Skip to content
Branch: master
Find file History
Latest commit 0bf13b9 Dec 4, 2019
Permalink
Type Name Latest commit message Commit time
..
Failed to load latest commit information.
images Updates for Vitis AI 1.0 release Dec 2, 2019
LICENSE Updated file structure Nov 27, 2019
README.md Update README.md Dec 4, 2019
get_model.sh Updates for Vitis AI 1.0 release Dec 2, 2019

README.md

Vitis AI Model Zoo

Introduction

This repository includes optimized deep learning models to speed up the deployment of deep learning inference on Xilinx™ platforms. These models cover different applications, including but not limited to ADAS/AD, video surveillance, robotics, data center, etc. You can get started with these free pre-trained models to enjoy the benefits of deep learning acceleration.

Model Information

The following table includes comprehensive information about each model, including application, framework, training and validation dataset, backbone, input size, computation as well as float and quantized precision.

Click here to view details
No. Application Model Name Framework Backbone Input Size OPS per image Training Set Val Set Float (Top1, Top5)/ mAP/mIoU Quantized (Top1, Top5)/mAP/mIoU
1 Image Classification resnet50 cf_resnet50_imagenet_224_224_7.7G caffe resnet50 224*224 7.7G ImageNet Train ImageNet Validataion 0.74828/0.92135 0.7338/0.9130
2 Image Classifiction resnet18 cf_resnet18_imagenet_224_224_3.65G caffe resnet18 224*224 3.65G ImageNet Train ImageNet Validataion 68.44%/88.64% 66.94%/88.25%
3 Image Classification Inception_v1 cf_inceptionv1_imagenet_224_224_3.16G caffe inception_v1 224*224 3.16G ImageNet Train ImageNet Validataion 0.689/0.897 0.69882/0.894122
4 Image Classification Inception_v2 cf_inceptionv2_imagenet_224_224_4G caffe bn-inception 224*224 4G ImageNet Train ImageNet Validataion 0.7283/0.9109 0.7170/0.9033
5 Image Classification Inception_v3 cf_inceptionv3_imagenet_299_299_11.4G caffe inception_v3 299*299 11.4G ImageNet Train ImageNet Validataion 0.77058/0.93326 0.76264/0.930322
6 Image Classification Inception_v4 cf_inceptionv4_imagenet_299_299_24.5G caffe inception_v3 299*299 24.5G ImageNet Train ImageNet Validataion 0.7959/0.9470 0.7899/0.9445
7 Image Classification mobileNet_v2 cf_mobilenetv2_imagenet_224_224_0.59G caffe MobileNet_v2 224*224 608M ImageNet Train ImageNet Validataion 0.6649/0.872362 0.635219/0.850701
8 Image Classifiction SqueezeNet cf_squeeze_imagenet_227_227_0.76G caffe squeezenet 227*227 0.76G ImageNet Train ImageNet Validataion 54.64%/78.20% 50.69%/77.01%
9 ADAS Pedstrain Detection ssd_pedestrain_pruned_0.97 cf_ssdpedestrian_coco_360_640_0.97_5.9G caffe VGG-bn-16 360*640 5.9G coco2014_train_person and crowndhuman coco2014_val_person 0.5899 0.585
10 Object Detection refinedet_pruned_0.8 cf_refinedet_coco_360_480_0.8_25G caffe VGG-bn-16 360*480 25G coco2014_train_person coco2014_val_person 67.68% 67.47%
11 Object Detection refinedet_pruned_0.92 cf_refinedet_coco_360_480_0.92_10.10G caffe VGG-bn-16 360*480 10.10G coco2014_train_person coco2014_val_person 64.60% 64.50%
12 Object Detection refinedet_pruned_0.96 cf_refinedet_coco_360_480_0.96_5.08G caffe VGG-bn-16 360*480 5.08G coco2014_train_person coco2014_val_person 60.89% 60.65%
13 ADAS Vehicle Detection ssd_adas_pruned_0.95 cf_ssdadas_bdd_360_480_0.95_6.3G caffe VGG-16 360*480 6.3G bdd100k + private data bdd100k + private data 0.426 0.424
14 Traffic Detection ssd_traffic_pruned_0.9 cf_ssdtraffic_360_480_0.9_11.6G caffe VGG-16 360*480 11.6G private data private data 0.602 0.588
15 ADAS Lane Detection VPGnet_pruned_0.99 cf_VPGnet_caltechlane_480_640_0.99_2.5G caffe VGG 480*640 2.5G caltech-lanes-train-dataset caltech lane 88.639%(F1-score) 87%(F1-score)
16 Object Detection ssd_mobilnet_v2 cf_ssdmobilenetv2_bdd_360_480_6.57G caffe MobileNet_v2 360*480 6.57G bdd100k train bdd100k val 0.3186 0.3019
17 ADAS Segmentation FPN cf_fpn_cityscapes_256_512_8.9G caffe Google_v1_BN 256*512 8.9G Cityscapes gtFineTrain(2975) Cityscapes Val(500) 0.5669 0.5645
18 Pose Estimation SP-net cf_SPnet_aichallenger_224_128_0.54G caffe Google_v1_BN 128*224 548.6M ai_challenger ai_challenger 88.2%(PCKh0.5) 87.86%(PCKh0.5)
19 Pose Estimation Openpose_pruned_0.3 cf_openpose_aichallenger_368_368_0.3_189.7G caffe VGG 368*368 49.88G ai_challenger ai_challenger 0.45067(OKs) 0.44287(Oks)
20 Face Detection densebox_320_320 cf_densebox_wider_320_320_0.49G caffe VGG-16 320*320 0.49G wider_face FDDB 0.8818 0.8768
21 Face Detection densebox_360_640 cf_densebox_wider_360_640_1.11G caffe VGG-16 360*640 1.11G wider_face FDDB 0.8909 0.8909
22 Face Recognition face_landmark cf_landmark_celeba_96_72_0.14G caffe lenet 96*72 0.14G celebA processed helen 0.03704(MAE) 0.03692(MAE)
23 Re-identification reid cf_reid_marketcuhk_160_80_0.95G caffe resnet18 160*80 0.95G Market1501+CUHK03 Market1501 78.00% 77.60%
24 Detection+Segmentation multi-task cf_multitask_bdd_288_512_14.8G caffe ssd 288*512 14.8G BDD100K+Cityscapes BDD100K+Cityscapes 41.0%(Det) 50.0%(Seg) 40.0%(Det) 47.8%(Seg)
