IBN-Net

import torch
model = torch.hub.load('XingangPan/IBN-Net', 'resnet50_ibn_a', pretrained=True)
model.eval()

所有預訓練模型都要求輸入影像以相同的方式進行歸一化，即由形狀為 (3 x H x W) 的 3 通道 RGB 影像組成的小批次資料，其中 H 和 W 預計至少為 224。影像必須載入到 [0, 1] 範圍內，然後使用 mean = [0.485, 0.456, 0.406] 和 std = [0.229, 0.224, 0.225] 進行歸一化。

這是一個示例執行。

# 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

IBN-Net是一種具有域/外觀不變性的CNN模型。受風格遷移工作的啟發，IBN-Net在一個深度網路中巧妙地統一了例項歸一化和批歸一化。它提供了一種簡單的方法，在不增加模型複雜性的情況下，同時提高建模和泛化能力。IBN-Net特別適用於跨域或人物/車輛再識別任務。

以下列出了使用預訓練模型在ImageNet資料集上的相應準確率。

模型名稱	Top-1 準確率	Top-5 準確率
resnet50_ibn_a	77.46	93.68
resnet101_ibn_a	78.61	94.41
resnext101_ibn_a	79.12	94.58
se_resnet101_ibn_a	78.75	94.49

以下列出了在兩個 Re-ID 基準資料集 Market1501 和 DukeMTMC-reID 上的 rank1/mAP（來自 michuanhaohao/reid-strong-baseline）。

骨幹網路	Market1501	DukeMTMC-reID
ResNet50	94.5 (85.9)	86.4 (76.4)
ResNet101	94.5 (87.1)	87.6 (77.6)
SeResNet50	94.4 (86.3)	86.4 (76.5)
SeResNet101	94.6 (87.3)	87.5 (78.0)
SeResNeXt50	94.9 (87.6)	88.0 (78.3)
SeResNeXt101	95.0 (88.0)	88.4 (79.0)
ResNet50-IBN-a	95.0 (88.2)	90.1 (79.1)

參考文獻

• 一石二鳥：透過 IBN-Net 增強學習和泛化能力