readme: ---
  tags:
  - vision
  - image-classification
  - CSGO
  datasets:
  - Kaludi/data-csgo-weapon-classification
  widget:
  - src: https://media.moddb.com/images/downloads/1/219/218947/render.png
    example_title: AK47
  - src: https://lh6.googleusercontent.com/36GwQuG6pKBsW1HEFNLwC1cEUp4nzX8NgWh0G7ruR5YpGJwBm4DoUFsGwZmKN5AXNyTsFmgXvV07OFIEy-33lPPJJb9PZZndE_-xYqWaaWmDhoqaMoIJAF7xXh4-xO2mufoTsgdn
    example_title: AWP
  - src: https://community.akamai.steamstatic.com/economy/image/-9a81dlWLwJ2UUGcVs_nsVtzdOEdtWwKGZZLQHTxDZ7I56KU0Zwwo4NUX4oFJZEHLbXH5ApeO4YmlhxYQknCRvCo04DEVlxkKgpopuP1FAR17PDJZS5J-dC6h7-bzqfLP7LWnn9u5MRjjeyPpYrz2lfhqEZvMm_6JdOXelJrYVqDrlbsxe66hp-56JjKnXowvCgg42GdwUIaw99WQg/360fx360f
    example_title: P90
  - src: https://www.talkesport.com/wp-content/uploads/csgo-new-update.jpg
    example_title: M4A1
  - src: https://mir-s3-cdn-cf.behance.net/project_modules/fs/c6040519785195.562e039771066.png
    example_title: USP
  co2_eq_emissions:
    emissions: 0.0421564161796381
  ---
  
  # CSGO Weapon Classification
  
  This is a CSGO Weapon Classifier Model that has been trained by [Kaludi](https://huggingface.co/Kaludi) to recognize **11** different types of Counter-Strike: Global Offensive (CSGO) Weapons, which include **AK-47,AWP,Famas,Galil-AR,Glock,M4A1,M4A4,P-90,SG-553,UMP,USP**. The model is capable of accurately classifying the weapon name present in an image. With its deep understanding of the characteristics of each weapon in the game, the model is a valuable tool for players and fans of CSGO.
  
  ### Gradio
  
  Tis model supports a [Gradio](https://github.com/gradio-app/gradio) Web UI to run the csgo-weapon-classification model:
  [![Open In HF Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/Kaludi/CSGO-Weapon-Classification_App)
  
  
  ## Validation Metrics
  
  - Loss: 0.282
  - Accuracy: 0.945
  - Macro F1: 0.946
  - Micro F1: 0.945
  - Weighted F1: 0.946
  - Macro Precision: 0.948
  - Micro Precision: 0.945
  - Weighted Precision: 0.948
  - Macro Recall: 0.945
  - Micro Recall: 0.945
  - Weighted Recall: 0.945