ACE-V1.1 & ACE-V1 .pt:

No .pt files have been taken down for safety reasons, new files will be uploaded with 48 hours!

ACE-V1.1: Brain Tumor Detection

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.

Integrity

ACE-V1.1 is a unique digital asset protected under CC-BY-NC-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 f4c7937a219e4c176853813109f7dc012770d44ff2686d2759bb857a8dcd0de4

ACE-V1.1 SHA 256 0eed1ebda4a95f71720b26d0f81de960f3e5837ffa4c0c88e2bb26813b355fe2

Generated 01-13-2026 | 13:00 local time

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 0.925 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)

Validation Proof

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

Figure 2: Precision-Recall curve confirming the 0.925 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

from ultralytics import YOLO

# Load the ACE-V1.1 weights
model = YOLO('ACE-V1.1.pt')

# Run inference with the optimal threshold
results = model.predict(source='mri_scan.jpg', conf=0.466, save=True)

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