denglin-github / DLPaddleModelZoo

Denglin PaddlePaddle Model Zoo

PaddlePaddle Denglin Model Zoo

🤝 百度飞桨 x 登临科技AI模型库

• 兼容性适配：目前登临科技与百度飞桨深度学习框架已完成三级兼容性适配认证，支持当下主流模型应用场景，覆盖了计算机视觉、智能语音、自然语言处理、推荐、图神经网络和强化学习等领域，支持当下主流模型数量100+;
• 一键启动：通过兼容飞桨推理接口，用户通过指定enable_dlnne()接口一键启动模型，并部署在登临GPU上执行;
• 性能评估：开启enable_profile()接口即可评估模型性能;
• 支持拓展：用户可自行准备飞桨预训练inference模型，通过登临GPU实现加速推理;
• 其他特性：有关enable_dlnne()接口的是详细使用方法可参考Paddle-dlNNE;

📦 模型信息

PaddleClas

Models	Evaluate Datasets	Input shape	Acc(paddle)	Acc(Denglin GPU)	Latency(ms)(Denglin GPU,BS=1)	Inference Model
AlexNet	ImageNet1k	1x3x224x224	0.567	0.56644	8.388	inference_model
DenseNet121	ImageNet1k	1x3x224x224	0.7566	0.75666	9.889	inference_model
DLA102	ImageNet1k	1x3x224x224	0.7893	0.78926	14.285	inference_model
DLA34	ImageNet1k	1x3x224x224	0.7603	0.76028	5.676	inference_model
DLA46_c	ImageNet1k	1x3x224x224	0.6321	0.63210	2.488	inference_model
DLA60	ImageNet1k	1x3x224x224	0.7610	0.76102	8.660	inference_model
DLA60x_c	ImageNet1k	1x3x224x224	0.6645	0.66448	2.907	inference_model
DPN68	ImageNet1k	1x3x224x224	0.7678	0.76780	7.785	inference_model
DPN92	ImageNet1k	1x3x224x224	0.7985	0.79852	23.162	inference_model
ESNet_x0_25	ImageNet1k	1x3x224x224	0.6248	0.62462	9.464	inference_model
ESNet_x0_5	ImageNet1k	1x3x224x224	0.6882	0.68820	9.857	inference_model
GoogleNet	ImageNet1k	1x3x224x224	0.7070	0.70690	4.678	inference_model
HarDNet39_ds	ImageNet1k	1x3x224x224	0.7133	0.71332	2.968	inference_model
HarDNet68_ds	ImageNet1k	1x3x224x224	0.7362	0.73618	4.298	inference_model
HRNet_W18_C	ImageNet1k	1x3x224x224	0.7692	0.76890	18.502	inference_model
MixNet_S	ImageNet1k	1x3x224x224	0.7628	0.76282	10.055	inference_model
MobileNetV1	ImageNet1k	1x3x224x224	0.7099	0.71000	3.750	inference_model
MobileNetV2	ImageNet1k	1x3x224x224	0.7215	0.72156	3.251	inference_model
MobileNetV3_small_x0_35_ssld	ImageNet1k	1x3x224x224	0.5555	0.55606	2.271	inference_model
MobileNetV3_small_x0_5	ImageNet1k	1x3x224x224	0.5921	0.59192	2.484	inference_model
MobileNetV3_small_x0_75	ImageNet1k	1x3x224x224	0.6602	0.66050	3.150	inference_model
MobileNetV3_small_x1_25	ImageNet1k	1x3x224x224	0.7067	0.70654	4.297	inference_model
PPLCNet_x0_25	ImageNet1k	1x3x224x224	0.5186	0.51812	2.287	inference_model
PPLCNet_x0_35	ImageNet1k	1x3x224x224	0.5809	0.58088	2.747	inference_model
PPLCNet_x0_5	ImageNet1k	1x3x224x224	0.6314	0.63172	2.921	inference_model
PPLCNet_x1_0	ImageNet1k	1x3x224x224	0.7132	0.71312	4.591	inference_model
RedNet26	ImageNet1k	1x3x224x224	0.7595	0.75950	219.600	inference_model
RedNet38	ImageNet1k	1x3x224x224	0.7747	0.77470	333.226	inference_model
Res2Net50_14w_8s	ImageNet1k	1x3x224x224	0.7946	0.79462	11.200	inference_model
ResNet101_vd	ImageNet1k	1x3x224x224	0.8017	0.80178	12.615	inference_model
ResNet18	ImageNet1k	1x3x224x224	0.7098	0.70988	3.411	inference_model
ResNet50	ImageNet1k	1x3x224x224	0.7650	0.76502	7.597	inference_model
ResNeXt50_32x4d	ImageNet1k	1x3x224x224	0.7775	0.77754	9.849	inference_model
ReXNet_1_0	ImageNet1k	1x3x224x224	0.7746	0.77452	23.933	inference_model
ReXNet_1_3	ImageNet1k	1x3x224x224	0.7913	0.79134	28.345	inference_model
ReXNet_1_5	ImageNet1k	1x3x224x224	0.8006	0.80072	30.896	inference_model
ReXNet_2_0	ImageNet1k	1x3x224x224	0.8122	0.81242	39.879	inference_model
ReXNet_3_0	ImageNet1k	1x3x224x224	0.8209	0.82086	60.846	inference_model
SE_ResNet18_Vd	ImageNet1k	1x3x224x224	0.7333	0.73332	4.402	inference_model
SE_ResNet34_vd	ImageNet1k	1x3x224x224	0.7651	0.76518	7.116	inference_model
SE_ResNet50_vd	ImageNet1k	1x3x224x224	0.7952	0.79524	15.831	inference_model
ShuffleNetV2_x0_25	ImageNet1k	1x3x224x224	0.4990	0.49904	12.256	inference_model
ShuffleNetV2_x1_5	ImageNet1k	1x3x224x224	0.7163	0.71636	14.337	inference_model
SqueezeNet1_1	ImageNet1k	1x3x224x224	0.601	0.60076	2.295	inference_model
VGG11	ImageNet1k	1x3x224x224	0.693	0.69294	21.421	inference_model
VGG13	ImageNet1k	1x3x224x224	0.700	0.69994	24.873	inference_model
VGG19	ImageNet1k	1x3x224x224	0.726	0.72556	31.127	inference_model

