chistopat/sku110k-yolo11-object-detector

SKU-110K YOLO11 Object Detector 📦

Two lightweight Ultralytics YOLO11 checkpoints trained on SKU-110K at imgsz=640.

The goal is simple: detect product-like objects on shelves and in real-world photos. It is a handy baseline for visual search prototypes, shelf analysis, dense object detection experiments, and other R&D work where a generic object detector is useful.

Try it in the browser: free Gradio demo 🚀

The models predict one class:

0: object

This is not a product-category classifier or a brand-recognition model. It just finds generic retail/product objects. That makes it flexible, but also means you should test it on your own images before using it in any real pipeline.

What's Inside ✨

model	PyTorch	ONNX	note
YOLO11n 640	weights/sku110k-yolo11-n640.pt	weights/sku110k-yolo11-n640.onnx	small, fast, easy to try
YOLO11s 640	weights/sku110k-yolo11-s640.pt	weights/sku110k-yolo11-s640.onnx	stronger metrics, still lightweight

The ONNX exports are fixed-shape exports with input shape [1, 3, 640, 640].

Basic Metrics 📊

Final test evaluation was run on the SKU-110K test split. Ultralytics skipped one corrupt test image, so the reported test run used 2,935 images and 431,419 instances.

model	precision	recall	mAP50	mAP50-95	inference
YOLO11n 640	0.896	0.838	0.906	0.550	0.9 ms/img
YOLO11s 640	0.913	0.867	0.927	0.577	4.3 ms/img

Validation checkpoints:

model	best epoch	val precision	val recall	val mAP50	val mAP50-95
YOLO11n 640	31	0.8962	0.8380	0.9023	0.5440
YOLO11s 640	41	0.9142	0.8529	0.8969	0.5600

Qualitative Example 🖼️

This real-world shelf photo was annotated with the published YOLO11s SKU-110K checkpoint. It is included for visual context only. It is not part of the SKU-110K benchmark and should not be read as category-specific validation for these weights.

Quick Start 🚀

from ultralytics import YOLO

model = YOLO("weights/sku110k-yolo11-s640.pt")
results = model.predict("image.jpg", imgsz=640, conf=0.25)

For the smaller model:

from ultralytics import YOLO

model = YOLO("weights/sku110k-yolo11-n640.pt")
results = model.predict("image.jpg", imgsz=640, conf=0.25)

R&D Notes ⚠️

These weights are research and development artifacts. They are useful for experiments, demos, and baseline comparisons, but they have not been validated for production use, safety-critical use, or automated business decisions.

The training dataset is SKU-110K, a dense retail shelf object dataset. Expect the model to detect many product-like things, and expect misses or extra boxes on photos that look very different from retail shelves. Use the included example as a quick visual check, then validate on your own target data before deployment.

The weights are derived from SKU-110K training data. Keep publication and downstream use aligned with the upstream SKU-110K dataset terms; this model card does not claim a more permissive license than the source data allows.

Files 📁

• weights/sku110k-yolo11-n640.pt: YOLO11n PyTorch checkpoint
• weights/sku110k-yolo11-n640.onnx: YOLO11n ONNX export
• weights/sku110k-yolo11-s640.pt: YOLO11s PyTorch checkpoint
• weights/sku110k-yolo11-s640.onnx: YOLO11s ONNX export
• examples/: qualitative example image generated with the published YOLO11s checkpoint
• metrics.json: structured metrics and dataset summary
• checksums.sha256: SHA-256 checksums for published files

Verify local files with:

shasum -a 256 -c checksums.sha256