Stanford Online Products Retrieval Leaderboard
The goal of this page is:
- To keep on track of state-of-the-art (SOTA) on Stanford Online Products Retrieval and image retrieval models
- To see the comparison of famous image retrieval models at a glance
- To access their research papers and implementations on different frameworks
If you want to keep following this page, please star and watch this repository.
Leaderboard
Value References
Numbers in the ‘Reference’ column indicate the reference webpages and papers for each model’s values.
- Making Classification Competitive for Deep Metric Learning
- Batch Feature Erasing for Person Re-identification and Beyond
- Learning Embeddings for Product Visual Search with Triplet Loss and Online Sampling
- Generalization in Metric Learning: Should the Embedding Layer be the Embedding Layer
- Heated-Up Softmax Embedding
- Combination of Multiple Global Descriptors for Image Retrieval
- Hardness-Aware Deep Metric Learning
- Improved Embeddings with Easy Positive Triplet Mining
Contribution
If you want
- To add any value from your own model and paper on the leaderboard
- To revise any mistake in the leaderboard
- To update any value on the existing model
please leave your suggestion in the issue page of this repository.
Author
Byung Soo Ko / kobiso62@gmail.com