Update README.md

README.md

@@ -29,10 +29,12 @@ tags:
 
 VAAS (Vision-Attention Anomaly Scoring) is a dual-module vision framework for **image anomaly detection and localization**.
 It combines **global attention-based reasoning** with **patch-level self-consistency analysis** to produce a **continuous, interpretable anomaly score** alongside dense spatial anomaly maps.
-
 Rather than making binary decisions, VAAS estimates **where anomalies occur** and **how strongly they deviate from learned visual regularities**, enabling explainable image analysis and integrity assessment.
 
-## Read Paper
+## Examples of detection and scoring
+![Inference with visual example](docs/visualizations/COCO_DF_I000B00000_00966250_vaas.png)
+
+## Read Research Paper
 
 - [Arxiv version](https://arxiv.org/abs/2512.15512)
 - [Conference version](https://arxiv.org/abs/2512.15512)
@@ -58,27 +60,13 @@ These components are combined via a hybrid scoring mechanism:
 
 `S_H` provides a continuous measure of anomaly intensity rather than a binary decision.
 
----
-## Intended Use
-
-This model can be used for:
-
-* Image anomaly detection
-* Visual integrity assessment
-* Explainable inspection of irregular regions
-* Research on attention-based anomaly scoring
-* Prototyping anomaly-aware vision systems
-
-It supports **CPU-only inference** and **GPU-accelerated inference**.
-GPU usage is recommended for faster processing but is not required.
-
 ---
 
 ## Installation
 
 VAAS is distributed as a **lightweight inference library** and can be installed instantly.
 
-PyTorch is **only required when running inference or loading pretrained models**.  
+PyTorch is **only required when executing inference or loading pretrained VAAS models**.  
 This allows users to inspect, install, and integrate VAAS without heavy dependencies.
 
 *This model was produced using `vaas==0.1.7`, but newer versions of VAAS may also be compatible for inference.*
@@ -92,7 +80,7 @@ Follow the official PyTorch installation guide for your platform:
 
 [https://pytorch.org/get-started/locally/](https://pytorch.org/get-started/locally/)
 
-**Quick installation**
+**Quick installation (CPU)**
 ```sh
 pip install torch torchvision
 ```
@@ -109,9 +97,16 @@ VAAS will automatically detect PyTorch at runtime and raise a clear error if it
 
 ## Usage
 
+---
+
+**Try VAAS instantly in Colab (no setup required):**  
+👉 [Open the interactive Colab demo](https://colab.research.google.com/drive/1aGQc_ZpPhDOEf7G4_-p03_Fmtdg_QcGd?usp=sharing)
+
+---
 
+### 1. Quick start: run VAAS and get a visual result
 
-### 1. Basic inference on local and online images
+The fastest way to verify VAAS is working is to generate a visualization from a single image.
 
 ```python
 from vaas.inference.pipeline import VAASPipeline
@@ -122,7 +117,7 @@ from io import BytesIO
 pipeline = VAASPipeline.from_pretrained(
     "OBA-Research/vaas-v1-df2023",
     device="cpu",
-    alpha=0.5
+    alpha=0.5,
 )
 
 # # Option A: Using a local image
@@ -132,54 +127,75 @@ pipeline = VAASPipeline.from_pretrained(
 # Option B: Using an online image
 url = "https://raw.githubusercontent.com/OBA-Research/VAAS/main/examples/images/COCO_DF_C110B00000_00539519.jpg"
 image = Image.open(BytesIO(requests.get(url).content)).convert("RGB")
+
+pipeline.visualize(
+    image=image,
+    save_path="vaas_visualization.png",
+    mode="all",        # "all", "px", "binary", "fx"
+    threshold=0.5,
+)
+```
+
+This produces a single figure containing:
+
+- The original image  
+- Patch-level anomaly heatmaps (Px)  
+- Global attention overlays (Fx)  
+- A gauge showing the hybrid anomaly score (S_H)
+
+The output is saved to `vaas_visualization.png`.
+
+For examples:
+![Inference with visual example](docs/visualizations/COCO_DF_I000B00000_00966250_vaas.png)
+![Inference with visual example](docs/visualizations/COCO_DF_S000B00000_00120651_vaas.png)
+![Inference with visual example](docs/visualizations/COCO_DF_C110B00000_00539519_vaas.png)
+
+---
+
+### 2. Programmatic inference (scores + anomaly map)
+
+For numerical outputs and downstream processing, call the pipeline directly:
+
+```python
 result = pipeline(image)
 
 print(result)
 anomaly_map = result["anomaly_map"]
-
 ```
 
-#### Output Format
+#### Output format
 
 ```python
 {
-  "S_F": float,
-  "S_P": float,
-  "S_H": float,
-  "anomaly_map": numpy.ndarray  # shape (224, 224)
+  "S_F": float,              # global attention score
+  "S_P": float,              # patch consistency score
+  "S_H": float,              # hybrid anomaly score
+  "anomaly_map": ndarray     # shape (224, 224)
 }
 ```
 
-### 2. Inference with visual explanation
-
-VAAS can also generate a qualitative visualization combining:
+---
 
-* Patch-level anomaly heatmaps (Px)
-* Global attention maps (Fx)
-* Final hybrid anomaly score (S_H)
+### Notes
 
-```python
+- VAAS supports both local and online images  
+- PyTorch is loaded lazily and only required at runtime  
+- CPU inference is supported; GPU accelerates execution but is optional  
 
-pipeline.visualize(
-    image=image,
-    save_path="vaas_visualization.png",
-    mode="all",        # options: "all", "px", "binary", "fx"
-    threshold=0.5,
-)
-```
+---
 
-This will save a figure containing:
+## Intended Use
 
-* Original image
-* Patch-level anomaly overlays
-* Global attention overlays
-* A gauge-style visualization of the hybrid anomaly score
+This model can be used for:
 
-For examples:
+* Image anomaly detection
+* Visual integrity assessment
+* Explainable inspection of irregular regions
+* Research on attention-based anomaly scoring
+* Prototyping anomaly-aware vision systems
 
-![Inference with visual example](docs/visualizations/COCO_DF_I000B00000_00966250_vaas.png)
-![Inference with visual example](docs/visualizations/COCO_DF_S000B00000_00120651_vaas.png)
-![Inference with visual example](docs/visualizations/COCO_DF_C110B00000_00539519_vaas.png)
+It supports **CPU-only inference** and **GPU-accelerated inference**.
+GPU usage is recommended for faster processing but is not required.
 
 ---