgg16_gemstone | vgg16 | gemstone | |
| xception71_imagenet | Xception | ImageNet-2012 | |
| repvgg_b2_imagenet | RepVGG | ImageNet-2012 | |
| dpn68_imagenet | DPN | ImageNet-2012 | |
| alexnet_imagenet | AlexNet | ImageNet-2012 | |
| rexnet_1_3_imagenet | ReXNet | ImageNet-2012 | |
| hrnet64_imagenet | HRNet | ImageNet-2012 | |
| efficientnetb7_imagenet | EfficientNet | ImageNet-2012 | |
| efficientnetb0_small_imagenet | EfficientNet | ImageNet-2012 | |
| efficientnetb6_imagenet | EfficientNet | ImageNet-2012 | |
| hrnet48_imagenet | HRNet | ImageNet-2012 | |
| rexnet_3_0_imagenet | ReXNet | ImageNet-2012 | |
| shufflenet_v2_imagenet | ShuffleNet V2 | ImageNet-2012 | |
| ghostnet_x0_5_imagenet | GhostNet | ImageNet-2012 | |
| inception_v4_imagenet | Inception_V4 | ImageNet-2012 | |
| resnext101_vd_64x4d_imagenet | ResNeXt_vd | ImageNet-2012 | |
| densenet201_imagenet | DenseNet | ImageNet-2012 | |
| vgg16_imagenet | VGG | ImageNet-2012 | |
| mobilenet_v3_small_imagenet_ssld | Mobilenet_v3_Small | ImageNet-2012 | |
| hrnet18_imagenet_ssld | HRNet | ImageNet-2012 | |
| resnext152_64x4d_imagenet | ResNeXt | ImageNet-2012 | |
| efficientnetb3_imagenet | EfficientNet | ImageNet-2012 | |
| efficientnetb2_imagenet | EfficientNet | ImageNet-2012 | |
| repvgg_b1g4_imagenet | RepVGG | ImageNet-2012 | |
| resnext101_32x4d_imagenet | ResNeXt | ImageNet-2012 | |
| resnext50_32x4d_imagenet | ResNeXt | ImageNet-2012 | |
| repvgg_a2_imagenet | RepVGG | ImageNet-2012 | |
| resnext152_vd_64x4d_imagenet | ResNeXt_vd | ImageNet-2012 | |
| xception41_imagenet | Xception | ImageNet-2012 | |
| googlenet_imagenet | GoogleNet | ImageNet-2012 | |
| resnet50_vd_imagenet_ssld | ResNet_vd | ImageNet-2012 | |
| repvgg_b1_imagenet | RepVGG | ImageNet-2012 | |
| repvgg_b0_imagenet | RepVGG | ImageNet-2012 | |
| resnet_v2_50_imagenet | ResNet V2 | ImageNet-2012 | |
| rexnet_1_0_imagenet | ReXNet | ImageNet-2012 | |
| resnet_v2_18_imagenet | ResNet V2 | ImageNet-2012 | |
| resnext101_32x8d_wsl | ResNeXt_wsl | ImageNet-2012 | |
| efficientnetb4_imagenet | EfficientNet | ImageNet-2012 | |
| efficientnetb5_imagenet | EfficientNet | ImageNet-2012 | |
| repvgg_b1g2_imagenet | RepVGG | ImageNet-2012 | |
| resnext101_32x48d_wsl | ResNeXt_wsl | ImageNet-2012 | |
| resnet50_vd_wildanimals | ResNet_vd | IFAW wild animals | |
| nasnet_imagenet | NASNet | ImageNet-2012 | |
| se_resnet18_vd_imagenet | |||
| spinalnet_res50_gemstone | resnet50 | gemstone | |
| resnext50_vd_64x4d_imagenet | ResNeXt_vd | ImageNet-2012 | |
| resnext101_32x32d_wsl | ResNeXt_wsl | ImageNet-2012 | |
| dpn131_imagenet | DPN | ImageNet-2012 | |
| xception65_imagenet | Xception | ImageNet-2012 | |
| repvgg_b3g4_imagenet | RepVGG | ImageNet-2012 | |
| marine_biometrics | ResNet50_vd_ssld | Fish4Knowledge | |
| res2net101_vd_26w_4s_imagenet | Res2Net | ImageNet-2012 | |
| dpn98_imagenet | DPN | ImageNet-2012 | |
| resnet18_vd_imagenet | ResNet_vd | ImageNet-2012 | |
| densenet121_imagenet | DenseNet | ImageNet-2012 | |
| vgg11_imagenet | VGG | ImageNet-2012 | |
| hrnet44_imagenet | HRNet | ImageNet-2012 | |
| densenet169_imagenet | DenseNet | ImageNet-2012 | |
| hrnet32_imagenet | HRNet | ImageNet-2012 | |
| dpn92_imagenet | DPN | ImageNet-2012 | |
| ghostnet_x1_0_imagenet | GhostNet | ImageNet-2012 | |
| hrnet48_imagenet_ssld | HRNet | ImageNet-2012 |
| module | Network | Dataset | Introduction | Huggingface Spaces Demo |
|---|---|---|---|---|
| pixel2style2pixel | Pixel2Style2Pixel | - | human face | |
