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--- |
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tags: |
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- vision |
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- image-classification |
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- CSGO |
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datasets: |
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- Kaludi/data-csgo-weapon-classification |
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widget: |
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- src: https://media.moddb.com/images/downloads/1/219/218947/render.png |
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example_title: AK47 |
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- src: https://lh6.googleusercontent.com/36GwQuG6pKBsW1HEFNLwC1cEUp4nzX8NgWh0G7ruR5YpGJwBm4DoUFsGwZmKN5AXNyTsFmgXvV07OFIEy-33lPPJJb9PZZndE_-xYqWaaWmDhoqaMoIJAF7xXh4-xO2mufoTsgdn |
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example_title: AWP |
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- src: https://community.akamai.steamstatic.com/economy/image/-9a81dlWLwJ2UUGcVs_nsVtzdOEdtWwKGZZLQHTxDZ7I56KU0Zwwo4NUX4oFJZEHLbXH5ApeO4YmlhxYQknCRvCo04DEVlxkKgpopuP1FAR17PDJZS5J-dC6h7-bzqfLP7LWnn9u5MRjjeyPpYrz2lfhqEZvMm_6JdOXelJrYVqDrlbsxe66hp-56JjKnXowvCgg42GdwUIaw99WQg/360fx360f |
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example_title: P90 |
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- src: https://www.talkesport.com/wp-content/uploads/csgo-new-update.jpg |
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example_title: M4A1 |
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- src: https://mir-s3-cdn-cf.behance.net/project_modules/fs/c6040519785195.562e039771066.png |
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example_title: USP |
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co2_eq_emissions: |
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emissions: 0.0421564161796381 |
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--- |
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# CSGO Weapon Classification |
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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. |
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### Gradio |
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Tis model supports a [Gradio](https://github.com/gradio-app/gradio) Web UI to run the csgo-weapon-classification model: |
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[![Open In HF Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/Kaludi/CSGO-Weapon-Classification_App) |
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## Validation Metrics |
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- Loss: 0.282 |
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- Accuracy: 0.945 |
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- Macro F1: 0.946 |
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- Micro F1: 0.945 |
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- Weighted F1: 0.946 |
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- Macro Precision: 0.948 |
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- Micro Precision: 0.945 |
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- Weighted Precision: 0.948 |
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- Macro Recall: 0.945 |
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- Micro Recall: 0.945 |
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- Weighted Recall: 0.945 |