25 Object Detection yolov3_bdd cf_yolov3_bdd_288_512_53.7G darknet darknet-53 288*512 53.7G bdd100k bdd100k 50.60% 49.14%
26 Object Detection yolov3_adas_prune_0.9 dk_yolov3_cityscapes_256_512_0.9_5.46G darknet darknet-53 256*512 5.46G cityscape train cityscape val 55.20% 53.00%
27 Object Detection yolov3_voc dk_yolov3_voc_416_416_65.42G darknet darknet-53 416*416 65.42G voc07+12_trainval voc07_test 82.4%(MaxIntegral) 81.5%(MaxIntegral)
28 Object Detection yolov2_voc dk_yolov2_voc_448_448_34G darknet darknet-19 448*448 34G voc07+12_trainval voc07_test 78.45%(MaxIntegral) 77.39%(MaxIntegral)
29 Object Detection yolov2_voc_pruned_0.66 dk_yolov2_voc_448_448_0.66_11.56G darknet darknet-19 448*448 11.56G voc07+12_trainval voc07_test 77%(MaxIntegral) 76%(MaxIntegral)
30 Object Detection yolov2_voc_pruned_0.71 dk_yolov2_voc_448_448_0.71_9.86G darknet darknet-19 448*448 9.86G voc07+12_trainval voc07_test 76.7%(MaxIntegral) 75.3%(MaxIntegral)
31 Object Detection yolov2_voc_pruned_0.77 dk_yolov2_voc_448_448_0.77_7.82G darknet darknet-19 448*448 7.82G voc07+12_trainval voc07_test 75.76%(MaxIntegral) 74.6%(MaxIntegral)
32 Image Classifiction Inception_resnet_v2 tf_inceptionresnetv2_imagenet_299_299_26.35G tensorflow inception 299*299 26.35G ImageNet Train ImageNet Validataion 80.37% 79.91%
33 Image Classifiction Inception_v1 tf_inceptionv1_imagenet_224_224_3G tensorflow inception 224*224 3G ImageNet Train ImageNet Validataion 69.76% 67.94%
34 Image Classifiction Inception_v3 tf_inceptionv3_imagenet_299_299_11.45G tensorflow inception 299*299 11.45G ImageNet Train ImageNet Validataion 69.76% 67.94%
35 Image Classifiction Inception_v4 tf_inceptionv4_imagenet_299_299_24.55G tensorflow inception 299*299 24.55G ImageNet Train ImageNet Validataion 69.76% 67.94%
36 Image Classifiction Mobilenet_v1 tf_mobilenetv1_0.25_imagenet_128_128_27.15M tensorflow mobilenet 299*299 24.55G ImageNet Train ImageNet Validataion 41.44% 34.64%
37 Image Classifiction Mobilenet_v1 tf_mobilenetv1_0.5_imagenet_160_160_150.07M tensorflow mobilenet 299*299 24.55G ImageNet Train ImageNet Validataion 59.03% 51.95%
38 Image Classifiction Mobilenet_v1 tf_mobilenetv1_1.0_imagenet_224_224_1.14G tensorflow mobilenet 299*299 24.55G ImageNet Train ImageNet Validataion 71.02% 66.10%
39 Image Classifiction Mobilenet_v2 tf_mobilenetv2_1.0_imagenet_224_224_0.59G tensorflow mobilenet 299*299 24.55G ImageNet Train ImageNet Validataion 70.13% 67.67%
40 Image Classifiction Mobilenet_v2 tf_mobilenetv2_1.4_imagenet_224_224_1.16G tensorflow mobilenet 299*299 24.55G ImageNet Train ImageNet Validataion 74.11% 71.94%
41 Image Classifiction resnet_v1_50 tf_resnetv1_50_imagenet_224_224_6.97G tensorflow resnetv1 224*224 6.97G ImageNet Train ImageNet Validataion 75.20% 74.23%
42 Image Classifiction resnet_v1_101 tf_resnetv1_101_imagenet_224_224_14.4G tensorflow resnetv1 224*224 14.4G ImageNet Train ImageNet Validataion 76.40% 74.17%
43 Image Classifiction resnet_v1_152 tf_resnetv1_152_imagenet_224_224_21.83G tensorflow resnetv1 224*224 21.83G ImageNet Train ImageNet Validataion 76.81% 74.69%
44 Image Classifiction vgg_16 tf_vgg16_imagenet_224_224_30.96G tensorflow vgg 224*224 30.96G ImageNet Train ImageNet Validataion 70.89% 70.69%
45 Image Classifiction vgg_19 tf_vgg19_imagenet_224_224_39.28G tensorflow vgg 224*224 39.28G ImageNet Train ImageNet Validataion 71.00% 70.26%
46 Object Detection ssd_mobilenet_v1 tf_ssdmobilenetv1_coco_300_300_2.47G tensorflow mobilenet 300*300 2.47G coco2017 coco2014 minival 20.80% 19.60%
47 Object Detection ssd_mobilenet_v2 tf_ssdmobilenetv2_coco_300_300_3.75G tensorflow mobilenet 300*300 3.75G coco2017 coco2014 minival 21.50% 20.30%
48 Object Detection ssd_resnet50_v1_fpn tf_ssdresnet50v1_fpn_coco_640_640_178.4G tensorflow resnet50 300*300 178.4G coco2017 coco2014 minival 30.10% 29.00%
49 Object Detection yolov3_voc tf_yolov3_voc_416_416_65.63G tensorflow darknet-53 416*416 65.63G voc07+12_trainval voc07_test 78.46% 77.38%
50 Object Detection mlperf_ssd_resnet34 tf_mlperf_resnet34_coco_1200_1200_433G tensorflow resnet34 1200*1200 433G coco2017 coco2017 22.50% 20.70%

Naming Rules

Model name: F_M_D_H_W_(P)_C

  • F specifies training framework: cf is Caffe, tf is Tensorflow, dk is Darknet, pt is PyTorch
  • M specifies the model
  • D specifies the dataset
  • H specifies the height of input data
  • W specifies the width of input data
  • P specifies the pruning ratio, it means how much computation is reduced. It is optional depending on whether the model is pruned.
  • C specifies the computation of the model: how many Gops per image

For example, cf_refinedet_coco_480_360_0.8_25G_1.0 is a RefineDet model trained with Caffe using COCO dataset, input data size is 480*360, 80% pruned, and the computation per image is 25Gops.

Caffe_Xilinx

This is a custom distribution of Caffe. Please use Caffe_Xilinx to test/finetune the caffe models listed in this page.

Note: To download Caffe_Xlinx, visit Caffe_Xilinx.zip

Model Download

The following table lists various models, download link and MD5 checksum for the zip file of each model.

