Awesome

FastAP: Deep Metric Learning to Rank

This repository contains implementation of the following paper:

Deep Metric Learning to Rank<br> Fatih Cakir*, Kun He*, Xide Xia, Brian Kulis, and Stan Sclaroff (*equal contribution)<br> IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019

We provide trained MatConvNet models and experimental logs for the results in the paper. These models were used to achieve the results in the tables.
The logs also include parameters settings that enable one to re-train a model if desired. It also includes evaluation results with model checkpoints at certain epochs.
- Table 1: Stanford Online Products
  - FastAP, ResNet-18, M=256, Dim=512: [model @ epoch 20, log]
  - FastAP, ResNet-50, M=96, Dim=128: [model @ epoch 30, log]
  - FastAP, ResNet-50, M=96, Dim=512: [model @ epoch 28, log]
  - FastAP, ResNet-50, M=256, Dim=512: [model @ epoch 12, log]
- Table 2: In-Shop Clothes
  - FastAP, ResNet-18, M=256, Dim=512: [model @ epoch 50, log]
  - FastAP, ResNet-50, M=96, Dim=512: [model @ epoch 40, log]
  - FastAP, ResNet-50, M=256, Dim=512: [model @ epoch 35, log]
- Table 3: PKU VehicleID
  - FastAP, ResNet-18, M=256, Dim=512: [model @ epoch 50, log]
  - FastAP, ResNet-50, M=96, Dim=512: [model @ epoch 40, log]
  - FastAP, ResNet-50, M=256, dim=512: [model @ epoch 30, log]
  (M=mini-batch size)
PyTorch code is a direct port from our MATLAB implementation. We haven't tried reproducing the paper results with our PyTorch code. For reproducibility use the MATLAB version.
Note that the mini-batch sampling strategy must also be used alongside the FastAP loss for good results.

For questions and comments, feel free to contact: kunhe@fb.com or fcakirs@gmail.com

MIT