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Sportswear Classification

Sportswear or activewear is clothing, including footwear, worn for sport or physical exercise. Sport-specific clothing is worn for most sports and physical exercise, for practical, comfort or safety reasons. Typical sport-specific garments include shorts, tracksuits, T-shirts, tennis shirts and polo shirts. Specialized garments include swimsuits (for swimming), wetsuits (for diving or surfing), ski suits (for skiing) and leotards (for gymnastics).

Sports footwear include trainers, football boots, riding boots, and ice skates. Sportswear also includes some underwear, such as the jockstrap and sports bra.

Sportswear is also at times worn as casual fashion clothing.

Models

Sportswear classification has been divided into different modules based of Sklearn, keras machine learning frameworks. I also have used Auto-Sklearn, TPOT auto-ml for exhaustive optimizations and parameter space searches.

Algorithms used in different modules are as follow.

  • Sklearn : Xtreme Gradient Boosting, K Nearest Neighbours, Support Vector Machines, Naive Bayes
  • Keras : LSTMs with Embedding and/or TF-IDF
  • Auto-Sklearn : Bayesian optimization, meta-learning and ensemble construction.
  • TPOT : Genetic programming.

Run Scripts

Every model has training & sampling scripts. After preprocessing the raw data, an hdf file containing URL texts and their labels will be created during the initialization phase of training. Folder indicates the location of each model's scripts.

Training: python run.py --data_dir --checkpoint_dir --save_dir

Sampling: python sample.py --hdf_file --checkpoint_dir --save_dir --run_type sample

Results

The following table shows a model accuracy and time taken respectively.

Model Accuracy Time
XGB 96.28 Few Minutes
Naive Bayes 90.01 Few Minutes
KNN 90.35 Few Minutes
Support Vector Machine 98.78 Few Minutes
Auto-Sklearn 99.17 2 hours
TPOT 98.95 5 hours
Keras with Embedding 99.51 5 mintutes

The accuracy of 99% has been acheived using Support Vector Machine, Keras LSTMs with Embedding. The best peforming model is Support Vector Machine due to less computation usage and time complexity.

Further Improvements

  • Balance dataset using different resampling techniques.
  • Exploring different kinds of embedding (and training with our data to customize to our requirements) for more semantic and contextual understanding of URL description feature as I have used tf-idf vectorization for topic/document modelling.
  • Analysing special keywords for our task and giving them more weights during training/classification.
  • Use Image as another input feature along with a semantic understanding of Color, Size features & Amount as a good discriminating numerical feature.
  • The above rich features will help improve the model and we can further train Ensemble and/or Deep learning models to increase accuracy with the availability of a large amount of data over the time.