Note: To download all the models, visit all_models_1.0.zip.

Click here to view details

If you are a:

  • Linux user, use the get_model.sh script to download all the models.
  • Windows user, use the download link listed in the following table to download a model.
No. Model Size Download link Checksum
1 cf_resnet50_imagenet_224_224_7.7G 226.62 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_resnet50_imagenet_224_224_1.0.zip 26a8881c800f6e27888a167947c33559
2 cf_inceptionv1_imagenet_224_224_3.16G 86.47 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_inceptionv1_imagenet_224_224_1.0.zip b3e6f9d61fe25ae4425c8efa24138625
3 cf_inceptionv2_imagenet_224_224_4G 143.38 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_inceptionv2_imagenet_224_224_1.0.zip c8db5d52d6b5fd061c17b5ef116c3f54
4 cf_inceptionv3_imagenet_299_299_11.4G 212.43 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_inceptionv3_imagenet_299_299_1.0.zip ebe9184731d13ce35c567c5f4a200f32
5 cf_inceptionv4_imagenet_299_299_24.5G 380.38 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_inceptionv4_imagenet_299_299_1.0.zip cca381dfe5c84e43195aadabe2899622
6 cf_mobilenetv2_imagenet_224_224_0.59G 23.27 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_mobilenetv2_imagenet_224_224_1.0.zip fc7de15fbcff8d318327716a7f04b7bd
7 cf_squeeze_imagenet_227_227_0.76G 11.27 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_squeeze_imagenet_227_227_1.0.zip efeed69bb60e4807d08a9ed4dee42731
8 cf_resnet18_imagenet_224_224_3.65G 175.28MB https://www.xilinx.com/bin/public/openDownload?filename=cf_resnet18_imagenet_224_224_1.0.zip cc6e2a7d48ddc9c1a68b5d2839fa2b84
9 cf_ssdpedestrian_coco_360_640_0.97_5.9G 7.78 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_ssdpedestrian_coco_360_640_0.97_1.0.zip 46b992db8718d98dbf212d203b0f1ec6
10 cf_refinedet_coco_360_480_0.8_25G 37.92 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_refinedet_coco_480_360_0.8_1.0.zip 1bf37b830552b1cc7fbf671414889074
11 cf_refinedet_coco_360_480_0.92_10.10G 10.66 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_refinedet_coco_480_360_0.92_1.0.zip dde4c33563eafefbe499bcb4b4cd6d1a
12 cf_refinedet_coco_360_480_0.96_5.08G 5.53 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_refinedet_coco_480_360_0.96_1.0.zip 0db83cf6ce87325fc34813f1c14ac6df
13 cf_ssdadas_bdd_360_480_0.95_6.3G 11.34 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_ssdadas_bdd_360_480_0.95_1.0.zip 5becc3e0853612277350d295687fd94e
14 cf_ssdtraffic_360_480_0.9_11.6G 20.13 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_ssdtraffic_360_480_0.9_1.0.zip a9b1b10f2f493a34074b70f70ee2dd84
15 cf_VPGnet_caltechlane_480_640_0.99_2.5G 10.39 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_VPGnet_caltechlane_480_640_0.99_1.0.zip b4e1091016917b2d0ccaf3e51ecfab3f
16 cf_ssdmobilenetv2_bdd_360_480_6.57G 100.77 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_ssdmobilenetv2_bdd_360_480_1.0.zip 171746d4c1a2d97408ff8eb9c08a7b6a
17 cf_fpn_cityscapes_256_512_8.9G 58.17MB https://www.xilinx.com/bin/public/openDownload?filename=cf_fpn_cityscapes_256_512_1.0.zip dbae0fba17aaf3c6242d511032efb0fd
18 cf_SPnet_aichallenger_224_128_0.54G 12.06 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_SPnet_aichallenger_224_128_1.0.zip a4ef58d3eaec7ff284af2c22f0178d2b
19 cf_openpose_aichallenger_368_368_0.3_189.7G 544.23 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_openpose_aichallenger_368_368_0.3_1.0.zip b62adb84d7df0aa976f5485bbec6a375
20 cf_densebox_wider_320_320_0.49G 6.26 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_densebox_wider_320_320_1.0.zip 15f2e1c780dc8ba72d01491b773c10be
21 cf_densebox_wider_360_640_1.11G 6.26 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_densebox_wider_360_640_1.0.zip fd9f136fe664cc4f56b3e0133efcfc49
22 cf_landmark_celeba_96_72_0.14G 50.47 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_landmark_celeba_96_72_1.0.zip 200993da21ada189110a34ba2f4b65ca
23 cf_reid_marketcuhk_160_80_0.95G 98.36 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_reid_marketcuhk_160_80_1.0.zip 092c2e42674af381b8a19564077b3c85
24 cf_multitask_bdd_288_512_14.8G 122.37 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_multitask_bdd_288_512_1.0.zip d7b1f54bf6a5ecbc91651b50c63bd1cb
25 cf_yolov3_bdd_288_512_53.7G 948 MB https://www.xilinx.com/bin/public/openDownload?filename=cf_yolov3_bdd_288_512_1.0.zip 83661dba91ac4acf5ddb6db6ee7413c5
