Fine-Tune YOLO11 Object Detection Models on FlexAI

This blueprint demonstrates how to train, validate, and deploy computer vision models using Ultralytics YOLO11 on FlexAI. You’ll learn how to fine-tune YOLO models for object detection, instance segmentation, and pose estimation tasks, then deploy them as production-ready inference endpoints. YOLO (You Only Look Once) is one of the most popular real-time object detection frameworks, and YOLO11 brings significant improvements in accuracy and efficiency across multiple computer vision tasks.

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Overview

This blueprint covers:

Object Detection Training: Fine-tune YOLO11 on custom datasets for object detection
Instance Segmentation: Train models to detect and segment objects at the pixel level
Pose Estimation: Train models for human pose detection and keypoint tracking
Model Validation: Evaluate model performance with comprehensive metrics
Model Export: Export to optimized formats (ONNX, TensorRT) for deployment
Inference Deployment: Deploy trained models as FlexAI inference endpoints

All YOLO tasks use the same CLI interface with different task modes (detect, segment, pose, track, etc.). This guide demonstrates the core workflows that apply across all computer vision tasks.

Prepare the Dataset

YOLO models require datasets in a specific format. We’ll use the COCO8 dataset (a small subset of COCO) for this example, but you can easily adapt this to your own custom dataset.

Option A: Use COCO8 Dataset (Quick Start)

The COCO8 dataset will be automatically downloaded by Ultralytics during training. No manual download is required.

Option B: Prepare Your Own Custom Dataset

For custom datasets, follow the YOLO format structure:

dataset/
├── images/
│   ├── train/
│   │   ├── image1.jpg
│   │   └── image2.jpg
│   └── val/
│       ├── image3.jpg
│       └── image4.jpg
└── labels/
    ├── train/
    │   ├── image1.txt
    │   └── image2.txt
    └── val/
        ├── image3.txt
        └── image4.txt

Each label file contains annotations in YOLO format (one object per line):

<class_id> <x_center> <y_center> <width> <height>

All coordinates must be normalized to the range [0, 1].Create a data configuration file (data.yaml):

path: /input
train: /input/images/train
val: /input/images/val

names:
  0: person
  1: bicycle
  2: car
  # ... add your classes

Both mapping and list formats are supported for class names. You can also use: names: ["person", "bicycle", "car"]

Use spaces (not tabs) for indentation in data.yaml. Tab characters will cause silent parsing failures.

YOLO accepts both relative and absolute paths, but using absolute paths (/input/...) reduces ambiguity inside FlexAI jobs.If your dataset uses a different annotation format (COCO JSON, Pascal VOC, etc.), convert it to YOLO format before uploading. Refer to the Ultralytics Data Format documentation for conversion guidance.

Upload Custom Dataset to FlexAI

Once your dataset is prepared, upload it to FlexAI:

flexai dataset push yolo-custom-dataset --file path/to/your-dataset

When you use the dataset in a training job with --dataset yolo-custom-dataset, FlexAI will mount the dataset contents directly at /input/ in your training environment. All dataset contents are mounted under /input, preserving their original folder structure.This means:

If your dataset structure is dataset/images/train/..., it will be accessible at /input/images/train/...
Your data.yaml should be at /input/data.yaml
The path field in your data.yaml should be /input as shown in the example above

Ensure these paths are reflected in your data.yaml before starting a training job.

Train an Object Detection Model

Train a YOLO11 model for object detection. We’ll start with the nano model (YOLO11n) which is fast and efficient.

Training on COCO8 Dataset

flexai training run yolo11-detection-coco8 \
  --accels 4 --nodes 1 \
  --repository-url https://github.com/flexaihq/blueprints \
  --requirements-path code/ultralytics/requirements.txt \
  -- yolo detect train \
    data=coco8.yaml \
    model=yolo11n.pt \
    epochs=100 \
    imgsz=640 \
    batch=16 \
    device=0,1,2,3 \
    project=/output-checkpoint \
    name=yolo11n-coco8 \
    patience=50 \
    save=True \
    val=True

--accels specifies the number of GPUs to allocate (e.g., --accels 4 = 4 GPUs of the chosen accelerator type).