| stgan_bald | STGAN | CelebA | stgan_bald | |
| styleganv2_editing | StyleGAN V2 | - | human face editing | |
| wav2lip | wav2lip | LRS2 | wav2lip | |
| attgan_celeba | AttGAN | Celeba | human face editing | |
| cyclegan_cityscapes | CycleGAN | Cityscapes | cyclegan_cityscapes | |
| stargan_celeba | StarGAN | Celeba | human face editing | |
| stgan_celeba | STGAN | Celeba | human face editing | |
| ID_Photo_GEN | HRNet_W18 | - | ID_Photo_GEN | |
| Photo2Cartoon | U-GAT-IT | cartoon_data | cartoon | |
| U2Net_Portrait | U^2Net | - | Portrait | |
| UGATIT_100w | U-GAT-IT | selfie2anime | selfie2anime | |
| UGATIT_83w | U-GAT-IT | selfie2anime | selfie2anime | |
| UGATIT_92w | U-GAT-IT | selfie2anime | selfie2anime | |
| animegan_v1_hayao_60 | AnimeGAN | The Wind Rises | animegan_v1_hayao | |
| animegan_v2_hayao_64 | AnimeGAN | The Wind Rises | animegan_v1_hayao | |
| animegan_v2_hayao_99 | AnimeGAN | The Wind Rises | animegan_v1_hayao | |
| animegan_v2_paprika_54 | AnimeGAN | Paprika | animegan_v2_paprika | |
| animegan_v2_paprika_74 | AnimeGAN | Paprika | animegan_v2_paprika | |
| animegan_v2_paprika_97 | AnimeGAN | Paprika | animegan_v2_paprika | |
| animegan_v2_paprika_98 | AnimeGAN | Paprika | animegan_v2_paprika | |
| animegan_v2_shinkai_33 | AnimeGAN | Your Name, Weathering with you | animegan_v2_shinkai | |
| animegan_v2_shinkai_53 | AnimeGAN | Your Name, Weathering with you | animegan_v2_shinkai | |
| msgnet | msgnet | COCO2014 | ||
| stylepro_artistic | StyleProNet | MS-COCO + WikiArt | stylepro_artistic | |
| stylegan_ffhq | StyleGAN | FFHQ | stylepro_artistic |
| module | Network | Dataset | Introduction |
|---|---|---|---|
| face_landmark_localization | Face_Landmark | AFW/AFLW | Face_Landmark |
| hand_pose_localization | - | MPII, NZSL | hand_pose_localization |
| openpose_body_estimation | two-branch multi-stage CNN | MPII, COCO 2016 | openpose_body_estimation |
| human_pose_estimation_resnet50_mpii | Pose_Resnet50 | MPII | human_pose_estimation |
| openpose_hands_estimation | - | MPII, NZSL | openpose_hands_estimation |
| module | Network | Dataset | Introduction |
|---|---|---|---|
| deeplabv3p_xception65_humanseg | deeplabv3p | - | humanseg |
| humanseg_server | deeplabv3p | - | humanseg |
| humanseg_mobile | hrnet | - | humanseg |
| humanseg_lite | shufflenet | - | humanseg |
| ExtremeC3_Portrait_Segmentation | ExtremeC3 | EG1800, Baidu fashion dataset | humanseg |
| SINet_Portrait_Segmentation | SINet | EG1800, Baidu fashion dataset | humanseg |
| FCN_HRNet_W18_Face_Seg | FCN_HRNet_W18 | - | humanseg |
| ace2p | ACE2P | LIP | ACE2P |
| Pneumonia_CT_LKM_PP | U-NET+ | - | Pneumonia_CT |
| Pneumonia_CT_LKM_PP_lung | U-NET+ | - | Pneumonia_CT |
| ocrnet_hrnetw18_voc | ocrnet, hrnet | PascalVoc2012 | |
| U2Net | U^2Net | - | U2Net |
| U2Netp | U^2Net | - | U2Net |
| Extract_Line_Draft | UNet | Pixiv | Extract_Line_Draft |
| unet_cityscapes | UNet | cityscapes | |
| ocrnet_hrnetw18_cityscapes | ocrnet_hrnetw18 | cityscapes | |
| hardnet_cityscapes | hardnet | cityscapes | |
| fcn_hrnetw48_voc | fcn_hrnetw48 | PascalVoc2012 | |
| fcn_hrnetw48_cityscapes | fcn_hrnetw48 | cityscapes | |
| fcn_hrnetw18_voc | fcn_hrnetw18 | PascalVoc2012 | |
| fcn_hrnetw18_cityscapes | fcn_hrnetw18 | cityscapes | |
| fastscnn_cityscapes | fastscnn | cityscapes | |
| deeplabv3p_resnet50_voc | deeplabv3p, resnet50 | PascalVoc2012 | |
| deeplabv3p_resnet50_cityscapes | deeplabv3p, resnet50 | cityscapes | |
| bisenetv2_cityscapes | bisenetv2 | cityscapes |