26 dk_yolov3_cityscapes_256_512_0.9_5.46G 38.08 MB https://www.xilinx.com/bin/public/openDownload?filename=dk_yolov3_cityscapes_256_512_0.9_1.0.zip be571f096cf2c52e56293f5a68837a50
27 dk_yolov3_voc_416_416_65.42G 940.24 MB https://www.xilinx.com/bin/public/openDownload?filename=dk_yolov3_voc_416_416_1.0.zip fc7f103d657a39b9efbe2d675c3de70e
28 dk_yolov2_voc_448_448_34G 476.55 MB https://www.xilinx.com/bin/public/openDownload?filename=dk_yolov2_voc_448_448_1.0.zip a02a009aed9f36185c5901604ad49c76
29 dk_yolov2_voc_448_448_0.66_11.56G 223.44 MB https://www.xilinx.com/bin/public/openDownload?filename=dk_yolov2_voc_448_448_0.66_1.0.zip 28f7ea8f29c73cc6507c79d86968c2cb
30 dk_yolov2_voc_448_448_0.71_9.86G 202.46 MB https://www.xilinx.com/bin/public/openDownload?filename=dk_yolov2_voc_448_448_0.71_1.0.zip 626971b06f893b24f4a4750fe150101f
31 dk_yolov2_voc_448_448_0.77_7.82G 146.72 MB https://www.xilinx.com/bin/public/openDownload?filename=dk_yolov2_voc_448_448_0.77_1.0.zip 4cb61f9312dc91f7150e599a133059ba
32 tf_inceptionresnetv2_imagenet_299_299_26.35G 657.27 MB https://www.xilinx.com/bin/public/openDownload?filename=tf_inception_resnet_v2_imagenet_299_299_1.0.zip bf515feaf817b156420c7043aa7ee744
33 tf_inceptionv1_imagenet_224_224_3G 76.95 MB https://www.xilinx.com/bin/public/openDownload?filename=tf_inceptionv1_imagenet_224_224_1.0.zip 7df195f8045c5d6d44e56c03c675f8fe
34 tf_inceptionv3_imagenet_299_299_11.45G 287.42 MB https://www.xilinx.com/bin/public/openDownload?filename=tf_inceptionv3_imagenet_299_299_1.0.zip 88c5d39491e143e7b10de7718e1e94f1
35 tf_inceptionv4_imagenet_299_299_24.55G 505.64 MB https://www.xilinx.com/bin/public/openDownload?filename=tf_inceptionv4_imagenet_299_299_1.0.zip 03f69653b71145fa893c66c0fffcf257
36 tf_mobilenetv1_0.25_imagenet_128_128_27.15M 10.74 MB https://www.xilinx.com/bin/public/openDownload?filename=tf_mobilenetv1_0.25_imagenet_128_128_1.0.zip f698cddfe7334c13dd1268d3e1d59b11
37 tf_mobilenetv1_0.5_imagenet_160_160_150.07M 29.72 MB https://www.xilinx.com/bin/public/openDownload?filename=tf_mobilenetv1_0.5_imagenet_160_160_1.0.zip d9cafa9cf361e99e7aabf836982dee3f
38 tf_mobilenetv1_1.0_imagenet_224_224_1.14G 93.97 MB https://www.xilinx.com/bin/public/openDownload?filename=tf_mobilenetv1_1.0_imagenet_224_224_1.0.zip 7747fad0fd70d7fd5e6688abeeb52817
39 tf_mobilenetv2_1.0_imagenet_224_224_0.59G 78.33 MB https://www.xilinx.com/bin/public/openDownload?filename=tf_mobilenetv2_1.0_imagenet_224_224_1.0.zip 4fb81d606c2b78fb34e1cc06acc58c00
40 tf_mobilenetv2_1.4_imagenet_224_224_1.16G 135.64 MB https://www.xilinx.com/bin/public/openDownload?filename=tf_mobilenetv2_1.4_imagenet_224_224_1.0.zip f31c3cf368c0c04762d3a251b173ed43
41 tf_resnetv1_50_imagenet_224_224_6.97G 295.19 MB https://www.xilinx.com/bin/public/openDownload?filename=tf_resnetv1_50_imagenet_224_224_1.0.zip fb6c2b68f6f5dd356100d6f630d21c35
42 tf_resnetv1_101_imagenet_224_224_14.4G 514.12 MB https://www.xilinx.com/bin/public/openDownload?filename=tf_resnetv1_101_imagenet_224_224_1.0.zip 4d72ed81fbf5ac01de244083f6fcadee
43 tf_resnetv1_152_imagenet_224_224_21.83G 695.86 MB https://www.xilinx.com/bin/public/openDownload?filename=tf_resnetv1_152_imagenet_224_224_1.0.zip 5c9321bc0e469f4d4f21cd075e2fadae
44 tf_vgg16_imagenet_224_224_30.96G 1.57 GB https://www.xilinx.com/bin/public/openDownload?filename=tf_vgg16_imagenet_224_224_1.0.zip b5c5ed1e8bc6d50821e6802c6702da7f
45 tf_vgg19_imagenet_224_224_39.28G 1.63 GB https://www.xilinx.com/bin/public/openDownload?filename=tf_vgg19_imagenet_224_224_1.0.zip 5ca310d0410eb266f4fccf49dd378e23
46 tf_ssdmobilenetv1_coco_300_300_2.47G 135.78 MB https://www.xilinx.com/bin/public/openDownload?filename=tf_ssdmobilenetv1_coco_300_300_1.0.zip 6c013ef52898b68699c1b3bc5ddc5909
47 tf_ssdmobilenetv2_coco_300_300_3.75G 318.32 MB https://www.xilinx.com/bin/public/openDownload?filename=tf_ssdmobilenetv2_coco_300_300_1.0.zip 53ace1f075ad0b01b53cca5f6884e0df
48 tf_ssdresnet50v1_fpn_coco_640_640_178.4G 732.25 MB https://www.xilinx.com/bin/public/openDownload?filename=tf_ssdresnet50v1_fpn_coco_640_640_1.0.zip fb5f1fbd4dbee9d4e19d4a382ddd893f
49 tf_yolov3_voc_416_416_65.63G 500.31 MB https://www.xilinx.com/bin/public/openDownload?filename=tf_yolov3_voc_416_416_1.0.zip 9f134db4acff5f028d822f15ee5da189
50 tf_mlperf_resnet34_coco_1200_1200_433G 508.07 MB https://www.xilinx.com/bin/public/openDownload?filename=tf_mlperf_resnet34_coco_1200_1200_1.0.zip b6a43644b9ff8d59c7c76201a62d8970
- all models 13.87 GB https://www.xilinx.com/bin/public/openDownload?filename=all_models_1.0.zip ed5509bcd0ce5e3aa2b220145acc17f5

