Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+tags:
+- medical-imaging
+- pressure-sore
+- pressure-ulcer
+- wound-classification
+- yolo
+- ultralytics
+- two-stage
+- binary-classification
+- multiclass-classification
+- computer-vision
+metrics:
+- accuracy
+- f1
+- roc-auc
+pipeline_tag: image-classification
+---
+
+# Pressure Sore Classifier — YOLO 2-Stage Weights
+
+[![Python](https://img.shields.io/badge/Python-3.11+-blue.svg)](https://www.python.org/downloads/)
+[![Ultralytics](https://img.shields.io/badge/Ultralytics-YOLO-orange.svg)](https://ultralytics.com/)
+[![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/license/mit)
+
+Six YOLO classification weights for a **2-stage cascade pipeline** that detects and stages pressure sores from clinical photographs. Stage 1 determines whether a pressure sore is present; Stage 2 classifies its severity (Stage I–IV).
+
+Full project code, training notebooks, and web application:  
+👉 **[github.com/MrCzaro/PS_Classifier](https://github.com/MrCzaro/PS_Classifier)**
+
+For the 3-level hierarchical cascade variant (8 models, binary decisions at every level):  
+👉 **[MrCzaro/Pressure_sore_cascade_classifier_YOLO](https://huggingface.co/MrCzaro/Pressure_sore_cascade_classifier_YOLO)**
+
+---
+
+## Medical Context
+
+Pressure sores are staged I–IV based on tissue damage depth:
+
+| Stage | Definition | Key Visual Feature |
+|-------|-----------|-------------------|
+| **I**   | Non-blanchable erythema, intact skin | Redness, no open wound |
+| **II**  | Partial-thickness skin loss, exposed dermis | Shallow open ulcer or blister |
+| **III** | Full-thickness skin loss, fat visible | Deep crater, no exposed bone/tendon |
+| **IV**  | Full-thickness tissue loss, muscle/bone exposed | Exposed deep structures, eschar/slough |
+
+---
+
+## Pipeline
+
+```
+Image
+  │
+  ▼
+[Stage 1]  Binary Detection — PS vs No-PS
+           3-model ensemble: YOLO11s + YOLOv8x + YOLO26x
+  │ NO  → "No pressure sore detected"
+  │ YES ↓
+[Stage 2]  Multiclass Staging — Stage I / II / III / IV
+           3-model ensemble: YOLOv8n + YOLO26m + YOLOv8x
+```
+
+Both stages use **averaged softmax probabilities** across three architecturally distinct YOLO families (v8, YOLO11, YOLO26) for robustness.
+
+---
+
+## Files
+
+| File | Stage | Task | Accuracy | AUC-ROC |
+|------|-------|------|----------|---------|
+| `Binary_YOLOv8x.pt` | S1 | PS vs No-PS | 1.0000 | 0.9998 |
+| `Binary_YOLOv11s.pt`  | S1 | PS vs No-PS | 1.0000 | 1.0000 |
+| `Binary_YOLOv26x.pt` | S1 | PS vs No-PS | 1.0000 | 1.0000 |
+| `Multiclass_YOLOv8n.pt`        | S2 | Stage I–IV  | 0.8571 | 0.9486 (macro OvR) |
+| `Multiclass_YOLOv8x.pt`       | S2 | Stage I–IV  | 0.8571 | 0.9306 (macro OvR) |
+| `Multiclass_YOLOv26m.pt`        | S2 | Stage I–IV  | 0.8571 | 0.9247 (macro OvR) |
+
+---
+
+
+
+## Performance
+
+### Stage 1 — Binary Detection (3-Model Ensemble)
+
+| Model | Accuracy | Macro F1 | AUC-ROC |
+|-------|----------|----------|---------|
+| YOLO11s | 1.0000 | 1.0000 | 0.9998 |
+| YOLOv8x | 1.0000 | 1.0000 | 1.0000 |
+| YOLO26x | 1.0000 | 1.0000 | 1.0000 |
+| **Ensemble** | **1.0000** | **1.0000** | **—** |
+
+### Stage 2 — Multiclass Staging (3-Model Ensemble)
+
+| Model | Accuracy | Macro F1 | Macro AUC-ROC (OvR) |
+|-------|----------|----------|---------------------|
+| YOLOv8n | 0.8571 | 0.8500 | 0.9486 |
+| YOLO26m | 0.8571 | 0.8542 | 0.9306 |
+| YOLOv8x | 0.8571 | 0.8501 | 0.9247 |
+| **Ensemble** | **0.8571** | **0.8514** | **—** |
+
+### Per-Class AUC-ROC (Stage 2)
+
+| Stage | YOLOv8n | YOLO26m | YOLOv8x |
+|-------|---------|---------|---------|
+| Stage I   | 0.9913 | 0.9712 | 0.9934 |
+| Stage II  | 0.9455 | 0.9261 | 0.9268 |
+| Stage III | 0.8852 | 0.8866 | 0.8450 |
+| Stage IV  | 0.9723 | 0.9386 | 0.9337 |
+
+The 3-model staging ensemble achieves **0.8514 Macro F1** — a significant improvement over the flat Torchvision staging baseline (0.74 Macro F1) used in Backend 1 of the same project.