| module | Network | Dataset | Introduction |
|---|---|---|---|
| pyramidbox_lite_mobile | PyramidBox | WIDER FACE | face_detection |
| pyramidbox_lite_mobile_mask | PyramidBox | WIDER FACE | face_detection |
| pyramidbox_lite_server_mask | PyramidBox | WIDER FACE | face_detection |
| ultra_light_fast_generic_face_detector_1mb_640 | Ultra-Light-Fast-Generic-Face-Detector-1MB | WIDER FACE | face_detection |
| ultra_light_fast_generic_face_detector_1mb_320 | Ultra-Light-Fast-Generic-Face-Detector-1MB | WIDER FACE | face_detection |
| pyramidbox_lite_server | PyramidBox | WIDER FACE | face_detection |
| pyramidbox_face_detection | PyramidBox | WIDER FACE | face_detection |
| module | Network | Dataset | Introduction | Huggingface Spaces Demo |
|---|---|---|---|---|
| chinese_ocr_db_crnn_mobile | Differentiable Binarization+RCNN | icdar2015 | Chinese text recognition | |
| chinese_text_detection_db_mobile | Differentiable Binarization | icdar2015 | Chinese text Detection | |
| chinese_text_detection_db_server | Differentiable Binarization | icdar2015 | Chinese text Detection | |
| chinese_ocr_db_crnn_server | Differentiable Binarization+RCNN | icdar2015 | Chinese text recognition | |
| Vehicle_License_Plate_Recognition | - | CCPD | Vehicle license plate recognition | |
| chinese_cht_ocr_db_crnn_mobile | Differentiable Binarization+CRNN | icdar2015 | Traditional Chinese text Detection | |
| japan_ocr_db_crnn_mobile | Differentiable Binarization+CRNN | icdar2015 | Japanese text recognition | |
| korean_ocr_db_crnn_mobile | Differentiable Binarization+CRNN | icdar2015 | Korean text recognition | |
| german_ocr_db_crnn_mobile | Differentiable Binarization+CRNN | icdar2015 | German text recognition | |
| french_ocr_db_crnn_mobile | Differentiable Binarization+CRNN | icdar2015 | French text recognition | |
| latin_ocr_db_crnn_mobile | Differentiable Binarization+CRNN | icdar2015 | Latin text recognition | |
| cyrillic_ocr_db_crnn_mobile | Differentiable Binarization+CRNN | icdar2015 | Cyrillic text recognition | |
| multi_languages_ocr_db_crnn | Differentiable Binarization+RCNN | icdar2015 | Multi languages text recognition | |
| kannada_ocr_db_crnn_mobile | Differentiable Binarization+CRNN | icdar2015 | Kannada text recognition | |
| arabic_ocr_db_crnn_mobile | Differentiable Binarization+CRNN | icdar2015 | Arabic text recognition | |
| telugu_ocr_db_crnn_mobile | Differentiable Binarization+CRNN | icdar2015 | Telugu text recognition | |
| devanagari_ocr_db_crnn_mobile | Differentiable Binarization+CRNN | icdar2015 | Devanagari text recognition | |
| tamil_ocr_db_crnn_mobile | Differentiable Binarization+CRNN | icdar2015 | Tamil text recognition |
| module | Network | Dataset | Introduction | Huggingface Spaces Demo |
|---|---|---|---|---|
| realsr | LP-KPN | RealSR dataset | Image / Video super-resolution | |
| deoldify | GAN | ILSVRC 2012 | Black-and-white image / video colorization | |
| photo_restoration | deoldify + realsr | - | Old photo restoration | |
| user_guided_colorization | siggraph | ILSVRC 2012 | User guided colorization | |
| falsr_c | falsr_c | DIV2k | Lightweight super resolution - 2x | |
| dcscn | dcscn | DIV2k | Lightweight super resolution - 2x | |
| falsr_a | falsr_a | DIV2k | Lightweight super resolution - 2x | |
| falsr_b | falsr_b | DIV2k | Lightweight super resolution - 2x |
| module | Network | Dataset | Introduction |
|---|---|---|---|
| solov2 | - | COCO2014 | Instance segmentation |
| module | Network | Dataset | Introduction |
