Bag of Tricks for Retail Product Image Classification

@inproceedings{Srivastava2020BagOT,
  title={Bag of Tricks for Retail Product Image Classification},
  author={Muktabh Mayank Srivastava},
  booktitle={International Conference on Image Analysis and Recognition},
  year={2020}
}
Retail Product Image Classification is an important Computer Vision and Machine Learning problem for building real world systems like self-checkout stores and automated retail execution evaluation. In this work, we present various tricks to increase accuracy of Deep Learning models on different types of retail product image classification datasets. These tricks enable us to increase the accuracy of fine tuned convnets for retail product image classification by a large margin. As the most… 

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References

SHOWING 1-10 OF 25 REFERENCES

Context-aware hybrid classification system for fine-grained retail product recognition

Using contextual relationships in retail shelves to improve the classification accuracy by executing a context-aware approach improves the accuracy of context-free image classifiers such as Support Vector Machines (SVMs) by combining them with a probabilistic graphical model such as Hidden Markov Models (HMMs) or Conditional Random Fields (CRFs).

Recognizing Products: A Per-exemplar Multi-label Image Classification Approach

This paper presents an efficient approach for per-exemplar multi-label image classification, which targets the recognition and localization of products in retail store images, and provides a large novel dataset and labeling tools for products image search.

Fine-Grained Grocery Product Recognition by One-Shot Learning

A novel hybrid classification approach that combines feature-based matching and one-shot deep learning with a coarse-to-fine strategy to improve the accuracy of fine-grained grocery products recognition effectively is presented.

Domain invariant hierarchical embedding for grocery products recognition

Maximum-Entropy Fine-Grained Classification

This work revisits Maximum-Entropy learning in the context of fine-grained classification, and provides a training routine that maximizes the entropy of the output probability distribution for training convolutional neural networks on FGVC tasks.

Deep Learning

Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.

Fine-Grained Recognition of Thousands of Object Categories with Single-Example Training

This work proposes a non-parametric probabilistic model for initial detection, CNN-based refinement and temporal integration where applicable, and successfully demonstrates its usefulness in a variety of experiments achieving state-of-the-art performance.

ImageNet Large Scale Visual Recognition Challenge

The creation of this benchmark dataset and the advances in object recognition that have been possible as a result are described, and the state-of-the-art computer vision accuracy with human accuracy is compared.

Benchmark for Generic Product Detection: A Low Data Baseline for Dense Object Detection

This work trains a standard object detector on a small, normally packed dataset with data augmentation techniques, and creates a varied benchmark for generic SKU product detection by providing full annotations for multiple public datasets.

Exploring the Limits of Weakly Supervised Pretraining

This paper presents a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images and shows improvements on several image classification and object detection tasks, and reports the highest ImageNet-1k single-crop, top-1 accuracy to date.