README.md

---
model_name: ACE-V1.1
pipeline_tag: object-detection
license: cc-by-nc-4.0
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: 1.00
      name: Background Specificity
---

# 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.

Paper: [arXiv:2506.14318](https://huggingface.co/papers/2506.14318)

---

# 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** bf210b74eb61c4729a8155137ba830ada8106c14ddd59e0b2e4886b3bde53056

**ACE-V1.1 SHA 256** 7d95e4e369f39149866c38d44aec0c668ad703147fd30b28df99e514e41fd853

Generated 01-19-2026 | 18:00 EST

---

### 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**
![Confusion Matrix](confusion_matrix_normalized.png)
*Figure 1: Normalized Confusion Matrix showing perfect separation of healthy tissue (Background).*

![Precision-Recall Curve](BoxPR_curve.png)
*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`

---

### Usage Guide
To run inference with the ACE-V1.1 weights, use the following snippet:

```python
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)
```

---

### 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](https://huggingface.co/LexBwmn/ACE-V1)}},
  note         = {Fine-tuned YOLO11 on the BRISC 2025 Dataset (arXiv:2506.14318)},
  version      = {1.1.0},
  hash         = {7d95e4e369f39149866c38d44aec0c668ad703147fd30b28df99e514e41fd853}
}