Model Directory Structure

Download and extract the model archive to your working area on the local hard disk. For details on the various models, their download link and MD5 checksum for the zip file of each model, see Model Download.

Caffe Model Directory Structure

For a Caffe model, you should see the following directory structure:

├── code                            # Contains code and instructions.
│   ├── test                        # Contains test code which can run demo and evaluate model performance.
│   ├── train                       # Contains train code and data preprocess code.
│   └── readme.md                   # Contains environment requirements and train eval instructions.
│                                     
│                                   
├── readme.md                       # Contains the environment requirement and data preprocess information.
│                                     Refer this file to know more about creating `float.prototxt` by adding
│                                     datalayer to `test.prototxt` in the `float` directory.
├── compiler                          
│   ├── deploy.caffemodel           # Input to the compiler. The same with deploy.caffemodel in the `quantized` directory.
│   └── deploy.prototxt             # Input to the compiler. The modified prototxt based on deploy.prototxt
│                                     in the `quantized` directory, which removes unnecessary or unsupported layers
│                                     for compilation.
├── quantized                             
│   ├── deploy.caffemodel           # Quantized weights, the output of vai_q_caffe without modification.
│   ├── deploy.prototxt             # Quantized prototxt, the output of vai_q_caffe without modification.
│   ├── quantized_test.prototxt           # Used to run evaluation with quantized_train_test.caffemodel on GPU
│   │                                 using python test code released in near future. Some models
│   │                                 don't have this file if they are converted from Darknet (Yolov2, Yolov3),
│   │                                 Pytorch (ReID) or there is no Caffe Test (Densebox).
│   ├── quantized_train_test.caffemodel   # Quantized weights can be used for quantizeded-point training and evaluation.    
│   └── quantized_train_test.prototxt     # Used for quantized-point training and testing with quantized_train_test.caffemodel
│                                     on GPU when datalayer modified to user's data path.
└── float                           
    ├── float.caffemodel            # Trained float-point weights.
    ├── float.prototxt              # Modified test.prototxt as the input to vai_q_caffe along
    │                                 with float.caffemodel. vai_q_caffe is Xilinx quantization tool
    │                                 which quantizes float-point to quantized-point model with minimal
    │                                 accuracy loss.
    ├── test.prototxt               # Used to run evaluation with python test codes released in near future.    
    └── trainval.prorotxt           # Used for training and testing with caffe train/test command
                                      when datalayer modified to user's data path. Some models don't
                                      have this file if they are converted from Darknet (Yolov2, Yolov3),
                                      Pytorch (ReID) or there is no Caffe Test (Densebox).          

Tensorflow Model Directory Structure

For a Tensorflow model, you should see the following directory structure:

├── test_code                       # Contains code and instructions.
│   ├── float                       # Test code and instruction for floating model for evaluation.
│   └── quantized                         # Test code and instruction for quantized model for evaluation.
│
├── readme.md                       # Contains the environment requirement, the input and output nodes as well as
│                                     the data preprocess and postprocess information.
├── quantized                          
│   ├── deploy.model.pb             # Quantized model for the compiler (extended Tensorflow format).
│   └── quantize_eval_model.pb      # Quantized model for evaluation.
│
└── float                             
    └── frozen.pb                   # Float-point frozen model, the input to the `vai_q_tensorflow`.

Note: For more information on vai_q_caffe and vai_q_tensorflow, see the Vitis AI User Guide.

Model Performance

All the models in the Model Zoo have been deployed on Xilinx hardware with Vitis AI and Vitis AI Library. The performance number including end-to-end throughput and latency for each model on various boards with different DPU configurations are listed in the following sections.

For more information about DPU, see DPU IP Product Guide.

Note: The model performance number listed in the following sections is generated with Vitis AI v1.0 and Vitis AI Lirary v1.0. For each board, a different DPU configuration is used. Vitis AI and Vitis AI Library can be downloaded for free from Vitis AI Github and Vitis AI Library Github. We will continue to improve the performance with Vitis AI. The performance number reported here is subject to change in the near future.

Performance on ZCU102 (0432055-04)

This version of ZCU102 is out of stock. The performance number shown below was measured with the previous AI SDK v2.0.4.

Click here to view details

The following table lists the performance number including end-to-end throughput and latency for each model on the ZCU102 (0432055-04) board with a 3 * B4096 @ 287MHz V1.4.0 DPU configuration:

No. Model Name E2E latency (ms) Thread num =1 E2E throughput -fps(Single Thread) E2E throughput -fps(Multi Thread)
1 resnet50 cf_resnet50_imagenet_224_224_7.7G 12.85 77.8 179.3
2 Inception_v1 cf_inceptionv1_imagenet_224_224_3.16G 5.47 182.683 485.533
3 Inception_v2 cf_inceptionv2_imagenet_224_224_4G 6.76 147.933 373.267
4 Inception_v3 cf_inceptionv3_imagenet_299_299_11.4G 17 58.8333 155.4
5 mobileNet_v2 cf_mobilenetv2_imagenet_224_224_0.59G 4.09 244.617 638.067
6 tf_resnet50 tf_resnet50_imagenet_224_224_6.97G 11.94 83.7833 191.417
7 tf_inception_v1 tf_inceptionv1_imagenet_224_224_3G 6.72 148.867 358.283
8 tf_mobilenet_v2 tf_mobilenetv2_imagenet_224_224_1.17G 5.46 183.117 458.65
9 ssd_adas_pruned_0.95 cf_ssdadas_bdd_360_480_0.95_6.3G 11.33 88.2667 320.5
10 ssd_pedestrain_pruned_0.97 cf_ssdpedestrian_coco_360_640_0.97_5.9G 12.96 77.1833 314.717
11 ssd_traffic_pruned_0.9 cf_ssdtraffic_360_480_0.9_11.6G 17.49 57.1833 218.183
12 ssd_mobilnet_v2 cf_ssdmobilenetv2_bdd_360_480_6.57G 24.21 41.3 141.233
13 tf_ssd_voc tf_ssd_voc_300_300_64.81G 69.28 14.4333 46.7833
14 densebox_320_320 cf_densebox_wider_320_320_0.49G 2.43 412.183 1416.63
15 densebox_360_640 cf_densebox_wider_360_640_1.11G 5.01 199.717 719.75
16 yolov3_adas_prune_0.9 dk_yolov3_cityscapes_256_512_0.9_5.46G 11.09 90.1667 259.65
17 yolov3_voc dk_yolov3_voc_416_416_65.42G 70.51 14.1833 44.4
18 tf_yolov3_voc tf_yolov3_voc_416_416_65.63G 70.75 14.1333 44.0167
19 refinedet_pruned_0.8 cf_refinedet_coco_360_480_0.8_25G 29.91 33.4333 109.067
20 refinedet_pruned_0.92 cf_refinedet_coco_360_480_0.92_10.10G 15.39 64.9667 216.317
21 refinedet_pruned_0.96 cf_refinedet_coco_360_480_0.96_5.08G 11.04 90.5833 312
22 FPN cf_fpn_cityscapes_256_512_8.9G 16.58 60.3 203.867
23 VPGnet_pruned_0.99 cf_VPGnet_caltechlane_480_640_0.99_2.5G 9.44 105.9 424.667
24 SP-net cf_SPnet_aichallenger_224_128_0.54G 1.73 579.067 1620.67
25 Openpose_pruned_0.3 cf_openpose_aichallenger_368_368_0.3_189.7G 279.07 3.58333 38.5
26 yolov2_voc dk_yolov2_voc_448_448_34G 39.76 25.15 86.35
27 yolov2_voc_pruned_0.66 dk_yolov2_voc_448_448_0.66_11.56G 18.42 54.2833 211.217
28 yolov2_voc_pruned_0.71 dk_yolov2_voc_448_448_0.71_9.86G 16.42 60.9167 242.433
29 yolov2_voc_pruned_0.77 dk_yolov2_voc_448_448_0.77_7.82G 14.46 69.1667 286.733
30 Inception-v4 cf_inceptionv4_imagenet_299_299_24.5G 34.25 29.2 84.25
31 SqueezeNet cf_squeeze_imagenet_227_227_0.76G 3.6 277.65 1080.77
32 face_landmark cf_landmark_celeba_96_72_0.14G 1.13 885.033 1623.3
33 reid cf_reid_marketcuhk_160_80_0.95G 2.67 375 773.533
34 yolov3_bdd cf_yolov3_bdd_288_512_53.7G 73.89 13.5333 42.8833
35 tf_mobilenet_v1 tf_mobilenetv1_imagenet_224_224_1.14G 3.2 312.067 875.967
36 resnet18 cf_resnet18_imagenet_224_224_3.65G 5.1 195.95 524.433
37 resnet18_wide tf_resnet18_imagenet_224_224_28G 33.28 30.05 83.4167