Training on Custom Dataset

flexai training run yolo11-detection-custom \
  --accels 4 --nodes 1 \
  --repository-url https://github.com/flexaihq/blueprints \
  --dataset yolo-custom-dataset \
  --requirements-path code/ultralytics/requirements.txt \
  -- yolo detect train \
    data=/input/data.yaml \
    model=yolo11n.pt \
    epochs=100 \
    imgsz=640 \
    batch=16 \
    device=0,1,2,3 \
    project=/output-checkpoint \
    name=yolo11n-custom \
    save=True

Training with Larger Models

For better accuracy, use larger YOLO variants. Adjust batch size based on model size:YOLO11s (Small):

flexai training run yolo11s-detection \
  --accels 4 --nodes 1 \
  --repository-url https://github.com/flexaihq/blueprints \
  --requirements-path code/ultralytics/requirements.txt \
  -- yolo detect train \
    data=coco8.yaml \
    model=yolo11s.pt \
    epochs=100 \
    imgsz=640 \
    batch=16 \
    device=0,1,2,3 \
    project=/output-checkpoint

YOLO11m (Medium):

flexai training run yolo11m-detection \
  --accels 4 --nodes 1 \
  --repository-url https://github.com/flexaihq/blueprints \
  --requirements-path code/ultralytics/requirements.txt \
  -- yolo detect train \
    data=coco8.yaml \
    model=yolo11m.pt \
    epochs=100 \
    imgsz=640 \
    batch=8 \
    device=0,1,2,3 \
    project=/output-checkpoint

YOLO11l (Large):

flexai training run yolo11l-detection \
  --accels 8 --nodes 1 \
  --repository-url https://github.com/flexaihq/blueprints \
  --requirements-path code/ultralytics/requirements.txt \
  -- yolo detect train \
    data=coco8.yaml \
    model=yolo11l.pt \
    epochs=100 \
    imgsz=640 \
    batch=4 \
    device=0,1,2,3,4,5,6,7 \
    project=/output-checkpoint

Train an Instance Segmentation Model

Instance segmentation detects objects and generates pixel-level masks for each instance.

flexai training run yolo11-segmentation \
  --accels 4 --nodes 1 \
  --repository-url https://github.com/flexaihq/blueprints \
  --requirements-path code/ultralytics/requirements.txt \
  -- yolo segment train \
    data=coco8-seg.yaml \
    model=yolo11n-seg.pt \
    epochs=100 \
    imgsz=640 \
    batch=16 \
    device=0,1,2,3 \
    project=/output-checkpoint \
    name=yolo11n-segmentation \
    save=True

For custom segmentation datasets, ensure your labels include polygon annotations in YOLO segmentation format.

Train a Pose Estimation Model

For human pose estimation tasks with keypoint detection:

flexai training run yolo11-pose \
  --accels 4 --nodes 1 \
  --repository-url https://github.com/flexaihq/blueprints \
  --requirements-path code/ultralytics/requirements.txt \
  -- yolo pose train \
    data=coco8-pose.yaml \
    model=yolo11n-pose.pt \
    epochs=100 \
    imgsz=640 \
    batch=16 \
    device=0,1,2,3 \
    project=/output-checkpoint

Validate the Model

After training, validate your model’s performance on the validation dataset.

