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license: apache-2.0 |
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datasets: |
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- Bingsu/Gameplay_Images |
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language: |
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- en |
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base_model: |
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- google/siglip2-so400m-patch14-384 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- Gameplay |
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- Classcode |
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- '10' |
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--- |
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# **Gameplay-Classcode-10** |
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> **Gameplay-Classcode-10** is a vision-language model fine-tuned from **google/siglip2-base-patch16-224** using the **SiglipForImageClassification** architecture. It classifies gameplay screenshots or thumbnails into one of ten popular video game titles. |
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```py |
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Classification Report: |
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precision recall f1-score support |
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Among Us 0.9990 0.9920 0.9955 1000 |
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Apex Legends 0.9737 0.9990 0.9862 1000 |
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Fortnite 0.9960 0.9910 0.9935 1000 |
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Forza Horizon 0.9990 0.9820 0.9904 1000 |
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Free Fire 0.9930 0.9860 0.9895 1000 |
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Genshin Impact 0.9831 0.9890 0.9860 1000 |
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God of War 0.9930 0.9930 0.9930 1000 |
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Minecraft 0.9990 0.9990 0.9990 1000 |
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Roblox 0.9832 0.9960 0.9896 1000 |
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Terraria 1.0000 0.9910 0.9955 1000 |
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accuracy 0.9918 10000 |
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macro avg 0.9919 0.9918 0.9918 10000 |
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weighted avg 0.9919 0.9918 0.9918 10000 |
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``` |
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The model predicts one of the following **game categories**: |
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- **0:** Among Us |
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- **1:** Apex Legends |
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- **2:** Fortnite |
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- **3:** Forza Horizon |
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- **4:** Free Fire |
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- **5:** Genshin Impact |
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- **6:** God of War |
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- **7:** Minecraft |
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- **8:** Roblox |
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- **9:** Terraria |
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--- |
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# **Run with Transformers 🤗** |
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```python |
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!pip install -q transformers torch pillow gradio |
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``` |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor, SiglipForImageClassification |
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from PIL import Image |
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import torch |
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# Load model and processor |
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model_name = "prithivMLmods/Gameplay-Classcode-10" # Replace with your actual model path |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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# Label mapping |
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id2label = { |
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0: "Among Us", |
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1: "Apex Legends", |
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2: "Fortnite", |
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3: "Forza Horizon", |
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4: "Free Fire", |
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5: "Genshin Impact", |
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6: "God of War", |
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7: "Minecraft", |
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8: "Roblox", |
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9: "Terraria" |
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} |
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def classify_game(image): |
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"""Predicts the game title based on the gameplay image.""" |
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image = Image.fromarray(image).convert("RGB") |
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inputs = processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() |
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predictions = {id2label[i]: round(probs[i], 3) for i in range(len(probs))} |
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predictions = dict(sorted(predictions.items(), key=lambda item: item[1], reverse=True)) |
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return predictions |
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# Gradio interface |
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iface = gr.Interface( |
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fn=classify_game, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(label="Game Prediction Scores"), |
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title="Gameplay-Classcode-10", |
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description="Upload a gameplay screenshot or thumbnail to identify the game title (Among Us, Fortnite, Minecraft, etc.)." |
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) |
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# Launch the app |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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--- |
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# **Intended Use** |
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This model can be used for: |
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- **Automatic tagging of gameplay content for streamers and creators** |
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- **Organizing gaming datasets** |
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- **Enhancing searchability in gameplay video repositories** |
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- **Training AI systems for game-related content moderation or recommendations** |