Performance on ZCU102 (0432055-05)

Measured with Vitis AI 1.0, Vitis AI Library 1.0 and Vitis DPU 1.0 in Nov 2019

Click here to view details

The following table lists the performance number including end-to-end throughput and latency for each model on the ZCU102 (0432055-05) board with a 3 * B4096 @ 287MHz V1.4.0 DPU configuration:

No. Model Name E2E latency (ms) Thread num =1 E2E throughput -fps(Single Thread) E2E throughput -fps(Multi Thread)
1 resnet50 cf_resnet50_imagenet_224_224_7.7G 14.06 71.1 150.2
2 resnet18 cf_resnet18_imagenet_224_224_3.65G 5.84 171.1 437.6
3 Inception_v1 cf_inceptionv1_imagenet_224_224_3.16G 6.17 162.2 422.4
4 Inception_v2 cf_inceptionv2_imagenet_224_224_4G 7.52 133 321.4
5 Inception_v3 cf_inceptionv3_imagenet_299_299_11.4G 18.25 54.8 131.2
6 Inception_v4 cf_inceptionv4_imagenet_299_299_24.5G 36.10 27.7 67.4
7 Mobilenet_v2 cf_mobilenetv2_imagenet_224_224_0.59G 4.76 210.1 557.4
8 SqueezeNet cf_squeeze_imagenet_227_227_0.76G 3.78 264.5 1121.6
9 ssd_pedestrain_pruned_0_97 cf_ssdpedestrian_coco_360_640_0.97_5.9G 13.16 76 306.1
10 refinedet_pruned_0_8 cf_refinedet_coco_360_480_0.8_25G 31.65 31.6 106
11 refinedet_pruned_0_92 cf_refinedet_coco_360_480_0.92_10.10G 16.78 59.6 206.2
12 refinedet_pruned_0_96 cf_refinedet_coco_360_480_0.96_5.08G 12.15 82.3 292.6
13 ssd_adas_pruned_0_95 cf_ssdadas_bdd_360_480_0.95_6.3G 12.11 82.6 299
14 ssd_traffic_pruned_0_9 cf_ssdtraffic_360_480_0.9_11.6G 18.05 55.4 214.2
15 VPGnet_pruned_0_99 cf_VPGnet_caltechlane_480_640_0.99_2.5G 9.82 101.8 401.1
16 ssd_mobilenet_v2 cf_ssdmobilenetv2_bdd_360_480_6.57G 25.84 38.7 117.8
17 FPN cf_fpn_cityscapes_256_512_8.9G 17.06 58.6 186.7
18 SP_net cf_SPnet_aichallenger_224_128_0.54G 1.95 511.6 1386.4
19 Openpose_pruned_0_3 cf_openpose_aichallenger_368_368_0.3_189.7G 285.71 3.5 15.6
20 densebox_320_320 cf_densebox_wider_320_320_0.49G 2.61 383 1363.7
21 densebox_640_360 cf_densebox_wider_360_640_1.11G 5.24 190.7 637.8
22 face_landmark cf_landmark_celeba_96_72_0.14G 1.28 779.6 1348
23 reid cf_reid_marketcuhk_160_80_0.95G 2.91 343.3 659.4
24 multi_task cf_multitask_bdd_288_512_14.8G 28.17 35.5 133.2
25 yolov3_bdd dk_yolov3_bdd_288_512_53.7G 77.52 12.9 37.5
26 yolov3_adas_pruned_0_9 dk_yolov3_cityscapes_256_512_0.9_5.46G 12.20 82 227.3
27 yolov3_voc dk_yolov3_voc_416_416_65.42G 74.07 13.5 38.2
28 yolov2_voc dk_yolov2_voc_448_448_34G 40.49 24.7 76.2
29 yolov2_voc_pruned_0_66 dk_yolov2_voc_448_448_0.66_11.56G 18.83 53.1 203.7
30 yolov2_voc_pruned_0_71 dk_yolov2_voc_448_448_0.71_9.86G 16.75 59.7 235.9
31 yolov2_voc_pruned_0_77 dk_yolov2_voc_448_448_0.77_7.82G 14.75 67.8 281.6
32 Inception_resnet_v2 tf_inceptionresnetv2_imagenet_299_299_26.35G 43.67 22.9 49.1
33 Inception_v1 tf_inceptionv1_imagenet_224_224_3G 5.98 167.2 434.8
34 Inception_v3 tf_inceptionv3_imagenet_299_299_11.45G 18.28 54.7 129.7
35 Inception_v4 tf_inceptionv4_imagenet_299_299_24.55G 36.10 27.7 67.5
36 Mobilenet_v1 tf_mobilenetv1_0.25_imagenet_128_128_27.15M 1.20 836.1 2270.7
37 Mobilenet_v1 tf_mobilenetv1_0.5_imagenet_160_160_150.07M 1.76 566.7 1816.9
38 Mobilenet_v1 tf_mobilenetv1_1.0_imagenet_224_224_1.14G 3.90 256.1 763.7
39 Mobilenet_v2 tf_mobilenetv2_1.0_imagenet_224_224_0.59G 4.68 213.6 575.2
40 Mobilenet_v2 tf_mobilenetv2_1.4_imagenet_224_224_1.16G 6.30 158.7 395.4
41 resnet_v1_50 tf_resnetv1_50_imagenet_224_224_6.97G 13.09 76.4 159.4
42 resnet_v1_101 tf_resnetv1_101_imagenet_224_224_14.4G 23.53 42.5 90.7
43 resnet_v1_152 tf_resnetv1_152_imagenet_224_224_21.83G 34.13 29.3 63.3
44 vgg_16 tf_vgg16_imagenet_224_224_30.96G 52.63 19 41.8
45 vgg_19 tf_vgg19_imagenet_224_224_39.28G 60.61 16.5 37.8
46 ssd_mobilenet_v1 tf_ssdmobilenetv1_coco_300_300_2.47G 11.11 90 320.6
47 ssd_mobilenet_v2 tf_ssdmobilenetv2_coco_300_300_3.75G 16.18 61.8 196.6
48 ssd_resnet_50_v1_fpn tf_ssdresnet50v1_fpn_coco_640_640_178.4G 769.23 1.3 5.9
49 yolov3_voc tf_yolov3_voc_416_416_65.63G 74.63 13.4 37.8
50 mlperf_ssd_resnet34 tf_mlperf_resnet34_coco_1200_1200_433G 526.32 1.9 7.7