List Training Checkpoints

flexai training checkpoints yolo11-detection-coco8

Run Validation

For validation with the COCO8 dataset (will be auto-downloaded):

flexai training run yolo11-validation \
  --accels 1 --nodes 1 \
  --repository-url https://github.com/flexaihq/blueprints \
  --checkpoint <CHECKPOINT_ID> \
  --requirements-path code/ultralytics/requirements.txt \
  -- yolo detect val \
    model=/checkpoint/weights/best.pt \
    data=coco8.yaml \
    imgsz=640 \
    batch=16 \
    project=/output-checkpoint/validation

For validation with a custom dataset:

flexai training run yolo11-validation-custom \
  --accels 1 --nodes 1 \
  --repository-url https://github.com/flexaihq/blueprints \
  --checkpoint <CHECKPOINT_ID> \
  --dataset yolo-custom-dataset \
  --requirements-path code/ultralytics/requirements.txt \
  -- yolo detect val \
    model=/checkpoint/weights/best.pt \
    data=/input/data.yaml \
    imgsz=640 \
    batch=16 \
    project=/output-checkpoint/validation

Understanding Validation Metrics

YOLO provides comprehensive metrics:

mAP50: Mean Average Precision at IoU threshold 0.5
mAP50-95: Mean Average Precision averaged across IoU thresholds 0.5-0.95
Precision: Ratio of true positive detections
Recall: Ratio of detected ground truth objects
F1-Score: Harmonic mean of precision and recall

These metrics are saved to the results directory after each training run.

Export the Model

Export your trained model to various formats for optimized deployment.

Download Checkpoint Locally

First, download the best checkpoint to your local machine:

flexai checkpoint fetch "<CHECKPOINT_ID>" --destination ./yolo11-checkpoint

Export to ONNX

ONNX format is widely supported and optimized for cross-platform inference:

flexai training run yolo11-export-onnx \
  --accels 1 --nodes 1 \
  --repository-url https://github.com/flexaihq/blueprints \
  --checkpoint <CHECKPOINT_ID> \
  --requirements-path code/ultralytics/requirements.txt \
  -- yolo export \
    model=/checkpoint/weights/best.pt \
    format=onnx \
    imgsz=640 \
    simplify=True \
    dynamic=False

Export to TensorRT

For NVIDIA GPU deployment with maximum performance:

flexai training run yolo11-export-tensorrt \
  --accels 1 --nodes 1 \
  --repository-url https://github.com/flexaihq/blueprints \
  --checkpoint <CHECKPOINT_ID> \
  --requirements-path code/ultralytics/requirements.txt \
  -- yolo export \
    model=/checkpoint/weights/best.pt \
    format=engine \
    imgsz=640 \
    half=True \
    device=0

Available Export Formats

Format	format Argument	Use Case
PyTorch	`torchscript`	General PyTorch deployment
ONNX	`onnx`	Cross-platform inference
TensorRT	`engine`	NVIDIA GPU optimization
CoreML	`coreml`	Apple devices (iOS/macOS)
TFLite	`tflite`	Mobile and embedded devices
OpenVINO	`openvino`	Intel hardware acceleration
NCNN	`ncnn`	Mobile deployment

See the Ultralytics Export documentation for more formats and options.

Run Inference on Your Trained Model

After training, you can run inference on images or videos using your trained model.

Run Inference as a Training Job

FlexAI’s managed inference endpoints currently support vLLM only. For YOLO models, use a training job to execute predictions directly:

flexai training run yolo11-predict \
  --accels 1 --nodes 1 \
  --repository-url https://github.com/flexaihq/blueprints \
  --checkpoint <CHECKPOINT_ID> \
  --requirements-path code/ultralytics/requirements.txt \
  -- yolo detect predict \
    model=/checkpoint/weights/best.pt \
    source=https://ultralytics.com/images/bus.jpg \
    conf=0.25 \
    iou=0.45 \
    save=True \
    project=/output-checkpoint/predictions

The predictions and annotated images will be saved in the job’s output directory.

Run Batch Predictions

To run inference on a directory of images, first upload them as a dataset:

flexai dataset push test-images --file path/to/images/

Then run prediction:

flexai training run yolo11-predict-batch \
  --accels 1 --nodes 1 \
  --repository-url https://github.com/flexaihq/blueprints \
  --checkpoint <CHECKPOINT_ID> \
  --dataset test-images \
  --requirements-path code/ultralytics/requirements.txt \
  -- yolo detect predict \
    model=/checkpoint/weights/best.pt \
    source=/input \
    conf=0.25 \
    save=True \
    project=/output-checkpoint/predictions

Download Prediction Results

After the prediction job completes, download the results:

flexai training checkpoints yolo11-predict

flexai checkpoint fetch "<PREDICTION_CHECKPOINT_ID>" --destination ./predictions

Quick Local Testing

Test your trained model locally before deploying to production.