PaddleOCR

Models	Evaluate Datasets	Input shape	Hmean(paddle)	Hmean(Denglin GPU)	Latency(ms)(Denglin GPU,BS=1)	Inference Model
det_mv3_db_v2.0	ICDAR2015	1x3x736x1280	0.7512	0.75092	96.365	inference_model
det_r50_vd_db_v2.0	ICDAR2015	1x3x736x1280	0.8238	0.82368	318.926	inference_model
det_mv3_east_v2.0	ICDAR2015	1x3x704x1280	0.7865	0.78680	74.671	inference_model
det_r50_vd_east_v2.0	ICDAR2015	1x3x704x1280	0.8488	0.84903	408.758	inference_model
det_r50_vd_sast_icdar15_v2.0	ICDAR2015	1x3x896x1536	0.8742	0.87415	1772.236	inference_model
det_mv3_pse_v2.0	ICDAR2015	1x3x736x1312	0.7589	0.75894	304.274	inference_model
det_r50_vd_pse_v2.0	ICDAR2015	1x3x736x1312	0.8255	0.82538	674.206	inference_model
rec_svtr_tiny_none_ctc_en	IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE	1x3x64x256	0.9013(Avg_10,acc)	0.90105 (acc)	6.564	inference_model

PaddleDetection

Models	Evaluate Datasets	Input shape	mAP(paddle)	mAP(Denglin GPU)	Latency(ms)(Denglin GPU,BS=1)	Inference Model
picodet_lcnet_1_5x_416_coco	coco	1x3x416x416	0.363	0.363	133.012	inference_model
picodet_s_320_coco	coco	1x3x320x320	0.271	0.271	66.497	inference_model
ppyolo_mbv3_large_coco	coco	1x3x320x320	0.232	0.240	27.512	inference_model
ppyolo_r50vd_dcn_1x_coco	coco	1x3x608x608	0.448	0.447	444.563	inference_model
ppyolo_tiny_650e_coco	coco	1x3x320x320	0.206	0.207	29.661	inference_model
ppyoloe_crn_s_300e_coco	coco	1x3x640x640	0.430	0.430	115.896	inference_model
ppyolov2_r50vd_dcn_365e_	coco	1x3x640x640	0.491	0.491	630.109	inference_model
ttfnet_darknet53_1x_coco	coco	1x3x512x512	0.335	0.336	413.021	inference_model
yolov3_darknet53_270e_coco	coco	1x3x608x608	0.391	0.391	279.647	inference_model
yolov3_mobilenet_v1_270e_coco	coco	1x3x608x608	0.294	0.294	136.460	inference_model
yolox_s_300e_coco	coco	1x3x640x640	0.404	0.404	142.276	inference_model

