Paddle-Image-Models

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A PaddlePaddle version image model zoo.

Install Package

• Install by pip：

  $ pip install ppim

• Install by wheel package：【Releases Packages】

Usage

• Quick Start

  import paddle
  from ppim import rednet_26
  
  # Load the model
  model, val_transforms = rednet_26(pretrained=True)
  
  # Model summary 
  paddle.summary(model, input_size=(1, 3, 224, 224))
  
  # Random a input
  x = paddle.randn(shape=(1, 3, 224, 224))
  
  # Model forword
  out = model(x)

• Finetune

  import paddle
  import paddle.nn as nn
  import paddle.vision.transforms as T
  from paddle.vision import Cifar100
  
  from ppim import rexnet_1_0
  
  # Load the model
  model, val_transforms = rexnet_1_0(pretrained=True, class_dim=100)
  
  # Use the PaddleHapi Model
  model = paddle.Model(model)
  
  # Set the optimizer
  opt = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
  
  # Set the loss function
  loss = nn.CrossEntropyLoss()
  
  # Set the evaluate metric
  metric = paddle.metric.Accuracy(topk=(1, 5))
  
  # Prepare the model 
  model.prepare(optimizer=opt, loss=loss, metrics=metric)
  
  # Set the data preprocess
  train_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 dataset
  train_dataset = Cifar100(mode='train', transform=train_transforms, backend='pil')
  val_dataset = Cifar100(mode='test',  transform=val_transforms, backend='pil')
  
  # Finetune the model 
  model.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
  )

Model Zoo

• DLA

• ReXNet

• RedNet

• RepVGG

• HarDNet

• CondenseNet V2

• PiT

• PVT

• TNT

• DeiT

• CaiT

• Swin Transformer