Update README.md

README.md

@@ -1,3 +1,104 @@
 ---
-license: mit
+language:
+- en
+tags:
+- anomaly-detection
+- industrial-inspection
+- computer-vision
+- patchcore
+- anomalib
+- mvtec-ad-2
+- defect-detection
+- manufacturing
+- pytorch
+library_name: anomalib
+pipeline_tag: image-classification
 ---
+
+# Vial PatchCore — Industrial Anomaly Detection
+
+A PatchCore-based industrial anomaly detection model trained for the **Vial** category of the **MVTec AD 2** dataset.
+
+The model is designed to distinguish anomalous vial images from normal vial images and provides an anomaly map for visualizing regions that contribute to the detected anomaly.
+
+## 🚀 Live Demo
+
+Try the deployed model through the interactive Gradio application:
+
+**[Industrial Vial Defect Detection — Hugging Face Space](https://huggingface.co/spaces/pranamjain/industrial-vial-defect-detection)**
+
+Upload a vial image and receive:
+
+- Normal / Defective prediction
+- Anomaly score
+- Anomaly heatmap
+- Visual localization of suspicious regions
+
+---
+
+## 🧠 Model Overview
+
+### Architecture
+
+**PatchCore**
+
+PatchCore is an industrial anomaly detection approach that represents image patches using deep visual features and compares them against a memory bank representing normal samples.
+
+The model is trained to learn the visual characteristics of normal vial images. During inference, deviations from the learned normal representation produce higher anomaly scores.
+
+### Model Details
+
+| Property | Value |
+|---|---|
+| Architecture | PatchCore |
+| Framework | PyTorch |
+| Library | Anomalib |
+| Dataset | MVTec AD 2 |
+| Category | Vial |
+| Input Resolution | 256 × 256 |
+| Task | Industrial Anomaly Detection |
+| Model File | `model.ckpt` |
+
+---
+
+## 📊 Evaluation
+
+The trained Vial model was evaluated on the MVTec AD 2 public test set.
+
+| Metric | Score |
+|---|---:|
+| Image AUROC | **0.7578** |
+| Image F1 Score | **0.5315** |
+| Pixel AUROC | **0.9142** |
+| Pixel F1 Score | **0.1197** |
+
+### Interpretation
+
+**Image AUROC — 0.7578**
+
+The model provides useful separation between normal and anomalous vial images at the image level.
+
+**Image F1 Score — 0.5315**
+
+The F1 score reflects the balance between precision and recall under the evaluation threshold used during the original evaluation.
+
+**Pixel AUROC — 0.9142**
+
+The high pixel-level AUROC indicates that the model can effectively rank anomalous regions relative to normal regions.
+
+**Pixel F1 Score — 0.1197**
+
+The lower pixel-level F1 indicates that precise defect segmentation remains challenging, even though the anomaly map provides useful localization information.
+
+---
+
+## 🎯 Deployment Threshold
+
+During development, the default image-level threshold resulted in a relatively high number of false negatives for the Vial model.
+
+An additional threshold analysis was performed using the MVTec AD 2 public test set.
+
+The experimental deployment threshold selected for the interactive demo is:
+
+```text
+0.09