# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("Kaludi/csgo-weapon-classification")
model = AutoModelForImageClassification.from_pretrained("Kaludi/csgo-weapon-classification")Quick Links
CSGO Weapon Classification
This is a CSGO Weapon Classifier Model that has been trained by 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 Web UI to run the csgo-weapon-classification model:
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
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Kaludi/csgo-weapon-classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")