Hi guys,
I’m new to deep learning.
I have a classification task and do the transfer learning using Resnet34(the implementation by PyTorch). The dataset I used is Stanford Car Dataset. For training, I used pretrained weights and fine-tuned the model using the vehicle dataset. The accuracy I got on train dataset and validation dataset is 99% and 89%, respectively (Btw, is there any over-fitting? I’m not sure).
When I do the inference, I feed one image(224x224) to my fine-tuned model and got correct label. But the inference speed is quite slow: around 160 ms. I think the speed should be much faster than this.
So what is a normal inference speed for Resnet34?
And how to increase the inference speed? (I know half precision may help, but not really sure.)
Anyone can help me?
Thanks!