Object Detection
ultralytics
Safetensors
yolo
medical
tumor-detection
yolo11
brain tumor
computer vision
Eval Results (legacy)
Instructions to use LexBwmn/ACE-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use LexBwmn/ACE-V1 with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("LexBwmn/ACE-V1", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
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README.md
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**ACE-V1.1** is a specialized computer vision model fine-tuned for MRI brain tumor detection. This version is a critical update that eliminates "hallucinations" (False Positives) in healthy brain tissue.
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> **NOTICE:** ACE-V1.1 uses custom channel architecture for 1.00 specificity.
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> **Video Speed:** 2x. 25-30 FPS real-time inference.
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**ACE-V1.1** is a specialized computer vision model fine-tuned for MRI brain tumor detection. This version is a critical update that eliminates "hallucinations" (False Positives) in healthy brain tissue.
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> **NOTICE:** ACE-V1.1 uses custom channel architecture for 1.00 specificity.
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