A family of fine-tuned YOLOv11 models for real-time detection of pallets (wooden skid + stacked products) in warehouse environments. Available in 2 YOLOv11 sizes — nano (2.6M params, edge-ready) and small (9.4M params, best accuracy). Optimized for foreground pallet identification in operational warehouse settings with forklifts, racks, and dynamic lighting.

Try It

→ Interactive demo on Hugging Face Spaces — upload a warehouse image and run detection with any variant in the browser.

Examples

Real detections from the validation set using the nano @ 640p variant:

Clean foreground pallet

Single foreground pallet detected at 0.86 confidence. Nano-640 is the edge workhorse — fast, clean, high-confidence on unambiguous shots.

Heavy motion blur

Same camera in motion — pallet detected at 0.58 confidence despite the entire frame being smeared. Lower confidence than a clean shot, as expected, but the detection holds.

Cluttered multi-pallet scene

16 of ~20 ground-truth pallets detected with confidence ranging 0.30–0.83 in a cluttered aisle. Real deployments should expect this kind of variable-confidence output in dense scenes; post-filter by your target threshold.

Model Variants

The nano variant is published at four resolutions covering everything from microcontroller-class edge hardware to high-resolution production cameras. The small variant is published at 640p and 1280p, the two resolutions where additional capacity materially helps accuracy.

320p Models (Ultra-Edge)

Model	Params	Size (MB)	Resolution	mAP@0.5	mAP@0.5:0.95	Precision	Recall	GPU (ms)	CPU (ms)	Best For
YOLOv11n-320	2.6M	~5	320	TBD	TBD	TBD	TBD	TBD	TBD	Ultra-edge: ESP32-S3, Coral Edge TPU, RPi Zero, hobby/embedded

640p Models (Standard Resolution)

Model	Params	Size (MB)	Resolution	mAP@0.5	mAP@0.5:0.95	Precision	Recall	GPU (ms)	CPU (ms)	Best For
YOLOv11n-640	2.6M	~5	640	0.674	0.571	0.658	0.614	~5	~25	Edge / real-time on low-power devices
YOLOv11s-640	9.4M	~19	640	0.592	0.485	0.599	0.574	~7	~45	Balanced speed/accuracy

960p Models (Production Middle)

Model	Params	Size (MB)	Resolution	mAP@0.5	mAP@0.5:0.95	Precision	Recall	GPU (ms)	CPU (ms)	Best For
YOLOv11n-960	2.6M	~5	960	TBD	TBD	TBD	TBD	TBD	TBD	Jetson Orin Nano, 1080p cameras downsampled — better small-object recall than 640 without 1280 latency

1280p Models (Native High Resolution)

Model	Params	Size (MB)	Resolution	mAP@0.5	mAP@0.5:0.95	Precision	Recall	GPU (ms)	CPU (ms)	Best For
YOLOv11n-1280	2.6M	~5	1280	0.666	0.565	0.645	0.610	~18	~95	Edge with high-res cameras
YOLOv11s-1280	9.4M	~19	1280	0.680	0.580	0.646	0.628	~25	~170	Balanced, small pallet detection

All variants share the same training data, augmentation pipeline, and hyperparameters. Medium, large, and extra-large variants were tested but showed no accuracy improvement over small with the current dataset, so only nano and small are published. Metrics for the 320p and 960p nano variants are marked TBD — training is in progress.

Model Description

This model detects complete pallet units (wooden skid base + all products stacked on top) in warehouse imagery. It was trained on real-world warehouse photos captured during normal operations, making it robust to common warehouse conditions: motion blur, variable lighting, partial occlusions by forklifts and personnel, and cluttered backgrounds.

Unlike generic object detection models, this model is specifically trained to:

Detect foreground pallets that are fully within the frame
Distinguish pallets from visually similar structures (ceiling rafters, doors, rack uprights)
Handle pallets stacked 2-high as separate detections per level
Work reliably with motion-blurred images from moving cameras

Intended Use

Warehouse automation: Real-time pallet counting and position tracking
Forklift guidance: Detecting pallets in the robot/forklift field of view
Inventory management: Automated pallet inventory from security or mounted cameras
3D warehouse mapping: Input to multi-view reconstruction pipelines for spatial pallet localization

Out of Scope

Empty pallet (wooden skid only) detection without products
Pallet type classification (EUR, GMA, block, stringer)
Damaged pallet assessment
Outdoor or non-warehouse environments

Training Details

Architecture

Variant	Base Model	Parameters	Input Resolutions	Classes	Framework
Nano	`yolo11n.pt`	~2.6M	320, 640, 960, 1280	1 (`pallet`)	Ultralytics 8.x
Small	`yolo11s.pt`	~9.4M	640, 1280	1 (`pallet`)	Ultralytics 8.x

The nano variant is trained at four resolutions (320, 640, 960, 1280) and the small variant at two (640, 1280), yielding 6 models total. All variants share the same architecture per size; only the training input resolution differs. Higher resolutions improve detection of small/distant pallets at the cost of slower inference.