Performance on ZCU104

Measured with Vitis AI 1.0, Vitis AI Library 1.0 and Vitis DPU 1.0 in Nov 2019

Click here to view details

The following table lists the performance number including end-to-end throughput and latency for each model on the ZCU104 board with a 2 * B4096 @ 305MHz V1.4.0 DPU configuration:

No. Model Name E2E latency (ms) Thread num =1 E2E throughput -fps(Single Thread) E2E throughput -fps(Multi Thread)
1 resnet50 cf_resnet50_imagenet_224_224_7.7G 12.64 79.1 142.7
2 resnet18 cf_resnet18_imagenet_224_224_3.65G 5.18 193 382.1
3 Inception_v1 cf_inceptionv1_imagenet_224_224_3.16G 5.41 184.7 371.4
4 Inception_v2 cf_inceptionv2_imagenet_224_224_4G 6.68 149.7 285
5 Inception_v3 cf_inceptionv3_imagenet_299_299_11.4G 16.81 59.5 113.6
6 Inception_v4 cf_inceptionv4_imagenet_299_299_24.5G 33.44 29.9 57.6
7 Mobilenet_v2 cf_mobilenetv2_imagenet_224_224_0.59G 4.10 244.2 510.5
8 SqueezeNet cf_squeeze_imagenet_227_227_0.76G 3.70 270.6 1060.4
9 ssd_pedestrain_pruned_0_97 cf_ssdpedestrian_coco_360_640_0.97_5.9G 12.80 78.1 192.8
10 refinedet_pruned_0_8 cf_refinedet_coco_360_480_0.8_25G 30.86 32.4 75
11 refinedet_pruned_0_92 cf_refinedet_coco_360_480_0.92_10.10G 16.42 60.9 137.8
12 refinedet_pruned_0_96 cf_refinedet_coco_360_480_0.96_5.08G 12.03 83.1 193.2
13 ssd_adas_pruned_0_95 cf_ssdadas_bdd_360_480_0.95_6.3G 11.83 84.5 197.5
14 ssd_traffic_pruned_0_9 cf_ssdtraffic_360_480_0.9_11.6G 17.48 57.2 133.1
15 VPGnet_pruned_0_99 cf_VPGnet_caltechlane_480_640_0.99_2.5G 9.53 104.9 351.3
16 ssd_mobilenet_v2 cf_ssdmobilenetv2_bdd_360_480_6.57G 39.53 25.3 108.4
17 FPN cf_fpn_cityscapes_256_512_8.9G 16.39 61 162.7
18 SP_net cf_SPnet_aichallenger_224_128_0.54G 1.87 534.9 1147.4
19 Openpose_pruned_0_3 cf_openpose_aichallenger_368_368_0.3_189.7G 270.27 3.7 11.1
20 densebox_320_320 cf_densebox_wider_320_320_0.49G 2.57 389.5 1342.9
21 densebox_640_360 cf_densebox_wider_360_640_1.11G 5.08 196.7 661.5
22 face_landmark cf_landmark_celeba_96_72_0.14G 1.19 837.2 1171.7
23 reid cf_reid_marketcuhk_160_80_0.95G 2.74 365.3 619.2
24 multi_task cf_multitask_bdd_288_512_14.8G 27.78 36 107.3
25 yolov3_bdd dk_yolov3_bdd_288_512_53.7G 74.07 13.5 28.7
26 yolov3_adas_pruned_0_9 dk_yolov3_cityscapes_256_512_0.9_5.46G 12.02 83.2 208.8
27 yolov3_voc dk_yolov3_voc_416_416_65.42G 70.42 14.2 29.6
28 yolov2_voc dk_yolov2_voc_448_448_34G 38.31 26.1 58.5
29 yolov2_voc_pruned_0_66 dk_yolov2_voc_448_448_0.66_11.56G 18.05 55.4 144.2
30 yolov2_voc_pruned_0_71 dk_yolov2_voc_448_448_0.71_9.86G 16.05 62.3 169.3
31 yolov2_voc_pruned_0_77 dk_yolov2_voc_448_448_0.77_7.82G 14.20 70.4 208.7
32 Inception_resnet_v2 tf_inceptionresnetv2_imagenet_299_299_26.35G 39.84 25.1 45.4
33 Inception_v1 tf_inceptionv1_imagenet_224_224_3G 5.19 192.5 383.8
34 Inception_v3 tf_inceptionv3_imagenet_299_299_11.45G 16.86 59.3 112.7
35 Inception_v4 tf_inceptionv4_imagenet_299_299_24.55G 33.44 29.9 57.7
36 Mobilenet_v1 tf_mobilenetv1_0.25_imagenet_128_128_27.15M 0.81 1233 3863.9
37 Mobilenet_v1 tf_mobilenetv1_0.5_imagenet_160_160_150.07M 1.35 739.9 1929.3
38 Mobilenet_v1 tf_mobilenetv1_1.0_imagenet_224_224_1.14G 3.29 304.4 672.3
39 Mobilenet_v2 tf_mobilenetv2_1.0_imagenet_224_224_0.59G 4.08 245.3 519.3
40 Mobilenet_v2 tf_mobilenetv2_1.4_imagenet_224_224_1.16G 5.53 180.8 369.1
41 resnet_v1_50 tf_resnetv1_50_imagenet_224_224_6.97G 11.78 84.9 152.2
42 resnet_v1_101 tf_resnetv1_101_imagenet_224_224_14.4G 21.37 46.8 85.6
43 resnet_v1_152 tf_resnetv1_152_imagenet_224_224_21.83G 31.06 32.2 59.2
44 vgg_16 tf_vgg16_imagenet_224_224_30.96G 48.08 20.8 37.1
45 vgg_19 tf_vgg19_imagenet_224_224_39.28G 55.25 18.1 33
46 ssd_mobilenet_v1 tf_ssdmobilenetv1_coco_300_300_2.47G 10.78 92.8 315.8
47 ssd_mobilenet_v2 tf_ssdmobilenetv2_coco_300_300_3.75G 15.31 65.3 177.6
48 ssd_resnet50_v1_fpn tf_ssdresnet50v1_fpn_coco_640_640_178.4G 714.29 1.4 6.1
49 yolov3_voc tf_yolov3_voc_416_416_65.63G 70.92 14.1 29.3
50 mlperf_ssd_resnet34 tf_mlperf_resnet34_coco_1200_1200_433G 526.32 1.9 5.6