|---|---|---|---|
| faster_rcnn_resnet50_coco2017 | faster_rcnn | COCO2017 | |
| ssd_vgg16_512_coco2017 | SSD | COCO2017 | |
| faster_rcnn_resnet50_fpn_venus | faster_rcnn | Baidu self built dataset | Large-scale general detection |
| ssd_vgg16_300_coco2017 | |||
| yolov3_resnet34_coco2017 | YOLOv3 | COCO2017 | |
| yolov3_darknet53_pedestrian | YOLOv3 | Baidu Self built large-scale pedestrian dataset | Pedestrian Detection |
| yolov3_mobilenet_v1_coco2017 | YOLOv3 | COCO2017 | |
| ssd_mobilenet_v1_pascal | SSD | PASCAL VOC | |
| faster_rcnn_resnet50_fpn_coco2017 | faster_rcnn | COCO2017 | |
| yolov3_darknet53_coco2017 | YOLOv3 | COCO2017 | |
| yolov3_darknet53_vehicles | YOLOv3 | Baidu Self built large-scale vehicles dataset | vehicles Detection |
| yolov3_darknet53_venus | YOLOv3 | Baidu self built datasetset | Large-scale general detection |
| yolov3_resnet50_vd_coco2017 | YOLOv3 | COCO2017 |
| module | Network | Dataset | Introduction | Huggingface Spaces Demo |
|---|---|---|---|---|
| MiDaS_Large | - | 3D Movies, WSVD, ReDWeb, MegaDepth | ||
| MiDaS_Small | - | 3D Movies, WSVD, ReDWeb, MegaDepth, etc. |
| module | Network | Dataset | Introduction | Huggingface Spaces Demo |
|---|---|---|---|---|
| disco_diffusion_clip_rn101 | - | Open domain multi round dataset | text_to_image | |
| ernie_vilg | - | Open domain multi round dataset | text_to_image | |
| stable_diffusion_img2img | - | Open domain multi round dataset | img2img |
| module | Network | Dataset | Introduction |
|---|---|---|---|
| ernie_gen | ERNIE-GEN | - | Pre-training finetuning framework for generating tasks |
| ernie_gen_poetry | ERNIE-GEN | Open source poetry dataset | Poetry generation |
| ernie_gen_couplet | ERNIE-GEN | Open source couplet dataset | Couplet generation |
| ernie_gen_lover_words | ERNIE-GEN | Online love poems and love talk data | Love word generation |
| ernie_tiny_couplet | Eernie_tiny | Open source couplet dataset | Couplet generation |
| ernie_gen_acrostic_poetry | ERNIE-GEN | Open source poetry dataset | Acrostic poetry Generation |
| Rumor_prediction | - | Sina Weibo Chinese rumor data | Rumor prediction |
| plato-mini | Unified Transformer | Billion level Chinese conversation data | Chinese dialogue |
| plato2_en_large | plato2 | Open domain multi round dataset | Super large scale generative dialogue |
| plato2_en_base | plato2 | Open domain multi round dataset | Super large scale generative dialogue |
| CPM_LM | GPT-2 | Self built dataset | Chinese text generation |
| unified_transformer-12L-cn | Unified Transformer | Ten million level Chinese conversation data | Man machine multi round dialogue |
| unified_transformer-12L-cn-luge | Unified Transformer | dialogue dataset | Man machine multi round dialogue |
| reading_pictures_writing_poems | Multi network cascade | - | Look at pictures and write poems |
| GPT2_CPM_LM | Q&A text generation | ||
| GPT2_Base_CN | Q&A text generation |
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| module | Network | Dataset | Introduction |
|---|---|---|---|
| transformer_zh-en | Transformer | CWMT2021 | 中文译英文 |
| transformer_en-de | Transformer | WMT14 EN-DE | 英文译德文 |
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| module | Network | Dataset | Introduction |
|---|---|---|---|
| chinese_electra_small | |||
| chinese_electra_base | |||
| roberta-wwm-ext-large | roberta-wwm-ext-large | Baidu self built dataset | |
| chinese-bert-wwm-ext | chinese-bert-wwm-ext | Baidu self built dataset | |
