Simplify org card: brief intro with link to EdgeFirst/Models Space

README.md

@@ -4,109 +4,35 @@ emoji: 🔬
 colorFrom: indigo
 colorTo: red
 sdk: static
-pinned: true
+pinned: false
 license: apache-2.0
 ---
 
 # EdgeFirst AI — Spatial Perception at the Edge
 
-**EdgeFirst Perception** is an open-source suite of libraries and microservices for AI-driven spatial perception on edge devices. It supports cameras, LiDAR, radar, and time-of-flight sensors — enabling real-time object detection, segmentation, sensor fusion, and 3D spatial understanding, optimized for resource-constrained embedded hardware.
+**EdgeFirst Perception** is an open-source suite of libraries and microservices for AI-driven spatial perception on edge devices — cameras, LiDAR, radar, and time-of-flight sensors — optimized for resource-constrained embedded hardware.
+
+**[View the EdgeFirst Model Zoo →](https://huggingface.co/spaces/EdgeFirst/Models)**
 
 [![EdgeFirst Studio](https://img.shields.io/badge/EdgeFirst_Studio-3E3371?style=for-the-badge&logoColor=white)](https://edgefirst.studio)
 [![GitHub](https://img.shields.io/badge/GitHub-212529?style=for-the-badge&logo=github&logoColor=white)](https://github.com/EdgeFirstAI)
 [![Documentation](https://img.shields.io/badge/Documentation-1FA0A8?style=for-the-badge&logo=readthedocs&logoColor=white)](https://doc.edgefirst.ai)
-[![Au-Zone Technologies](https://img.shields.io/badge/Au--Zone_Technologies-6C757D?style=for-the-badge)](https://www.au-zone.com)
-
----
-
-## Workflow
-
-<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="https://huggingface.co/spaces/EdgeFirst/README/resolve/main/02-model-lifecycle.png" alt="Model Lifecycle: Training to Publication"/>
 
-## On-Target Validation
+## Models
 
-<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.
-
----
+| Task | Models | Link |
+|------|--------|------|
+| Detection | YOLO26, YOLO11, YOLOv8, YOLOv5 | [Browse →](https://huggingface.co/spaces/EdgeFirst/Models) |
+| Instance Segmentation | YOLO26, YOLO11, YOLOv8 | [Browse →](https://huggingface.co/spaces/EdgeFirst/Models) |
 
 ## Supported Hardware
 
 ![NXP i.MX 8M Plus](https://img.shields.io/badge/NXP-i.MX_8M_Plus-3E3371?style=flat-square&logoColor=white)
 ![NXP i.MX 95](https://img.shields.io/badge/NXP-i.MX_95-3E3371?style=flat-square&logoColor=white)
 ![NXP Ara240](https://img.shields.io/badge/NXP-Ara240-3E3371?style=flat-square&logoColor=white)
-![RPi5 + Hailo-8/8L](https://img.shields.io/badge/RPi5-Hailo--8%2F8L-1FA0A8?style=flat-square&logoColor=white)
+![RPi5 + Hailo](https://img.shields.io/badge/RPi5-Hailo--8%2F8L-1FA0A8?style=flat-square&logoColor=white)
 ![NVIDIA Jetson](https://img.shields.io/badge/NVIDIA-Jetson-76B900?style=flat-square&logoColor=white)
 
 ---
 
-## Model Zoo
-
-Pre-trained YOLO models for edge deployment. Each model repo contains all sizes (nano through x-large), ONNX FP32 and TFLite INT8 formats, with platform-specific compiled variants as they become available.
-
-### Detection
-
-| Model | Sizes | Nano mAP@0.5 | Link |
-|-------|-------|-------------|------|
-| **YOLO26** | n/s/m/l/x | 54.9% | [EdgeFirst/yolo26-det](https://huggingface.co/EdgeFirst/yolo26-det) |
-| **YOLO11** | n/s/m/l/x | 53.4% | [EdgeFirst/yolo11-det](https://huggingface.co/EdgeFirst/yolo11-det) |
-| **YOLOv8** | n/s/m/l/x | 50.2% | [EdgeFirst/yolov8-det](https://huggingface.co/EdgeFirst/yolov8-det) |
-| **YOLOv5** | n/s/m/l/x | 49.6% | [EdgeFirst/yolov5-det](https://huggingface.co/EdgeFirst/yolov5-det) |
-
-### Instance Segmentation
-
-| Model | Sizes | Nano Mask mAP | Link |
-|-------|-------|--------------|------|
-| **YOLO26** | n/s/m/l/x | 37.0% | [EdgeFirst/yolo26-seg](https://huggingface.co/EdgeFirst/yolo26-seg) |
-| **YOLO11** | n/s/m/l/x | 35.5% | [EdgeFirst/yolo11-seg](https://huggingface.co/EdgeFirst/yolo11-seg) |
-| **YOLOv8** | n/s/m/l/x | 34.1% | [EdgeFirst/yolov8-seg](https://huggingface.co/EdgeFirst/yolov8-seg) |
-
----
-
-## Naming Convention
-
-| Component | Pattern | Example |
-|-----------|---------|---------|
-| HF Repo | `EdgeFirst/{version}-{task}` | `EdgeFirst/yolov8-det` |
-| ONNX Model | `{version}{size}-{task}-coco.onnx` | `yolov8n-det-coco.onnx` |
-| TFLite Model | `{version}{size}-{task}-coco.tflite` | `yolov8n-det-coco.tflite` |
-| i.MX 95 Model | `{version}{size}-{task}-coco.imx95.tflite` | `yolov8n-det-coco.imx95.tflite` |
-| Studio Project | `{Dataset} {Task}` | `COCO Detection` |
-| Studio Experiment | `{Version} {Task}` | `YOLOv8 Detection` |
-
-## Validation Pipeline
-
-| Stage | What | Where |
-|-------|------|-------|
-| **Reference** | ONNX FP32 and TFLite INT8 mAP on full COCO val2017 (5000 images) | EdgeFirst Studio (cloud) |
-| **On-Target** | Full dataset mAP + timing breakdown per device | Board farm (real hardware) |
-
-## Perception Architecture
-
-| Layer | Description |
-|-------|-------------|
-| **Foundation** | Hardware abstraction, video I/O, accelerated inference delegates |
-| **Zenoh** | Modular perception pipeline over Zenoh pub/sub |
-| **GStreamer** | Spatial perception elements for GStreamer / NNStreamer |
-| **ROS 2** | Native ROS 2 nodes extending Zenoh microservices *(Roadmap)* |
-
-## EdgeFirst Studio
-
-[**EdgeFirst Studio**](https://edgefirst.studio) is the MLOps platform that drives the entire model zoo pipeline. **Free tier available.**
-
-- Dataset management & AI-assisted annotation
-- Model training with automatic multi-format export and INT8 quantization
-- Reference and on-target validation with full metrics collection
-- CameraAdaptor integration for native sensor format training
-- Deploy trained models to edge devices via the [EdgeFirst Client](https://github.com/EdgeFirstAI/client) CLI
-
----
-
 Apache 2.0 · [Au-Zone Technologies Inc.](https://www.au-zone.com)