Fashio Detection & Segmentation, Multi-Labels Classification and Attributes Embedding

1. Jeans Detection and Segmentation

Detectron2 Model - Generalized R-CNN Models

Detectron aims to provide a high quality and industry standard codebase for object detection research. The results it has posted are incredibly accurate. The image above shows the prediction power of the software. The following object related algorithms are embedded in Detectron:

• Mask R-CNN
• RetinaNet
• Faster R-CNN
• RPN
• Fast R-CNN
• R-FCN

Detectron2 Archtecture

Dataset & Annotation

• From Jeans Websites
• Annotation the jeans from the each images

Notebook & Results

• Detailed Training Information is in the Notebook

• Reseults:

2.Fashion Multi-Label Classification

• multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple labels may be assigned to each instance. Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to. Formally, multi-label classification is the problem of finding a model that maps inputs x to binary vectors y (assigning a value of 0 or 1 for each element (label) in y).

• For every garment, there are a lot attributes. We can treated as the multi-labels for training and classifcation.

• Training:

  • Use fastai v1 and v2 to train the model.
  • Notebook(v1)
  • Notebook(v2)

-Results:

3 BERT Attributed(Fashion) Embedding

• BERT BERT (Bidirectional Encoder Representations from Transformers), released in late 2018, is the model we will use in this tutorial to provide readers with a better understanding of and practical guidance for using transfer learning models in NLP. BERT is a method of pretraining language representations that was used to create models that NLP practicioners can then download and use for free. You can either use these models to extract high quality language features from your text data, or you can fine-tune these models on a specific task (classification, entity recognition, question answering, etc.) with your own data to produce state of the art predictions.

• BERT embeddings

Use BERT to extract features, namely word and sentence embedding vectors, from text data. What can we do with these word and sentence embedding vectors? First, these embeddings are useful for keyword/search expansion, semantic search and information retrieval. For example, if you want to match customer questions or searches against already answered questions or well documented searches, these representations will help you accuratley retrieve results matching the customer's intent and contextual meaning, even if there's no keyword or phrase overlap.

• Notebooks Input: the fashion product description or product attributes Out:Word or Sentences Embedding Vector (1024), use pre-trained bert-large-uncased,For 24-layer, 1024-hidden, 16-heads, 340M parameters. Trained on lower-cased English text.

  Notebook