You can test your model locally after downloading the checkpoint:

# Download the checkpoint
flexai checkpoint fetch "<CHECKPOINT_ID>" --destination ./yolo11-checkpoint

# Run inference locally
yolo detect predict \
  model=./yolo11-checkpoint/weights/best.pt \
  source=path/to/image.jpg \
  conf=0.25 \
  save=True

Results will be saved to runs/detect/predict/.

Monitoring Training Progress

Check Training Status

flexai training inspect yolo11-detection-coco8

View Training Logs

flexai training logs yolo11-detection-coco8

Training Observability with TensorBoard

Ultralytics automatically logs training metrics. Access FlexAI’s hosted TensorBoard instance to track:

Training and validation loss curves
mAP (mean Average Precision) metrics
Precision and recall curves
Learning rate schedules

Weights & Biases Integration

For advanced monitoring, integrate with Weights & Biases by adding environment variables:

flexai training run yolo11-detection-wandb \
  --accels 4 --nodes 1 \
  --repository-url https://github.com/flexaihq/blueprints \
  --secret WANDB_API_KEY=<WANDB_API_KEY_SECRET_NAME> \
  --env WANDB_PROJECT=yolo11-experiments \
  --requirements-path code/ultralytics/requirements.txt \
  -- yolo detect train \
    data=coco8.yaml \
    model=yolo11n.pt \
    epochs=100 \
    project=/output-checkpoint

Advanced Use Cases

Object Tracking

YOLO11 supports multi-object tracking in videos. First upload your video as a dataset:

flexai dataset push test-video --file path/to/video.mp4

Then run tracking:

flexai training run yolo11-tracking \
  --accels 1 --nodes 1 \
  --repository-url https://github.com/flexaihq/blueprints \
  --checkpoint <CHECKPOINT_ID> \
  --dataset test-video \
  --requirements-path code/ultralytics/requirements.txt \
  -- yolo track \
    model=/checkpoint/weights/best.pt \
    source=/input/video.mp4 \
    conf=0.25 \
    iou=0.45 \
    save=True \
    project=/output-checkpoint/tracking

Model Benchmarking

Compare model performance across different formats and hardware:

flexai training run yolo11-benchmark \
  --accels 1 --nodes 1 \
  --repository-url https://github.com/flexaihq/blueprints \
  --checkpoint <CHECKPOINT_ID> \
  --requirements-path code/ultralytics/requirements.txt \
  -- yolo benchmark \
    model=/checkpoint/weights/best.pt \
    data=coco8.yaml \
    imgsz=640

This will benchmark:

PyTorch inference speed
ONNX inference speed
TensorRT inference speed (if available)
Model accuracy metrics

Hyperparameter Tuning

Use Ultralytics’ built-in hyperparameter tuning:

flexai training run yolo11-tune \
  --accels 4 --nodes 1 \
  --repository-url https://github.com/flexaihq/blueprints \
  --requirements-path code/ultralytics/requirements.txt \
  -- yolo detect tune \
    data=coco8.yaml \
    model=yolo11n.pt \
    epochs=30 \
    iterations=300 \
    optimizer=AdamW

Expected Results

Detection Performance (YOLO11n on COCO8)

mAP50: typically 0.45–0.55
mAP50-95: typically 0.30–0.40
Inference Speed: 1.5–2.5 ms/image (H100 TensorRT)
Model Size: ~3 MB

Segmentation Performance (YOLO11n-seg on COCO8-seg)

Box mAP50: typically 0.45–0.55
Mask mAP50: typically 0.40–0.50
Inference Speed: 2–3 ms/image (H100 TensorRT)

Training Time

YOLO11n on COCO8: ~5–10 minutes (4 × H100, 100 epochs).
YOLO11s on COCO8: ~10–15 minutes (4 × H100, 100 epochs).
YOLO11m on full COCO: ~4–6 hours (4 × H100, 100 epochs).

