From f0af483af8468f21544518746c914bd4034ad05f Mon Sep 17 00:00:00 2001 From: dongshuilong Date: Thu, 1 Jul 2021 15:46:56 +0800 Subject: [PATCH] update VehicleReID model --- docs/en/application/vehicle_recognition_en.md | 7 ++----- docs/en/tutorials/quick_start_recognition_en.md | 2 +- docs/zh_CN/application/vehicle_recognition.md | 5 +---- docs/zh_CN/tutorials/quick_start_recognition.md | 2 +- ppcls/configs/Vehicle/ResNet50_ReID.yaml | 5 +---- 5 files changed, 6 insertions(+), 15 deletions(-) diff --git a/docs/en/application/vehicle_recognition_en.md b/docs/en/application/vehicle_recognition_en.md index 1f7bff3d..0c1e611e 100644 --- a/docs/en/application/vehicle_recognition_en.md +++ b/docs/en/application/vehicle_recognition_en.md @@ -58,10 +58,7 @@ This method is used in VERI-Wild dataset. This dataset was captured in a large C | GLAMOR(Resnet50+PGN)[3] | 77.15 | 92.13 | 97.43 | | PVEN(Resnet50)[4] | 79.8 | 94.01 | 98.06 | | SAVER(VAE+Resnet50)[5] | 80.9 | 93.78 | 97.93 | -| PaddleClas baseline1 | 65.6 | 92.37 | 97.23 | -| PaddleClas baseline2 | 80.09 | **93.81** | **98.26** | - - Baseline1 is the released, and baseline2 will be released soon. +| PaddleClas baseline | 80.57 | **93.81** | **98.06** | ### 2.2 Vehicle Fine-grained Classification @@ -79,7 +76,7 @@ The images in the dataset mainly come from the network and monitoring data. The | Fine-Tuning DARTS[7] | 95.9% | | Resnet50 + COOC[8] | 95.6% | | A3M[9] | 95.4% | -| PaddleClas baseline (ResNet50) | **97.36**% | +| PaddleClas baseline (ResNet50) | **97.37**% | ## 3 References diff --git a/docs/en/tutorials/quick_start_recognition_en.md b/docs/en/tutorials/quick_start_recognition_en.md index 5d2a39f9..dcea1b93 100644 --- a/docs/en/tutorials/quick_start_recognition_en.md +++ b/docs/en/tutorials/quick_start_recognition_en.md @@ -41,7 +41,7 @@ The detection model with the recognition inference model for the 4 directions (L | Cartoon Face Recognition Model| Cartoon Face Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/cartoon_rec_ResNet50_iCartoon_v1.0_infer.tar) | [inference_cartoon.yaml](../../../deploy/configs/inference_cartoon.yaml) | [build_cartoon.yaml](../../../deploy/configs/build_cartoon.yaml) | | Vehicle Fine-Grained Classfication Model | Vehicle Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_cls_ResNet50_CompCars_v1.0_infer.tar) | [inference_vehicle.yaml](../../../deploy/configs/inference_vehicle.yaml) | [build_vehicle.yaml](../../../deploy/configs/build_vehicle.yaml) | | Product Recignition Model | Product Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/product_ResNet50_vd_Inshop_v1.0_infer.tar) | [inference_product.yaml](../../../deploy/configs/inference_product.yaml) | [build_product.yaml](../../../deploy/configs/build_product.yaml) | -| Vehicle ReID Model | Vehicle ReID Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_reid_ResNet50_VERI_Wild_v1.0_infer.tar) | - | - | +| Vehicle ReID Model | Vehicle ReID Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_reid_ResNet50_VERIWild_v1.0_infer.tar) | - | - | Demo data in this tutorial can be downloaded here: [download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognition_demo_data_en_v1.0.tar). diff --git a/docs/zh_CN/application/vehicle_recognition.md b/docs/zh_CN/application/vehicle_recognition.md index 2c12a104..665e4470 100644 --- a/docs/zh_CN/application/vehicle_recognition.md +++ b/docs/zh_CN/application/vehicle_recognition.md @@ -57,10 +57,7 @@ ReID，也就是 Re-identification，其定义是利用算法，在图像库中 | GLAMOR(Resnet50+PGN)[3] | 77.15 | 92.13 | 97.43 | | PVEN(Resnet50)[4] | 79.8 | 94.01 | 98.06 | | SAVER(VAE+Resnet50)[5] | 80.9 | 93.78 | 97.93 | -| PaddleClas baseline1 | 65.6 | 92.37 | 97.23 | -| PaddleClas baseline2 | 80.09 | **93.81** | **98.26** | - -注：baseline1 为目前的开源模型，baseline2即将开源 +| PaddleClas baseline | 80.57 | **93.81** | **98.06** | ### 2.2 车辆细分类 diff --git a/docs/zh_CN/tutorials/quick_start_recognition.md b/docs/zh_CN/tutorials/quick_start_recognition.md index d08f9bc5..81518f39 100644 --- a/docs/zh_CN/tutorials/quick_start_recognition.md +++ b/docs/zh_CN/tutorials/quick_start_recognition.md @@ -41,7 +41,7 @@ | 动漫人物识别模型 | 动漫人物场景 | [模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/cartoon_rec_ResNet50_iCartoon_v1.0_infer.tar) | [inference_cartoon.yaml](../../../deploy/configs/inference_cartoon.yaml) | [build_cartoon.yaml](../../../deploy/configs/build_cartoon.yaml) | | 车辆细分类模型 | 车辆场景 | [模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_cls_ResNet50_CompCars_v1.0_infer.tar) | [inference_vehicle.yaml](../../../deploy/configs/inference_vehicle.yaml) | [build_vehicle.yaml](../../../deploy/configs/build_vehicle.yaml) | | 商品识别模型 | 商品场景 | [模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/product_ResNet50_vd_aliproduct_v1.0_infer.tar) | [inference_product.yaml](../../../deploy/configs/inference_product.yaml) | [build_product.yaml](../../../deploy/configs/build_product.yaml) | -| 车辆ReID模型 | 车辆ReID场景 | [模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_reid_ResNet50_VERI_Wild_v1.0_infer.tar) | - | - | +| 车辆ReID模型 | 车辆ReID场景 | [模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_reid_ResNet50_VERIWild_v1.0_infer.tar) | - | - | 本章节demo数据下载地址如下: [数据下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognition_demo_data_v1.0.tar)。 diff --git a/ppcls/configs/Vehicle/ResNet50_ReID.yaml b/ppcls/configs/Vehicle/ResNet50_ReID.yaml index f5644bd0..333b6a24 100644 --- a/ppcls/configs/Vehicle/ResNet50_ReID.yaml +++ b/ppcls/configs/Vehicle/ResNet50_ReID.yaml @@ -52,11 +52,8 @@ Optimizer: name: Momentum momentum: 0.9 lr: - name: MultiStepDecay + name: Cosine learning_rate: 0.01 - milestones: [30, 60, 70, 80, 90, 100, 120, 140] - gamma: 0.5 - verbose: False last_epoch: -1 regularizer: name: 'L2' -- GitLab