Dataset

Split	Ratio	Description
Train	80%	Labeled warehouse images
Validation	15%	Held out for epoch-level evaluation
Test	5%	Held out for final evaluation

Training uses the full available dataset (no image cap). Exact counts depend on the number of labeled images at training time.

Labeling Pipeline: Images were auto-labeled using Qwen3.5-9B (natively multimodal vision-language model) with structured prompts to identify pallet bounding boxes, followed by human review of preview images. Negative examples (images with no pallets) are included as hard negatives.

Label Format: YOLO format (class_id, x_center, y_center, width, height) normalized 0-1.

Training Configuration

Hyperparameter	Value (640p)	Value (1280p)
Epochs	100 (early stopping, patience=20)	100 (early stopping, patience=20)
Batch Size	16	4 (auto-scaled)
Image Size	640	1280
Optimizer	SGD (Ultralytics default)	SGD (Ultralytics default)
Learning Rate	Auto-scaled	Auto-scaled
Augmentation	HSV jitter, horizontal flip, mosaic, scale	Same
Device	NVIDIA GPU (CUDA)	NVIDIA GPU (CUDA)

Data Augmentation Details:

hsv_h=0.015, hsv_s=0.7, hsv_v=0.4
degrees=10.0, translate=0.1, scale=0.5
fliplr=0.5, mosaic=1.0

Evaluation Results

Test Set Performance (All Variants)

Variant	mAP@0.5	mAP@0.5:0.95	Precision	Recall	GPU (ms)	CPU (ms)
Nano (n)	0.674	0.571	0.658	0.614	~5	~25
Small (s)	0.592	0.485	0.599	0.574	~7	~45

Benchmark Comparison

There is no established standard benchmark for warehouse pallet detection. The table below compares against results reported in published literature on similar (but not identical) datasets, to provide context for this model's performance.

Model	Dataset	Images	mAP@0.5	Precision	Recall	Source
This model (YOLOv11n)	EDITools Warehouse	Full dataset	0.572	0.600	0.563	This work
This model (YOLOv11s)	EDITools Warehouse	Full dataset	0.592	0.599	0.574	This work
NVIDIA SDG Pallet	Omniverse synthetic	~25,000	-	-	-	NVIDIA SDG Pallet Model
YOLOv8 (synthetic)	Unity synthetic	Synthetic	0.995	-	-	Pallet Detection From Synthetic Data (2025)
YOLOv8 (synthetic boost)	Synthetic + real	Custom	+69% stacked	-	-	Improving Pallet Detection Using Synthetic Data (2024)
YOLOv8	Custom warehouse	Custom	0.950	-	-	Semi-Autonomous Forklift (2025)
YOLOv11	Custom warehouse	Custom	-	0.93+	-	Semi-Autonomous Forklift (2025)
AM-Mask R-CNN	Complex warehouse	Custom	-	-	-	Enhanced Pallet Detection (2025)
Faster R-CNN	Industrial warehouse	1,344	0.89	-	-	IEEE Comparison (2020)
SSD	Industrial warehouse	1,344	0.85	-	-	IEEE Comparison (2020)
YOLOv4	Industrial warehouse	1,344	0.82	-	-	IEEE Comparison (2020)
YOLOv5 + ArUco	Custom + fiducial	Custom	-	0.995	-	Pallet Detection with YOLO + Fiducial (2023)
YOLOv8 + CBAM	Warehouse tracking	Custom	-	-	-	CBAM Pallet Tracking (2025)
YOLOX	Industrial	Custom	-	-	-	Digital Camera Pallet Detection

Notes on comparison:

No standard benchmark exists for warehouse pallet detection (unlike COCO or KITTI for general/driving OD). Each study uses its own private dataset, making direct comparison difficult.
The NVIDIA SDG model is the most production-ready alternative, trained on ~25K synthetic images via Omniverse, targeting pallet side-face centers/corners. It detects wood, metal, and plastic pallets but focuses on pallet pocket localization for forklift docking rather than full pallet unit detection.
The synthetic-data model (0.995 mAP) was evaluated on simple single-pallet scenes, not cluttered warehouses.
This model is the first dedicated pallet detection model published to Hugging Face Hub — no fine-tuned pallet model previously existed in the HF ecosystem.
This model is specifically optimized for foreground pallet detection in real operational environments.