Performance on Ultra96

The performance number shown below was measured with the previous AI SDK v2.0.4 on Ultra96 v1. The Vitis platform of Ultra96 v2 has not been released yet. So the performance numbers are therefore not reported for this Model Zoo release.

Click here to view details

The following table lists the performance number including end-to-end throughput and latency for each model on the Ultra96 board with a 1 * B1600 @ 287MHz V1.4.0 DPU configuration:

Note: The original power supply of Ultra96 is not designed for high performance AI workload. The board may occasionally hang to run few models, When multi-thread is used. For such situations, NA is specified in the following table.

No. Model Name E2E latency (ms) Thread num =1 E2E throughput -fps(Single Thread) E2E throughput -fps(Multi Thread)
1 resnet50 cf_resnet50_imagenet_224_224_7.7G 30.8 32.4667 33.4667
2 Inception_v1 cf_inceptionv1_imagenet_224_224_3.16G 13.98 71.55 75.0667
3 Inception_v2 cf_inceptionv2_imagenet_224_224_4G 17.16 58.2667 61.2833
4 Inception_v3 cf_inceptionv3_imagenet_299_299_11.4G 44.05 22.7 23.4333
5 mobileNet_v2 cf_mobilenetv2_imagenet_224_224_0.59G 7.34 136.183 NA
6 tf_resnet50 tf_resnet50_imagenet_224_224_6.97G 28.02 35.6833 36.6
7 tf_inception_v1 tf_inceptionv1_imagenet_224_224_3G 16.96 58.9667 61.2833
8 tf_mobilenet_v2 tf_mobilenetv2_imagenet_224_224_1.17G 10.17 98.3 104.25
9 ssd_adas_pruned_0.95 cf_ssdadas_bdd_360_480_0.95_6.3G 24.3 41.15 46.2
10 ssd_pedestrain_pruned_0.97 cf_ssdpedestrian_coco_360_640_0.97_5.9G 23.29 42.9333 50.8
11 ssd_traffic_pruned_0.9 cf_ssdtraffic_360_480_0.9_11.6G 35.5 28.1667 31.8
12 ssd_mobilnet_v2 cf_ssdmobilenetv2_bdd_360_480_6.57G 60.79 16.45 27.8167
13 tf_ssd_voc tf_ssd_voc_300_300_64.81G 186.92 5.35 5.81667
14 densebox_320_320 cf_densebox_wider_320_320_0.49G 4.17 239.883 334.167
15 densebox_360_640 cf_densebox_wider_360_640_1.11G 8.55 117 167.2
16 yolov3_adas_prune_0.9 dk_yolov3_cityscapes_256_512_0.9_5.46G 22.79 43.8833 49.6833
17 yolov3_voc dk_yolov3_voc_416_416_65.42G 185.19 5.4 5.53
18 tf_yolov3_voc tf_yolov3_voc_416_416_65.63G 199.34 5.01667 5.1
19 refinedet_pruned_0.8 cf_refinedet_coco_360_480_0.8_25G 66.37 15.0667 NA
20 refinedet_pruned_0.92 cf_refinedet_coco_360_480_0.92_10.10G 32.17 31.0883 33.6667
21 refinedet_pruned_0.96 cf_refinedet_coco_360_480_0.96_5.08G 20.29 49.2833 55.25
22 FPN cf_fpn_cityscapes_256_512_8.9G 36.34 27.5167 NA
23 VPGnet_pruned_0.99 cf_VPGnet_caltechlane_480_640_0.99_2.5G 13.9 71.9333 NA
24 SP-net cf_SPnet_aichallenger_224_128_0.54G 3.82 261.55 277.4
25 Openpose_pruned_0.3 cf_openpose_aichallenger_368_368_0.3_189.7G 560.75 1.78333 NA
26 yolov2_voc dk_yolov2_voc_448_448_34G 118.11 8.46667 8.9
27 yolov2_voc_pruned_0.66 dk_yolov2_voc_448_448_0.66_11.56G 37.5 26.6667 30.65
28 yolov2_voc_pruned_0.71 dk_yolov2_voc_448_448_0.71_9.86G 30.99 32.2667 38.35
29 yolov2_voc_pruned_0.77 dk_yolov2_voc_448_448_0.77_7.82G 26.29 38.03333 46.8333
30 Inception-v4 cf_inceptionv4_imagenet_299_299_24.5G 88.76 11.2667 11.5333
31 SqueezeNet cf_squeeze_imagenet_227_227_0.76G 5.96 167.867 283.583
32 face_landmark cf_landmark_celeba_96_72_0.14G 2.95 339.183 347.633
33 reid cf_reid_marketcuhk_160_80_0.95G 6.28 159.15 166.633
34 yolov3_bdd cf_yolov3_bdd_288_512_53.7G 193.55 5.16667 5.31667
35 tf_mobilenet_v1 tf_mobilenetv1_imagenet_224_224_1.14G 5.97 167.567 186.55
36 resnet18 cf_resnet18_imagenet_224_224_3.65G 13.47 74.2167 77.8167
37 resnet18_wide tf_resnet18_imagenet_224_224_28G 97.72 10.2333 10.3833

Contributing

We welcome community contributions. When contributing to this repository, first discuss the change you wish to make via:

You can also submit a pull request with details on how to improve the product. Prior to submitting your pull request, ensure that you can build the product and run all the demos with your patch. In case of a larger feature, provide a relevant demo.

License

Xilinx AI Model Zoo is licensed under Apache License Version 2.0. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.


Copyright© 2019 Xilinx

You can’t perform that action at this time.