ACE-V1 / README.md

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model_name: ACE-V1.1
pipeline_tag: object-detection
library_name: ultralytics
license: cc-by-nc-nd-4.0
example_title: Sample Brain Scan
space_companion: LexBwmn/ACE_DEMO
links-to-paper: https://arxiv.org/abs/2506.14318
tags:
  - medical
  - tumor-detection
  - yolo
  - yolo11
  - brain tumor
  - computer vision
  - ultralytics
  - arxiv:2506.14318
model-index:
  - name: ACE-V1.1
    results:
      - task:
          type: object-detection
          name: Brain Tumor Detection
        dataset:
          name: BRISC 2025 (Fateh et al.)
          type: external
          args:
            kaggle: https://www.kaggle.com/datasets/briscdataset/brisc2025/data
        metrics:
          - type: map
            value: 0.899
            name: mAP@0.5
          - type: specificity
            value: 1
            name: Background Specificity

ACE-V1.1: Brain Tumor Detection

MEDICAL RESEARCH USE ONLY. ACE-V1.1 is NOT a cleared medical device. It must not be used for primary diagnosis or clinical decision-making. All outputs must be verified by a qualified professional.

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.

NOTICE: ACE-V1.1 uses custom channel architecture for 1.00 specificity. It will NOT load in a standard YOLO environment.

Integrity & Authorship

ACE-V1.1 is a unique digital asset protected under CC-BY-NC-ND 4.0. This model’s 1.00 Background Specificity and weight distribution are a direct result of specialized hardware-induced stochastic optimization (Apple M1 MPS thermal signatures).

Notice to Institutional Integration Teams: I am aware of current efforts to "wrap" or "compress" this architecture.
Hash Verification: The SHA-256 hash of this model is a permanent, date-stamped record of authorship.
Signature Matching: Any "proprietary" paper claiming a 1.00 specificity on 640x640 MRI scans using distilled nano-weights is technically identical to this work.

ACE-V1 SHA 256 bf210b74eb61c4729a8155137ba830ada8106c14ddd59e0b2e4886b3bde53056

ACE-V1.1 SHA 256 c9c8d895f277c1d8fe2dc4132c066ae6e6ac093d49a5dfe2a90c2c0a3ebfc1ee

Institutional & Commercial Inquiry

This model is licensed under CC BY-NC-ND 4.0. For institutional adoption, commercial licensing, or clinical integration support, please contact: LexBwmnDev@gmail.com

Hardware & Environment

Training Platform: MacBook Pro (M1 Pro Chip)
Acceleration: Apple Silicon Metal Performance Shaders (MPS)
Framework: Ultralytics YOLOv11
Total Epochs: ACE-V1 (90) + Finetuning ACE-V1.1 (30) = 120 Total Epochs

Key Improvements in V1.1

False-Positive Rate: Achieved 1.00 Specificity on healthy brain scans.
Accuracy: Verified 0.899 mAP@0.5 on the independent test set.
Performance: Optimized for a high F1-score to ensure reliable clinical support.

Performance & Validation

Metric	Value
mAP50	0.925
Precision	91.1%
Recall	89.7%
Background Specificity	1.00 (Perfect)

Performance & Testing (Blind Test)

Metric	Value
mAP50	0.899
Precision	90.0%
Recall	83.8%
Background Specificity	1.00 (Perfect)

Test Proof

Figure 1: Normalized Confusion Matrix showing perfect separation of healthy tissue (Background).

Figure 2: Precision-Recall curve confirming the 0.899 mAP score.

Note on Training Logs: The results.png file reflects a high-intensity training run conducted without a validation split (val=False) to maximize the training data pool. Final metrics were verified using a separate hold-out test set as shown in the PR and F1 curves.

Operational Guide

For the most reliable results, I recommend the following inference settings based on the F1-Confidence analysis:

Recommended Confidence: 0.466
Image Size: 640x640

Citation

@misc{bowman2026acev11, author = {Bowman, Alexa}, title = {ACE-V1.1: Optimized Brain Tumor Detection with 1.00 Background Specificity}, year = {2026}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/LexBwmn/ACE-V1}}, note = {Fine-tuned YOLO11 on the BRISC 2025 Dataset (arXiv:2506.14318)}, version = {1.1.0}, hash = {7d95e4e369f39149866c38d44aec0c668ad703147fd30b28df99e514e41fd853} }

Security & Audit

This model has been independently indexed and scanned by Socket.dev for supply-chain security.

Status: Verified Clean
Security Score: Passed all deep-malware and "Pickle" injection tests.
License Compliance: CC BY-NC-ND 4.0 (Verified).