Performance by Scenario

Scenario	Qualitative Performance
Single pallet, clear view	Excellent
Multiple pallets in row	Good - detects individual units
Pallet on forklift forks	Good - detects if mostly visible
Pallets on racks (background)	Limited - trained for foreground detection
Motion blur	Good - trained on real warehouse video frames
Low/mixed lighting	Good - augmented with HSV jitter

Usage

Quick Start

python

from ultralytics import YOLO

# Pick a repo in the form: EFFGRP/yolov11{size}-warehouse-pallets-{resolution}
#   size:       "n" = nano (fastest, edge)      "s" = small (best accuracy)
#   resolution: 320, 640, 960, or 1280 for nano; 640 or 1280 for small
# See "Choosing a Variant" below for recommended combinations.
model = YOLO("EFFGRP/yolov11n-warehouse-pallets-640")

# Run inference on an image
results = model.predict("warehouse_photo.jpg", conf=0.25)

# Process results
for result in results:
    for box in result.boxes:
        cls_id = int(box.cls[0])
        confidence = float(box.conf[0])
        x1, y1, x2, y2 = box.xyxy[0].tolist()
        print(f"Pallet detected: conf={confidence:.2f}, bbox=({x1:.0f},{y1:.0f},{x2:.0f},{y2:.0f})")

Choosing a Variant

python

from ultralytics import YOLO

# Ultra-edge (ESP32-S3, Coral Edge TPU, RPi Zero, hobby boards)
model = YOLO("EFFGRP/yolov11n-warehouse-pallets-320")

# Edge deployment (Jetson Nano, RPi 4, mobile) — nano at 640p
model = YOLO("EFFGRP/yolov11n-warehouse-pallets-640")

# General warehouse camera system — small at 640p as the best balanced tradeoff
model = YOLO("EFFGRP/yolov11s-warehouse-pallets-640")

# Production middle-ground (Jetson Orin Nano, 1080p cameras) — nano at 960p
model = YOLO("EFFGRP/yolov11n-warehouse-pallets-960")

# Edge with high-res cameras — nano at 1280p
model = YOLO("EFFGRP/yolov11n-warehouse-pallets-1280")

# High-res camera with small/distant pallets — small at 1280p (best accuracy)
model = YOLO("EFFGRP/yolov11s-warehouse-pallets-1280")

Batch Processing

python

from ultralytics import YOLO
from pathlib import Path

# Small @ 1280p for highest-accuracy offline batch processing
model = YOLO("EFFGRP/yolov11s-warehouse-pallets-1280")

# Process a directory of images
image_dir = Path("warehouse_photos/")
results = model.predict(
    source=str(image_dir),
    conf=0.25,
    save=True,           # Save annotated images
    save_txt=True,       # Save YOLO-format labels
    project="output/",
    name="pallet_detections"
)

Export to ONNX for Edge Deployment

python

from ultralytics import YOLO

# Export nano @ 640p for edge deployment (swap the repo + imgsz for other variants)
model = YOLO("EFFGRP/yolov11n-warehouse-pallets-640")
model.export(format="onnx", imgsz=640, simplify=True)
# Produces a .onnx file alongside the .pt for deployment on edge devices

Integration with Warehouse Systems

This model is designed to work as part of a larger warehouse automation pipeline. Example integration with a Redis message queue:

python

import json
import redis
from ultralytics import YOLO

model = YOLO("EFFGRP/yolov11s-warehouse-pallets-1280")
r = redis.Redis()

def process_frame(image_path):
    results = model.predict(image_path, conf=0.25, verbose=False)
    detections = []
    for box in results[0].boxes:
        detections.append({
            "confidence": float(box.conf[0]),
            "bbox": box.xyxy[0].tolist(),
            "center": box.xywh[0][:2].tolist(),
        })
    r.lpush("pallet_detections", json.dumps({
        "image": str(image_path),
        "count": len(detections),
        "detections": detections,
    }))

Model Files

Each variant repository contains:

File	Description
`whole_pallet_{size}_{resolution}.pt`	PyTorch model weights
`README.md`	This model card
`benchmark_results.json`	Structured benchmark comparison data

Where {size} is one of: n (nano), s (small). {resolution} is 320, 640, 960, or 1280 for nano; 640 or 1280 for small.

