Add workflow diagrams as PNG, describe model lifecycle and on-target validation

.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+01-ecosystem.png filter=lfs diff=lfs merge=lfs -text
+02-model-lifecycle.png filter=lfs diff=lfs merge=lfs -text
+03-on-target-validation.png filter=lfs diff=lfs merge=lfs -text
+04-coverage-matrix.png filter=lfs diff=lfs merge=lfs -text

index.html

@@ -209,13 +209,29 @@
         <h2>Workflow</h2>
 
         <div class="diagram-container">
-            <img src="01-ecosystem.svg" alt="EdgeFirst Model Zoo Ecosystem: Training, Validation, and Publication Workflow">
+            <img src="01-ecosystem.png" alt="EdgeFirst Model Zoo Ecosystem: Training, Validation, and Publication Workflow">
         </div>
 
         <p>
             Every model in the EdgeFirst Model Zoo passes through a validated pipeline. <a href="https://edgefirst.studio"><strong>EdgeFirst Studio</strong></a> manages datasets, training, multi-format export (ONNX, TFLite INT8, eIQ Neutron, Kinara DVM, HailoRT HEF, TensorRT), and reference validation. Models are then deployed to our board farm for <strong>full-dataset on-target validation</strong> on real hardware &mdash; measuring both accuracy (mAP) and detailed timing breakdown per device. Results are published here on HuggingFace with per-platform performance tables.
         </p>
 
+        <h2>Model Lifecycle</h2>
+
+        <div class="diagram-container">
+            <img src="02-model-lifecycle.png" alt="Model Lifecycle: 5 stages from training to publication">
+        </div>
+
+        <h2>On-Target Validation</h2>
+
+        <div class="diagram-container">
+            <img src="03-on-target-validation.png" alt="On-Target Validation Pipeline: full dataset validation on real hardware">
+        </div>
+
+        <p>
+            Unlike desktop-only benchmarks, EdgeFirst validates every model on <strong>real target hardware</strong> with the full dataset. Each device produces both accuracy metrics (mAP) and a detailed timing breakdown &mdash; load, preprocessing, NPU inference, and decode &mdash; so you know exactly how a model performs on your specific platform.
+        </p>
+
         <h2>Supported Hardware</h2>
         <div class="badges">
             <img src="https://img.shields.io/badge/NXP-i.MX_8M_Plus-3E3371?style=flat-square&logoColor=white" alt="NXP i.MX 8M Plus">