Technical Details

Recommended Resource Configuration

Model	GPUs	Batch Size	Memory	Training Time (100 epochs, COCO8)
YOLO11n	1-4 × H100	16-32	8GB+	5-10 min
YOLO11s	2-4 × H100	8-16	12GB+	10-15 min
YOLO11m	4 × H100	4-8	16GB+	15-20 min
YOLO11l	4-8 × H100	2-4	24GB+	20-30 min

Key Training Parameters

Image Size (imgsz):

Standard: 640×640
Small objects: 1280×1280 (slower but better detection)
Real-time applications: 320×320 or 416×416 (faster inference)

Batch Size (batch):

Larger batches generally lead to better convergence
Adjust based on GPU memory: YOLO11n (16-32), YOLO11s (8-16), YOLO11m (4-8)

Epochs:

Small datasets (< 1000 images): 100-150 epochs
Medium datasets (1000-10000): 50-100 epochs
Large datasets (> 10000): 30-50 epochs

Early Stopping (patience):

Stops training if no improvement for N epochs
Recommended: 50 epochs for COCO8, 30 epochs for larger datasets

Data Augmentation:

Enabled by default with optimized settings
Includes mosaic, mixup, HSV augmentation, and geometric transforms

Multi-GPU Training

FlexAI automatically enables distributed training when multiple GPUs are requested:

flexai training run yolo11-multi-gpu \
  --accels 8 --nodes 1 \
  --repository-url https://github.com/flexaihq/blueprints \
  --requirements-path code/ultralytics/requirements.txt \
  -- yolo detect train \
    data=coco8.yaml \
    model=yolo11n.pt \
    epochs=100 \
    batch=32 \
    device=0,1,2,3,4,5,6,7 \
    project=/output-checkpoint

Transfer Learning

Continue training from a previous checkpoint:

flexai training run yolo11-transfer-learning \
  --accels 4 --nodes 1 \
  --repository-url https://github.com/flexaihq/blueprints \
  --checkpoint <PREVIOUS_CHECKPOINT_ID> \
  --requirements-path code/ultralytics/requirements.txt \
  -- yolo detect train \
    data=coco8.yaml \
    model=/checkpoint/weights/best.pt \
    epochs=50 \
    batch=16 \
    project=/output-checkpoint

Troubleshooting

Common Issues

Fixing Training Job Failures:

# Check FlexAI authentication
flexai auth status

# Verify dataset upload
flexai dataset list

Fixing Out-of-Memory Errors:

Reduce batch size: batch=8 or batch=4
Use smaller image size: imgsz=416
Try a smaller model variant: YOLO11n instead of YOLO11s/m/l

Fixing Low mAP / Poor Performance:

Train for more epochs: epochs=200 or epochs=300
Increase image size: imgsz=1280
Use a larger model: YOLO11s/m instead of YOLO11n
Check dataset quality and annotation accuracy
Ensure balanced class distribution

Fixing Dataset Format Errors:

Verify YOLO format: <class_id> <x_center> <y_center> <width> <height>
Check that coordinates are normalized (0-1 range)
Ensure data.yaml paths are correct
Verify image-label pairs match (same filename, different extension)

Fixing Export Failures:

Update Ultralytics: Add ultralytics>=8.3.0 to requirements
Check CUDA/TensorRT compatibility
Try without optimization: simplify=False for ONNX
Ensure model path is correct

Fixing Prediction Job Failures:

Verify checkpoint ID is correct
Check that model path /checkpoint/weights/best.pt exists
Ensure source path is correct (use /input for datasets)
Check logs: flexai training logs <job-name>

Dataset Format Requirements

If you have a dataset in another format (COCO JSON, Pascal VOC, etc.), you’ll need to convert it to YOLO format before using it with this blueprint.

