CVPR Paper Reads - Large-scale Product Recognition

CVPR Paper Reads - Large-scale Product Recognition

Paper #1 - 1st Place Solution to CVPR 2021 AliProducts Challenge: Large-scale Product Recognition

Key Lessons

• The final solution employed 11 models including three backbones: efficientnet, efficientnetv2, and nfnet.
• Small models were trained with less epochs and large models were trained with more epoch

Data Augmentation

• RandomCrop: 448*448
• RandomRotation: ±30°
• RandomHorizontalFlip: p=0.5

Paper #2 - Solution for Large-scale Long-tailed Recognition with Noisy Labels

Key Lessons

• CNNs and Transformer, including ResNeSt, EfficientNetV2, and DeiT
• Ensemble three different network architectures with ImageNet pretrained weights, including ResNeSt-101, DeiT-small and EfficientNevV2-m.

Paper #3 - An Effective Ensemble Method for AliProducts Challenge: Large-scale Product Recognition

Key Lessons

• The AliProducts dataset consists of more than 3M images of nearly 50K different products.
• All networks are initialized with pre-trained weights on ImageNet and trained with cross entropy loss.
• As for image augmentation, we use RandomCrop, RandomHorizontalFlip as well as Nomalization

Paper #4 - RETAIL VISION WORKSHOP 2021 - PRODUCT PRICING CHALLENGE(4TH PLACE SOLUTION)

Key Lessons

• First step involves detecting the prices present on shelves. A single class called "pricing"  (Bounding Box)
• Second step is to detect and recognize text present inside the pricing. Google Vision API was used for text detection and recognition
• Price Text Box Extraction: The text box with the max area containing only number was chosen the price box(or integer part of the price). 
• Price Text Cleaning, Price Rounding off

Summary -  As we can see a mix of techniques custom detection, OCR comes into play for item price area detection, parsing, cleaning, and product match based on both text, price, value. We could also do a similar image / key points match too.

More reads - Link

Keep Thinking!!!