Fix: use absolute resolve URLs for images on org page

README.md

@@ -21,17 +21,17 @@ license: apache-2.0
 
 ## Workflow
 
-<img src="01-ecosystem.png" alt="EdgeFirst Model Zoo Ecosystem"/>
+<img src="https://huggingface.co/spaces/EdgeFirst/README/resolve/main/01-ecosystem.png" alt="EdgeFirst Model Zoo Ecosystem"/>
 
 Every model in the EdgeFirst Model Zoo passes through a validated pipeline. [**EdgeFirst Studio**](https://edgefirst.studio) 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 **full-dataset on-target validation** on real hardware — measuring both accuracy (mAP) and detailed timing breakdown per device. Results are published here on HuggingFace with per-platform performance tables.
 
 ## Model Lifecycle
 
-<img src="02-model-lifecycle.png" alt="Model Lifecycle: Training to Publication"/>
+<img src="https://huggingface.co/spaces/EdgeFirst/README/resolve/main/02-model-lifecycle.png" alt="Model Lifecycle: Training to Publication"/>
 
 ## On-Target Validation
 
-<img src="03-on-target-validation.png" alt="On-Target Validation Pipeline"/>
+<img src="https://huggingface.co/spaces/EdgeFirst/README/resolve/main/03-on-target-validation.png" alt="On-Target Validation Pipeline"/>
 
 Unlike desktop-only benchmarks, EdgeFirst validates every model on **real target hardware** with the full dataset. Each device produces both accuracy metrics (mAP) and a detailed timing breakdown — load, preprocessing, NPU inference, and decode — so you know exactly how a model performs on your specific platform.