YOLO Format Specification

Each image should have a corresponding text file with the same name:

<class_id> <x_center> <y_center> <width> <height>

Where:

class_id: Integer class ID (0-indexed)
x_center, y_center, width, height: Normalized coordinates (0-1 range)

Example label file (image001.txt):

0 0.5 0.5 0.3 0.4
1 0.2 0.3 0.1 0.15

Converting Other Formats

For converting from other formats:

COCO JSON: See Ultralytics COCO format documentation
Pascal VOC: See Ultralytics VOC format documentation
Other formats: Refer to Ultralytics Data Formats guide

After converting your dataset to YOLO format, ensure your data.yaml has path: /input before uploading to FlexAI.

Best Practices

Dataset Preparation

Use high-quality, diverse images
Ensure balanced class distribution
Include various lighting conditions, angles, and backgrounds
Minimum 1500 images per class recommended
Validate annotation accuracy before training

Training

Start with pre-trained weights (transfer learning)
Use data augmentation (enabled by default)
Monitor validation metrics, not just training loss
Use early stopping to prevent overfitting
Save checkpoints regularly

Deployment

Export to optimized formats (ONNX/TensorRT) for production
Test on representative images before deployment
Set appropriate confidence thresholds (0.25-0.5 typical range)
Benchmark inference speed on target hardware

References

Code

requirements.txt

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​Overview

​Option A: Use COCO8 Dataset (Quick Start)

​Option B: Prepare Your Own Custom Dataset

​Upload Custom Dataset to FlexAI

​Training on COCO8 Dataset

​Training on Custom Dataset

​Training with Larger Models

​List Training Checkpoints

​Run Validation

​Understanding Validation Metrics

​Download Checkpoint Locally

​Export to ONNX

​Export to TensorRT

​Available Export Formats

​Run Inference as a Training Job

​Run Batch Predictions

​Download Prediction Results

​Quick Local Testing

​Monitoring Training Progress

​Check Training Status

​View Training Logs

​Training Observability with TensorBoard

​Weights & Biases Integration

​Advanced Use Cases

​Object Tracking

​Model Benchmarking

​Hyperparameter Tuning

​Expected Results

​Detection Performance (YOLO11n on COCO8)

​Segmentation Performance (YOLO11n-seg on COCO8-seg)

​Training Time

​Technical Details

​Recommended Resource Configuration

​Key Training Parameters

​Multi-GPU Training

​Transfer Learning

​Troubleshooting

​Common Issues

​Dataset Format Requirements

​YOLO Format Specification

​Converting Other Formats

​Best Practices

​Dataset Preparation

​Training

​Deployment

​References

​Code

​requirements.txt

​🚀 Run this on FlexAI

Overview

Option A: Use COCO8 Dataset (Quick Start)

Option B: Prepare Your Own Custom Dataset

Upload Custom Dataset to FlexAI

Training on COCO8 Dataset

Training on Custom Dataset

Training with Larger Models

List Training Checkpoints

Run Validation

Understanding Validation Metrics

Download Checkpoint Locally

Export to ONNX

Export to TensorRT

Available Export Formats

Run Inference as a Training Job

Run Batch Predictions

Download Prediction Results

Quick Local Testing

Monitoring Training Progress

Check Training Status

View Training Logs

Training Observability with TensorBoard

Weights & Biases Integration

Advanced Use Cases

Object Tracking

Model Benchmarking

Hyperparameter Tuning

Expected Results

Detection Performance (YOLO11n on COCO8)

Segmentation Performance (YOLO11n-seg on COCO8-seg)

Training Time

Technical Details

Recommended Resource Configuration

Key Training Parameters

Multi-GPU Training

Transfer Learning

Troubleshooting

Common Issues

Dataset Format Requirements

YOLO Format Specification

Converting Other Formats

Best Practices

Dataset Preparation

Training

Deployment

References

Code

requirements.txt

🚀 Run this on FlexAI