Hello, sir. I met similar problem with you about using pytorch pre-trained resnet-101 to extract feature on oxford5k, but the mAP is so low, even worse than vgg16. Did you solve it? how?
I also use the caffe resnet-101 model in the link your provided, but it didn’t improve my mAP. Sad.
My code is below:
import pretrainedmodels

import torch
import torch.nn as nn
from scipy.misc import imread
import numpy as np
import os
from torchvision import transforms
from sklearn.preprocessing import normalize

torch.cuda.set_device(0) 
model_name = 'cafferesnet101'
model = pretrainedmodels.__dict__[model_name](num_classes=1000, pretrained='imagenet')
newmodel = list(model.children())[:-2]
newmodel = nn.Sequential(*newmodel)
newmodel = newmodel.cuda()
for p in newmodel.parameters():
    p.requires_grad = False
newmodel.eval()

normalize_p = transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
transform = transforms.Compose([transforms.ToTensor(),normalize_p])

ave_Result = []

Qimg = imread(Image address, mode='RGB')
Qimg = transform(Qimg).cuda() 
Qimg = torch.unsqueeze(Qimg, 0)
outputQ = newmodel(Qimg).cuda()
outputQ = outputQ.cpu().numpy()[0]
P5A = np.transpose(outputQ,(1,2,0)) # feature map 

ave_DPA = np.mean(P5A,axis=0) # average pooling
ave_DPA = np.reshape(ave_DPA,(1,2048))
ave_DPA = normalize(ave_DPA,axis=1) # normalization
ave_Result.append(ave_DPA)