Install Package

Usage

Quick Start

importpaddlefromppimimportrednet_26# Load the model with PPIM wheel packagemodel,val_transforms=rednet_26(pretrained=True,return_transforms=True)# Load the model with paddle.hub API# paddlepaddle >= 2.1.0'''model, val_transforms = paddle.hub.load(    'AgentMaker/Paddle-Image-Models:dev',    'rednet_26',    source='github',    force_reload=False,    pretrained=True,    return_transforms=True)'''# Model summarypaddle.summary(model,input_size=(1,3,224,224))# Random a inputx=paddle.randn(shape=(1,3,224,224))# Model forwordout=model(x)

Classification（PaddleHapi）

importpaddleimportpaddle.nnasnnimportpaddle.vision.transformsasTfrompaddle.visionimportCifar100fromppimimportrexnet_1_0# Load the modelmodel,val_transforms=rexnet_1_0(pretrained=True,return_transforms=True,class_dim=100)# Use the PaddleHapi Modelmodel=paddle.Model(model)# Set the optimizeropt=paddle.optimizer.Adam(learning_rate=0.001,parameters=model.parameters())# Set the loss functionloss=nn.CrossEntropyLoss()# Set the evaluate metricmetric=paddle.metric.Accuracy(topk=(1,5))# Prepare the modelmodel.prepare(optimizer=opt,loss=loss,metrics=metric)# Set the data preprocesstrain_transforms=T.Compose([T.Resize(256,interpolation='bicubic'),T.RandomCrop(224),T.ToTensor(),T.Normalize(mean=[0.485,0.456,0.406],std=[0.229,0.224,0.225])])# Load the Cifar100 datasettrain_dataset=Cifar100(mode='train',transform=train_transforms,backend='pil')val_dataset=Cifar100(mode='test',transform=val_transforms,backend='pil')# Finetune the modelmodel.fit(train_data=train_dataset,eval_data=val_dataset,batch_size=256,epochs=2,eval_freq=1,log_freq=1,save_dir='save_models',save_freq=1,verbose=1,drop_last=False,shuffle=True,num_workers=0)

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