Train_PixPro_model.md

Train PixPro Model

Introduction

PASSL reproduces PixPro, which is an unsupervised visual feature learning approach by leveraging pixel-level pretext task.

Installation

• See INSTALL.md

Data Preparation

• See GETTING_STARTED.md

Implemented Models

Models are all trained with ResNet-50 backbone.

	epochs	official results	passl results	Backbone	Model
PixPro	100	55.1(fp16)	57.2(fp32)	ResNet-50	download

Implemented COCO with PaddleDetection

Backbone	Model	images/GPU	lr schedule	FPS	Box AP
ResNet50(supervised)	FCOS（PaddleDetection）	2	1x	----	39.6
ResNet50(official)	FCOS（mmDetection）	2	1x	----	37.4
ResNet50(pixpro)	FCOS（PaddleDetection）	2	1x	----	38.4

Getting Started

1. Train PixPro

single gpu

python tools/train.py -c configs/pixpro/pixpro_base_r50_100ep.yaml

multiple gpus

python -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" tools/train.py -c configs/pixpro/pixpro_base_r50_100ep.yaml

Pretraining models with 100 epochs can be found at [pixpro](https://passl .bj.bcebos.com/models/pixpro_r50_ep100_no_instance_with_linear.pdparams)

Note: The default learning rate in config files is for 8 GPUs. If using differnt number GPUs, the total batch size will change in proportion, you have to scale the learning rate following new_lr = old_lr * new_ngpus / old_ngpus.

2. Extract backbone weights

python tools/extract_weight.py ${CHECKPOINT} --output ${WEIGHT_FILE}

3. Evaluation on ImageNet Linear Classification

Train:

python -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" tools/train.py -c configs/pixpro/pixpro_base_r50_100ep_IM_clas.yaml --pretrained ${WEIGHT_FILE}

Evaluate:

python -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" tools/train.py -c configs/pixpro/pixpro_base_r50_100ep_IM_clas.yaml --load ${CLS_WEGHT_FILE} --evaluate-only