Limitations

Single class: Only detects "pallet" (complete unit). Does not distinguish pallet types, contents, or conditions.
Foreground bias: Trained primarily on foreground pallets. Background or distant pallets may be missed.
Domain specific: Trained on a single warehouse environment. Performance may degrade in visually different warehouses (outdoor yards, cold storage, etc.). Fine-tuning on your own data is recommended.
Partial occlusion: Pallets significantly occluded by other pallets (not people or forklifts) are intentionally excluded from training labels.
Dataset size: Performance improves with more training data. Fine-tuning on your own warehouse data is recommended.

Ethical Considerations

This model is intended for warehouse automation and logistics optimization. It does not process personal biometric data. However, warehouse images may incidentally contain workers - this model does not detect or track people, but users should ensure compliance with workplace surveillance regulations when deploying camera systems.

Citation

If you use this model in your research, please cite:

bibtex

@misc{effgrp-warehouse-pallets-2026,
  title={YOLOv11 Warehouse Pallet Detector},
  author={EFFGRP},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/EFFGRP/yolov11s-warehouse-pallets-1280}
}

Landscape: Why This Model Exists

As of March 2026, no dedicated pallet detection model exists on Hugging Face. The HF Hub has ~21 models tagged "logistics" but none are pallet-specific. The closest alternatives are:

NVIDIA SDG Pallet Model (GitHub) — trained on ~25K synthetic images via Omniverse, focuses on pallet side-face and pocket localization for autonomous forklift docking. Production-ready but targets a different task (pocket detection vs. full pallet unit detection).
Roboflow Universe (pallets) — community datasets with 1,755+ images and some pre-trained models, but fragmented across projects with inconsistent annotation guidelines.
Academic models — published in papers but weights/code not publicly shared on model hubs.

This model fills the gap as a ready-to-use, real-world-trained pallet unit detector for the Hugging Face ecosystem.

Related Work

NVIDIA SDG Pallet Model (2024) - Synthetic data + Omniverse for pallet pocket localization
NVIDIA Pallet Detection Blog - OpenUSD synthetic data pipeline
Pallet Detection and Localisation From Synthetic Data (2025) - Unity domain randomization + YOLOv8
Improving Pallet Detection Using Synthetic Data (2024) - 69% mAP improvement on stacked pallets with synthetic augmentation
Learning-Based Vision Systems for Semi-Autonomous Forklift (2025) - YOLOv8/v11 comparison for forklift pallet detection
Enhanced Pallet Detection: AM-Mask R-CNN (2025) - Attention-enhanced Mask R-CNN for complex warehouses
A Comparison of Deep Learning Models for Pallet Detection (IEEE, 2020) - Benchmark comparing Faster R-CNN, SSD, YOLOv4 on 1,344 images
Pallet Detection and Distance Estimation with YOLO + Fiducial Markers (2023) - YOLOv5 + ArUco for distance estimation
Amazon ARMBench (2023) - Large-scale warehouse manipulation benchmark (450K+ labels)
CBAM-Enhanced Pallet Tracking (2025) - YOLOv8 + attention + DeepSORT for tracking
Roboflow Pallet Datasets - Community-contributed pallet detection datasets (1,755+ images)
EmaroLab PDT - Pallet Detection and Tracking with Faster R-CNN + 2D LRF

amir-salar/yolov11n-warehouse-pallets-640

YOLOv11 Warehouse Pallet Detector

Try It

Examples

Clean foreground pallet

Heavy motion blur

Cluttered multi-pallet scene

Model Variants

320p Models (Ultra-Edge)

640p Models (Standard Resolution)

960p Models (Production Middle)

1280p Models (Native High Resolution)

Model Description

Intended Use

Out of Scope

Training Details

Architecture

Dataset

Training Configuration

Evaluation Results

Test Set Performance (All Variants)

Benchmark Comparison

Performance by Scenario

Usage

Quick Start

Choosing a Variant

Batch Processing

Export to ONNX for Edge Deployment

Integration with Warehouse Systems

Model Files

Limitations

Ethical Considerations

Citation

Landscape: Why This Model Exists

Related Work

amir-salar/
yolov11n-warehouse-pallets-640