| lda_webpage | LDA | Baidu Self built Web Page Domain Dataset | |
| lda_novel | |||
| bert-base-multilingual-uncased | |||
| rbt3 | |||
| ernie_v2_eng_base | ernie_v2_eng_base | Baidu self built dataset | |
| bert-base-multilingual-cased | |||
| rbtl3 | |||
| chinese-bert-wwm | chinese-bert-wwm | Baidu self built dataset | |
| bert-large-uncased | |||
| slda_novel | |||
| slda_news | |||
| electra_small | |||
| slda_webpage | |||
| bert-base-cased | |||
| slda_weibo | |||
| roberta-wwm-ext | roberta-wwm-ext | Baidu self built dataset | |
| bert-base-uncased | |||
| electra_large | |||
| ernie | ernie-1.0 | Baidu self built dataset | |
| simnet_bow | BOW | Baidu self built dataset | |
| ernie_tiny | ernie_tiny | Baidu self built dataset | |
| bert-base-chinese | bert-base-chinese | Baidu self built dataset | |
| lda_news | LDA | Baidu Self built News Field Dataset | |
| electra_base | |||
| ernie_v2_eng_large | ernie_v2_eng_large | Baidu self built dataset | |
| bert-large-cased |
| module | Network | Dataset | Introduction | Huggingface Spaces Demo |
|---|---|---|---|---|
| ernie_skep_sentiment_analysis | SKEP | Baidu self built dataset | Sentence level sentiment analysis | |
| emotion_detection_textcnn | TextCNN | Baidu self built dataset | Dialogue emotion detection | |
| senta_bilstm | BiLSTM | Baidu self built dataset | Chinesesentiment analysis | |
| senta_bow | BOW | Baidu self built dataset | Chinese sentiment analysis | |
| senta_gru | GRU | Baidu self built dataset | Chinese sentiment analysis | |
| senta_lstm | LSTM | Baidu self built dataset | Chinese sentiment analysis | |
| senta_cnn | CNN | Baidu self built dataset | Chinese sentiment analysis |
| module | Network | Dataset | Introduction |
|---|---|---|---|
| DDParser | Deep Biaffine Attention | Search query, web text, voice input and other data | Syntactic analysis |
| module | Network | Dataset | Introduction |
|---|---|---|---|
| transformer_nist_wait_1 | transformer | NIST 2008 | Chinese to English - wait-1 |
| transformer_nist_wait_3 | transformer | NIST 2008 | Chinese to English - wait-3 |
| transformer_nist_wait_5 | transformer | NIST 2008 | Chinese to English - wait-5 |
| transformer_nist_wait_7 | transformer | NIST 2008 | Chinese to English - wait-7 |
| transformer_nist_wait_all | transformer | NIST 2008 | Chinese to English - waitk=-1 |
| module | Network | Dataset | Introduction | Huggingface Spaces Demo |
|---|---|---|---|---|
| jieba_paddle | BiGRU+CRF | Baidu self built dataset | Jieba uses Paddle to build a word segmentation network (two-way GRU). At the same time, it supports traditional word segmentation methods of jieba, such as precise mode, full mode, search engine mode, etc. | |
| lac | BiGRU+CRF | Baidu self built dataset | The lexical analysis model jointly developed by Baidu can complete the tasks of Chinese word segmentation, part of speech tagging and proper name recognition as a whole. Evaluated on Baidu self built dataset, LAC effect: Precision=88.0%, Recall=88.7%, F1 Score=88.4%. |
| module | Network | Dataset | Introduction |
|---|---|---|---|
| auto_punc | Ernie-1.0 | WuDaoCorpora 2.0 | Automatically add 7 punctuation marks |
| module | Network | Dataset | Introduction |
|---|---|---|---|
| porn_detection_cnn | CNN | Baidu self built dataset | Pornography detection, automatically identify whether the text is pornographic and give the corresponding confidence, and identify pornographic descriptions, vulgar friends, and dirty documents in the text |
