import torch
model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_5m_m1", pretrained=True)
# or any of these variants
# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_5m_m2", pretrained=True)
# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_9m_m1", pretrained=True)
# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_9m_m2", pretrained=True)
# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_small_m1_05", pretrained=True)
# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_small_m2_05", pretrained=True)
# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_small_m1_075", pretrained=True)
# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_small_m2_075", pretrained=True)
model.eval()

所有预训练模型都要求输入图像按相同方式进行归一化，即形状为 (3 x H x W) 的 3 通道 RGB 小批量图像，其中 H 和 W 预计至少为 224。图像必须加载到 [0, 1] 范围内，然后使用 mean = [0.485, 0.456, 0.406] 和 std = [0.229, 0.224, 0.225] 进行归一化。

以下是一个示例执行过程。

# Download an example image from the pytorch website
import urllib
url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
try: urllib.URLopener().retrieve(url, filename)
except: urllib.request.urlretrieve(url, filename)

# sample execution (requires torchvision)
from PIL import Image
from torchvision import transforms
input_image = Image.open(filename)
preprocess = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model

# move the input and model to GPU for speed if available
if torch.cuda.is_available():
    input_batch = input_batch.to('cuda')
    model.to('cuda')

with torch.no_grad():
    output = model(input_batch)
# Tensor of shape 1000, with confidence scores over ImageNet's 1000 classes
print(output[0])
# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
probabilities = torch.nn.functional.softmax(output[0], dim=0)
print(probabilities)

# Download ImageNet labels
!wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt

# Read the categories
with open("imagenet_classes.txt", "r") as f:
    categories = [s.strip() for s in f.readlines()]
# Show top categories per image
top5_prob, top5_catid = torch.topk(probabilities, 5)
for i in range(top5_prob.size(0)):
    print(categories[top5_catid[i]], top5_prob[i].item())

模型描述

SimpleNet 模型在论文“让我们保持简单，使用简单的架构来超越更深、更复杂的架构”中提出。
这里有 8 个版本的 SimpleNet 模型，分别包含 150 万、320 万、570 万和 950 万个参数。
详细的模型架构参见表 1 和表 2。
下面列出了它们使用预训练模型在 ImageNet 数据集上的 1-crop 错误率。

m2 变体

模型结构 Top-1 错误率 Top-5 错误率
simplenetv1_small_m2_05 38.33 16.512
simplenetv1_small_m2_075 31.494 11.85
simplenetv1_5m_m2 27.97 9.676
simplenetv1_9m_m2 25.77 8.252

m1 变体

模型结构 Top-1 错误率 Top-5 错误率
simplenetv1_small_m1_05 38.878 17.012
simplenetv1_small_m1_075 32.216 12.282
simplenetv1_5m_m1 28.452 10.06
simplenetv1_9m_m1 26.208 8.514

参考文献