+
+### Model Selection Rationale
+
+**Stage 1** — all three models achieve perfect or near-perfect accuracy and AUC; the three were chosen from different YOLO families (v8, YOLO11, YOLO26) to maximise ensemble diversity.
+
+**Stage 2** — selected by Macro AUC-ROC as primary criterion (threshold-independent, treats all 4 classes equally) with Macro F1 as tiebreaker:
+
+| Pick | Model | Reason |
+|------|-------|--------|
+| 1 | YOLOv8n | Best AUC+F1 balance, strongest Stage IV AUC (0.9723) |
+| 2 | YOLO26m | Best Macro F1 overall, best Stage III AUC among YOLO26 models, architecture diversity |
+| 3 | YOLOv8x | Third AUC, strong Stage I AUC (0.9934), large v8 model |
+
+---
+
+## Installation & Usage
+
+### 1. Install dependencies
+
+```bash
+pip install ultralytics huggingface_hub
+```
+
+### 2. Download weights
+
+**Option A — clone the full repo**
+
+```bash
+git lfs install
+git clone https://huggingface.co/MrCzaro/Pressure_sore_classifier_YOLO
+```
+
+**Option B — download individual files**
+
+```python
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download(
+    repo_id="MrCzaro/Pressure_sore_classifier_YOLO",
+    filename="Binary_YOLOv8x.pt"
+)
+```
+
+### 3. Wire weights into the application
+
+```bash
+git clone https://github.com/MrCzaro/PS_Classifier
+cd PS_Classifier
+pip install -r requirements.txt
+```
+
+Update path constants in `ps_classifier_yolo.py`:
+
+```python
+BINARY_MODEL_PATHS = [
+    "models/yolo/Binary_YOLOv8x.pt",
+    "models/yolo/Binary_YOLOv11s.pt",
+    "models/yolo/Binary_YOLOv26x.pt"
+]
+STAGE_MODEL_PATHS = [
+    "models/yolo/Multiclass_YOLOv8n.pt",
+    "models/yolo/Multiclass_YOLOv8x.pt",
+    "models/yolo/Multiclass_YOLOv26m.pt"
+]
+```
+
+Then run and select **YOLO** from the backend dropdown:
+
+```bash
+python main.py
+# Open http://localhost:5001
+```
+
+### 4. Standalone inference script
+
+```python
+from huggingface_hub import hf_hub_download
+from ultralytics import YOLO
+import numpy as np
+from PIL import Image
+
+REPO = "MrCzaro/Pressure_sore_classifier_YOLO"
+
+BINARY_MODEL_PATHS = [
+    "models/yolo/Binary_YOLOv8x.pt",
+    "models/yolo/Binary_YOLOv11s.pt",
+    "models/yolo/Binary_YOLOv26x.pt"
+]
+STAGE_MODEL_PATHS = [
+    "models/yolo/Multiclass_YOLOv8n.pt",
+    "models/yolo/Multiclass_YOLOv8x.pt",
+    "models/yolo/Multiclass_YOLOv26m.pt"
+]
+
+def ensemble_predict(model_paths, img):
+    all_probs, names = [], None
+    for p in model_paths:
+        model  = YOLO(p)
+        result = model(img, verbose=False)[0]
+        all_probs.append(result.probs.data.cpu().numpy())
+        if names is None:
+            names = result.names
+    avg = np.mean(all_probs, axis=0)
+    idx = int(np.argmax(avg))
+    return idx, names[idx], float(avg[idx])
+
+img = Image.open("your_image.jpg").convert("RGB")
+
+# Stage 1 — binary detection
+_, label, conf = ensemble_predict(BINARY_MODEL_PATHS, img)
+
+if "not" in label.lower():
+    print(f"No pressure sore detected ({conf:.2f})")
+else:
+    # Stage 2 — multiclass staging
+    _, stage, stage_conf = ensemble_predict(STAGE_MODEL_PATHS, img)
+    print(f"Pressure sore detected ({conf:.2f})")
+    print(f"Stage: {stage} ({stage_conf:.2f})")
+```
+
+---
+
+
+
+## ⚠️ Medical Disclaimer
+
+This model is provided **for research and educational purposes only**.
+
+- ❌ NOT a medical device  
+- ❌ NOT certified for clinical diagnosis  
+- ❌ NOT a substitute for professional medical judgment  
+- ❌ NOT validated in clinical trials  
+
+Always consult licensed healthcare professionals for medical diagnosis and treatment.
+
+---
+
+## Contact
+
+**Author**: MrCzaro  
+**GitHub**: [@MrCzaro](https://github.com/MrCzaro)  
+**Project**: [PS_Classifier](https://github.com/MrCzaro/PS_Classifier)  
+**Email**: cezary.tubacki@gmail.com  
+**License**: MIT