README.md

metadata

title: EdgeFirst Model Zoo
emoji: 🔬
colorFrom: indigo
colorTo: red
sdk: static
pinned: true
license: cc-by-nc-4.0
short_description: Multi-platform model zoo validated on real edge hardware
thumbnail: https://huggingface.co/spaces/EdgeFirst/Models/resolve/main/social-card.png

EdgeFirst Model Zoo

This Space hosts the EdgeFirst Model Zoo landing page. The visual interface is rendered from index.html.

Each model family lives in its own HuggingFace repo containing all size variants (nano through x-large) and platform-specific compiled formats. Models are trained and validated on EdgeFirst Studio, then published here.

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Model Repositories

Detection

Repo	Model	Sizes	Nano mAP@0.5
EdgeFirst/yolo26-det	YOLO26	n/s/m	55.0%
EdgeFirst/yolo11-det	YOLO11	n/s/m	53.1%
EdgeFirst/yolov8-det	YOLOv8	n/s/m	50.5%
EdgeFirst/yolov5-det	YOLOv5	n/s/m	47.8%

Segmentation

Repo	Model	Sizes	Nano Mask mAP
EdgeFirst/yolo26-seg	YOLO26	n/s/m	32.7%
EdgeFirst/yolo11-seg	YOLO11	n/s	30.2%
EdgeFirst/yolov8-seg	YOLOv8	n/s/m	28.7%

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Repo Structure

Each model repo follows a consistent layout with platform folders:

EdgeFirst/yolov8-det/
├── README.md                                # Model card
├── onnx/
│   ├── yolov8n-det-fp32.onnx
│   └── ...
├── tflite/
│   ├── yolov8n-det-int8.tflite              # Default (logical split-decoder)
│   ├── yolov8n-det-int8-smart.tflite        # Smart variant
│   └── ...
├── imx95/
│   ├── yolov8n-det-int8.imx95.tflite
│   └── ...
├── hailo/
│   ├── yolov8n-det-int8.hailo8l.hef
│   └── ...
└── jetson/
    ├── yolov8n-det-fp16.orin-nano.engine
    └── ...

Naming Convention

Pattern: {version}{size}-{task}-{precision}[-{variant}][.{platform}].{ext}

Component	Description	Examples
{version}{size}	Model family + variant	yolov8n, yolo11s, dfine-n
-{task}	Task suffix	-det, -seg, -semseg, -depth
-{precision}	Weight precision	-fp32, -fp16, -int8
-{variant}	Decoder variant (optional)	-smart
.{platform}	Deployment target (optional)	.imx95, .ara240, .hailo8l, .orin-nano
.{ext}	File format	.onnx, .tflite, .dvm, .hef, .engine

Decoder variants: No suffix = default for that format (logical split-decoder for INT8, combined for ONNX/float). -smart = multi-scale split-decoder offering better accuracy at higher compute cost.

Examples:

Description	Filename
ONNX FP32 (reference)	yolov8n-det-fp32.onnx
Generic INT8 TFLite	yolov8n-det-int8.tflite
Smart variant TFLite	yolov8n-det-int8-smart.tflite
NXP i.MX 95 TFLite	yolov8n-det-int8.imx95.tflite
Smart NXP i.MX 95	yolov8n-seg-int8-smart.imx95.tflite
Hailo-8L HEF	yolov8n-det-int8.hailo8l.hef
Jetson TensorRT FP16	yolov8n-det-fp16.orin-nano.engine

Supported Hardware

• Linux x86_64 — ONNX Runtime CUDA / CPU (FP32 reference)
• Linux aarch64 — ONNX Runtime / TFLite (ARM64 generic Linux)
• Apple macOS — ONNX Runtime + CoreML ANE / GPU / CPU (FP16)
• NXP i.MX 8M Plus — 2.3 TOPS, TFLite INT8
• NXP i.MX 95 — 2.0 TOPS, eIQ Neutron TFLite (YOLOv5 / YOLOv8 only; YOLO11 / YOLO26 not yet supported on eIQ Neutron)
• NXP Ara240 — 40 eTOPS, .DVM
• RPi5 + Hailo-8L — 13 TOPS, HailoRT HEF
• NVIDIA Jetson Orin — 67–157 TOPS, TensorRT

Validation Pipeline

Every artifact in the Model Zoo is measured on the same dataset on the same hardware users deploy on. Accuracy numbers and per-stage timing are produced by the same pipeline that runs the deployed model — there is no "benchmark configuration" separate from production.

End-to-end flow

Each training session produces a single set of weights in EdgeFirst Studio. The export pipeline emits ONNX FP32, INT8 TFLite, and platform-specific compiled formats (NXP i.MX 95 Neutron, NXP Ara240 .DVM, Hailo HEF, Jetson TensorRT). Every output is paired with an on-target validation that captures both accuracy (COCO mAP) and full-pipeline timing. The ONNX FP32 run from each training session is the reference baseline; quantization and runtime loss are measured relative to it.

EdgeFirst Profiler

The on-target validation agent. Given a model and dataset, it runs full inference on the target device, captures per-image predictions in EdgeFirst Arrow/Parquet, and emits a Perfetto trace alongside. Loads each runtime through its native delegate — VX Delegate on NXP i.MX 8M Plus, eIQ Neutron on NXP i.MX 95, NXP Ara SDK on Ara240, HailoRT on RPi5 + Hailo, TensorRT on Jetson — so timing reflects deployed-application reality.

EdgeFirst Validator

The off-target post-processor. Consumes predictions + Perfetto trace, computes the 12-metric COCO accuracy tuple via pycocotools (or lvis-api for large-vocabulary datasets), and rebuilds per-stage timing summaries from the trace. Results attach to the Studio validation session as a structured YAML payload — the same payload this Model Zoo reads.

EdgeFirst HAL

The EdgeFirst Hardware Abstraction Layer provides hardware-accelerated primitives used at both validation and deployment time: letterbox resize, color-space conversion, normalization, layout conversion, YOLO/ModelPack post-decode, NMS. HAL automatically selects DMA-BUF, OpenGL ES, NXP G2D, or CPU paths depending on the platform. Apache 2.0; Rust + Python + C surfaces.

Latency and pipelined throughput

Two timing surfaces per validation:

• timing.inline — per-image preprocess_ms / inference_ms / postprocess_ms with min / mean / median / p95 / p99 / max. The universal contract every producer fills.
• timing.trace — full per-stage breakdown from the Perfetto trace (typically 25–33 stages), plus end-to-end FPS distribution.

Throughput exceeds the sum of stage latencies because the runtime pipelines I/O, preprocessing, NPU inference, and decode across frames. The Model Zoo headlines trace.fps.median as the throughput number, not the derived 1000 / (preprocess + inference + postprocess). Example: YOLOv5n on NXP i.MX 95 Neutron has per-stage means 21.7 + 12.2 + 15.8 ms (naive ≈ 20 FPS) but pipelined throughput of 56 FPS median.

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Validation results & card data: CC BY-NC 4.0 · YOLO model weights: © Ultralytics Inc. (AGPL-3.0) · © 2026 Au-Zone Technologies

_{NXP^®, i.MX, eIQ^®, Neutron, and Ara240 are trademarks or products of NXP Semiconductors. Hailo is a trademark of Hailo Technologies Ltd. Jetson is a trademark of NVIDIA Corporation. All other trademarks are the property of their respective owners.}