PaddleSeg

Models	Evaluate Datasets	Input shape	mIoU(paddle)	mIoU(Denglin GPU)	Latency(ms)(Denglin GPU,BS=1)	Inference Model
BiSeNetV1	Cityscapes	1x3x1024x2048	0.7519	0.75191	23283.000	inference_model
BiSeNetv2	Cityscapes	1x3x1024x2048	0.7319	0.73169	594.874	inference_model
CCNet	Cityscapes	1x3x1025x2049	0.8095	0.80951	6435.860	inference_model
DDRNet_23(DDRNet）	Cityscapes	1x3x1024x2048	0.7985	0.79847	729.794	inference_model
DeepLabv3p_resnet50_cityscapes	Cityscapes	1x3x1024x2048	0.8036	0.8036	3712.280	inference_model
ENet	Cityscapes	1x3x1024x2048	0.6742	0.67420	801.838	inference_model
FCN_HRNet_W18	飞桨内部人像数据集	1x3x1024x2048	0.787	0.78969	1580.298	inference_model
GloRe	Cityscapes	1x3x1024x2048	0.7826	0.78256	31732.400	inference_model
HRNetW48Contrast	Cityscapes	1x3x1024x2048	0.8230	0.82398	3544.080	inference_model
OCRNet_HRNetW18	Cityscapes	1x3x1024x2048	0.8067	0.80702	3801.400	inference_model
PFPNNet	Cityscapes	1x3x1024x2048	0.7907	0.79072	28974.200	inference_model
STDC_STDC1	Cityscapes	1x3x1024x2048	0.7474	0.74739	904.822	inference_model
UPERNet	ADE20K	1x3x1024x2048	0.7958	0.79581	8477.040	inference_model

PaddleNLP

Models	Evaluate Datasets	Sequence Length	Acc(paddle)	Acc(Denglin GPU)	Latency(ms)(Denglin GPU,BS=1)	Inference Model
BERT-Base	SST-2	128	0.92660	0.92661	20.455	inference_model
Bi-LSTM	ChnSentiCorp	599	0.8983	0.89833	25.231	inference_model
ConvBert	SST-2	128	0.9139	0.91399	102.281	inference_model
ELECTRA	SST-2	128	0.9185	0.91857	4.601	inference_model
LayoutLM	FUNSD	512	F1: 0.7913	F1: 0.79116	172.491	inference_model
MiniLMv2	AFQMC	128	0.7138	0.71362	9.536	inference_model
seq2seq	IWSLT15 en-vi	128	BLEU: 0.2433	BLEU: 0.24340	782.965	inference_model
TextCNN	ChnSentiCorp	599	0.9107	0.91000	1.273	inference_model
TinyBert	SST-2	128	0.9300	0.93005	20.583	inference_model

PaddleRec

Models	Evaluate Datasets	Metrics(paddle)	Metrics(Denglin GPU)	Latency(ms)(Denglin GPU,BS=1)	Inference Model
DSSM	BQ	0.93(正序率)	0.92875(正序率)	2.805	inference_model
match-pyramid	Letor07	0.39(map)	0.39296map)	0.895	inference_model
NCF	movielens	0.58(HR@10) 、0.33(NDCG@10)	0.58543(HR@10) 、 0.33538(NDCG@10)	0.699	inference_model
DLRM	criteo	Auc:0.79 +	0.80120	6.016	inference_model
DeepFM	Criteo	Auc:0.78	0.794357	1.357	inference_model

PARL

Models	Evaluate Datasets	Metrics	Reward(CPU)	Reward(Denglin GPU)	Latency(ms)(Denglin GPU,BS=1)	Inference Model
DQN_variant	Atari games	Reward	3.66667	3.66667	7.171	inference_model
PPO Atari	games	Reward	-21.0	-21.0	1.587	inference_model
DQN	CartPole-v0	Reward	19.0	19.0	0.350	inference_model
MADDPG	gym	Reward	-75.19758	-75.19758	0.768	inference_model

PGL

Models	Evaluate Datasets	Acc(paddle)	Acc(Denglin GPU)	Acc(CPU)	Latency(ms)(Denglin GPU,BS=1)	Inference Model
gin	MUTAG	--	0.78947	0.78947	10.529	inference_model
GraphSage	reddit	--	0.74706	0.74706	581.025	inference_model
gat	cora	0.83	0.83333	--	103.456	inference_model
gcn	CORA	0.81	0.81000	--	109.458	inference_model

PaddleSpeech

Models	Evaluate Datasets	Metrics	Acc(paddle)	Acc(Denglin GPU)	Acc(CPU)	Latency(ms)(Denglin GPU,BS=1)	Inference Model
hifigan	AISHELL-3	mel_loss	0.1068	--	0.10699	104.712	inference_model
Tacotron2	CSMSC	eval/loss	--	1.928438	1.928438	--	inference_model
Speedyspeech	CSMSC	eval/loss	--	0.879209	0.879209	146.011	inference_model

🎈 推理预测

以图像分类为例简要介绍模型使用方法，其他模型场景详细用法请参考飞桨官方模型库：

1.模型准备： 通过链接下载登临飞桨ImageNet1K图像分类模型，例如 MobileNetV3.pdmodel 、 MobileNetV3.pdiparams

2.数据准备： 输入图像应符合NCHW Format , Shape 为 [1,3,224,224]

3.执行推理：

python3 tools/deploy/predict.py                             \
    --model_file    ${MODEL_PATH}/MobileNetV3.pdmodel       \
    --params_file   ${MODEL_PATH}/MobileNetV3.pdiparams     \
    --input_data    ${INPUT_DATA}

4.获取最终推理结果，如图像类别、Bouding Box可视化、OCR检测结果等，可参考飞桨模型库相关代码