Super-squash branch 'main' using huggingface_hub

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README.md

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+---
+model_name: ACE-V1.1
+pipeline_tag: object-detection
+library_name: ultralytics
+widget:
+- src: "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-guide/mri_example.jpg"
+example_title: "Sample Brain Scan"
+space_companion: LexBwmn/ACE_DEMO
+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
+
+[![Socket Badge](https://badge.socket.dev/huggingface/package/lexbwmn/ace-v1?version=9fc388072e3ddfbe5027c4c0f9d290ce73fae547)](https://badge.socket.dev/huggingface/package/lexbwmn/ace-v1?version=9fc388072e3ddfbe5027c4c0f9d290ce73fae547)[![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/)![Python](https://img.shields.io/badge/Made%20with-Python-3776AB?logo=python&logoColor=white)![Format](https://img.shields.io/badge/Format-Safetensors-green)
+
+**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`
+---
+
+### 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}
+}
+
+---
+
+## Security & Audit
+This model has been independently indexed and scanned by [Socket.dev](https://socket.dev/huggingface/package/lexbwmn/ace-v1) for supply-chain security. 
+- **Status:** Verified Clean
+- **Security Score:** Passed all deep-malware and "Pickle" injection tests.
+- **License Compliance:** CC BY-NC 4.0 (Verified).
+
+---
+
+## Contact
+For research inquiries or commercial licensing, please contact:
+**LexBwmnDev@gmail.com**

archive/ACE-V1.safetensors

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config.json

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+{
+  "model_type": "yolo",
+  "architectures": [
+    "YOLO11"
+  ],
+  "ultralytics_version": "8.3.0",
+  "task": "detect"
+}

data.yaml

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+# ACE V1.1 - Brain Tumor Localization
+# Developed by: Alexa Bowman 
+
+path: .
+train: ../train/images
+val: ../valid/images
+test: ../test/images
+
+nc: 1
+names: ['tumor']
+

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