| porn_detection_gru | GRU | Baidu self built dataset | Pornography detection, automatically identify whether the text is pornographic and give the corresponding confidence, and identify pornographic descriptions, vulgar friends, and dirty documents in the text |
| porn_detection_lstm | LSTM | Baidu self built dataset | Pornography detection, automatically identify whether the text is pornographic and give the corresponding confidence, and identify pornographic descriptions, vulgar friends, and dirty documents in the text |
| module | Network | Dataset | Introduction |
|---|---|---|---|
| ge2e_fastspeech2_pwgan | FastSpeech2 | AISHELL-3 | Chinese speech cloning |
| lstm_tacotron2 | LSTM、Tacotron2、WaveFlow | AISHELL-3 | Chinese speech cloning |
| module | Network | Dataset | Introduction |
|---|---|---|---|
| transformer_tts_ljspeech | Transformer | LJSpeech-1.1 | English speech synthesis |
| fastspeech_ljspeech | FastSpeech | LJSpeech-1.1 | English speech synthesis |
| fastspeech2_baker | FastSpeech2 | Chinese Standard Mandarin Speech Copus | Chinese speech synthesis |
| fastspeech2_ljspeech | FastSpeech2 | LJSpeech-1.1 | English speech synthesis |
| deepvoice3_ljspeech | DeepVoice3 | LJSpeech-1.1 | English speech synthesis |
| module | Network | Dataset | Introduction |
|---|---|---|---|
| deepspeech2_aishell | DeepSpeech2 | AISHELL-1 | Chinese Speech Recognition |
| deepspeech2_librispeech | DeepSpeech2 | LibriSpeech | English Speech Recognition |
| u2_conformer_aishell | Conformer | AISHELL-1 | Chinese Speech Recognition |
| u2_conformer_wenetspeech | Conformer | WenetSpeech | Chinese Speech Recognition |
| u2_conformer_librispeech | Conformer | LibriSpeech | English Speech Recognition |
| module | Network | Dataset | Introduction |
|---|---|---|---|
| panns_cnn6 | PANNs | Google Audioset | It mainly includes 4 convolution layers and 2 full connection layers, and the model parameter is 4.5M. After pre-training, it can be used to extract the embbedding of audio. The dimension is 512 |
| panns_cnn14 | PANNs | Google Audioset | It mainly includes 4 convolution layers and 2 full connection layers, and the model parameter is 4.5M. After pre-training, it can be used to extract the embbedding of audio. The dimension is 2048 |
| panns_cnn10 | PANNs | Google Audioset | It mainly includes 4 convolution layers and 2 full connection layers, and the model parameter is 4.5M. After pre-training, it can be used to extract the embbedding of audio. The dimension is 512 |
| module | Network | Dataset | Introduction |
|---|---|---|---|
| videotag_tsn_lstm | TSN + AttentionLSTM | Baidu self built dataset | Short-video classification |
| tsn_kinetics400 | TSN | Kinetics-400 | Video classification |
| tsm_kinetics400 | TSM | Kinetics-400 | Video classification |
| stnet_kinetics400 | StNet | Kinetics-400 | Video classification |
| nonlocal_kinetics400 | Non-local | Kinetics-400 | Video classification |
| module | Network | Dataset | Introduction |
|---|---|---|---|
| SkyAR | UNet | UNet | Video sky Replacement |
| module | Network | Dataset | Introduction |
|---|---|---|---|
| fairmot_dla34 | CenterNet | Caltech Pedestrian+CityPersons+CUHK-SYSU+PRW+ETHZ+MOT17 | Realtime multiple object tracking |
| jde_darknet53 | YOLOv3 | Caltech Pedestrian+CityPersons+CUHK-SYSU+PRW+ETHZ+MOT17 | object tracking with both accuracy and speed |
| module | Network | Dataset | Introduction |
|---|---|---|---|
| WatermeterSegmentation | DeepLabV3 | Water meter dataset | Water meter segmentation |