Training Loggers¶

RF-DETR supports integration with popular experiment tracking and visualization platforms. You can enable one or more loggers to monitor your training runs, compare experiments, and track metrics over time.

TensorBoard¶

TensorBoard is a powerful toolkit for visualizing and tracking training metrics.

Setup¶

Install the required packages:

pip install "rfdetr[metrics]"

Usage¶

Enable TensorBoard logging in your training:

from rfdetr import RFDETRMedium

model = RFDETRMedium()

model.train(
    dataset_dir="path/to/dataset",
    epochs=100,
    batch_size=4,
    grad_accum_steps=4,
    lr=1e-4,
    output_dir="output",
    tensorboard=True,
)

Viewing Logs¶

Local environment:

tensorboard --logdir output

Then open http://localhost:6006/ in your browser.

Google Colab:

%load_ext tensorboard
%tensorboard --logdir output

Logged Metrics¶

TensorBoard tracks:

Training and validation loss (total)
Validation mAP
EMA model metrics (when enabled)

Weights and Biases¶

Weights and Biases (W&B) is a cloud-based platform for experiment tracking and visualization.

Setup¶

Install the required packages:

pip install "rfdetr[metrics]"

Log in to W&B:

wandb login

You can retrieve your API key at wandb.ai/authorize.

Usage¶

Enable W&B logging in your training:

from rfdetr import RFDETRMedium

model = RFDETRMedium()

model.train(
    dataset_dir="path/to/dataset",
    epochs=100,
    batch_size=4,
    grad_accum_steps=4,
    lr=1e-4,
    output_dir="output",
    wandb=True,
    project="my-detection-project",
    run="experiment-001",
)

Configuration¶

Parameter	Description
`project`	Groups related experiments together
`run`	Identifies individual training sessions

If you don't specify a run name, W&B assigns a random one automatically.

Features¶

Access your runs at wandb.ai. W&B provides:

Real-time metric visualization
Experiment comparison
Hyperparameter tracking
System metrics (GPU usage, memory)
Training config logging

ClearML¶

ClearML is an open-source platform for managing, tracking, and automating machine learning experiments.

Setup¶

Install the required packages:

pip install "rfdetr[metrics]"

Initialize ClearML:

clearml-init

Follow the instructions to connect to your ClearML server (hosted or self-hosted).

Usage¶

Enable ClearML logging in your training:

from rfdetr import RFDETRMedium

model = RFDETRMedium()

model.train(
    dataset_dir="path/to/dataset",
    epochs=100,
    batch_size=4,
    grad_accum_steps=4,
    lr=1e-4,
    output_dir="output",
    clearml=True,
    project="my-detection-project",
    run="experiment-001",
)

Configuration¶

Parameter	Description
`project`	Groups related experiments together
`run`	Identifies individual training sessions (task name)

Features¶

Access your experiments in the ClearML Web UI. ClearML provides:

Real-time metric visualization
Experiment comparison
Hyperparameter tracking
Artifact storage
Model versioning

MLflow¶

MLflow is an open-source platform for the machine learning lifecycle that helps track experiments, package code into reproducible runs, and share and deploy models.

Setup¶

Install the required packages:

pip install "rfdetr[metrics]"

Usage¶

Enable MLflow logging in your training:

from rfdetr import RFDETRMedium

model = RFDETRMedium()

model.train(
    dataset_dir="path/to/dataset",
    epochs=100,
    batch_size=4,
    grad_accum_steps=4,
    lr=1e-4,
    output_dir="output",
    mlflow=True,
    project="my-detection-project",
    run="experiment-001",
)

Configuration¶

Parameter	Description
`project`	Sets the experiment name in MLflow
`run`	Sets the run name (auto-generated if not specified)

Custom Tracking Server¶

To use a custom MLflow tracking server, set environment variables:

import os

# Set MLflow tracking URI
os.environ["MLFLOW_TRACKING_URI"] = "https://your-mlflow-server.com"

# For authentication with tracking servers that require it
os.environ["MLFLOW_TRACKING_TOKEN"] = "your-auth-token"

# Then initialize and train your model
model = RFDETRMedium()
model.train(..., mlflow=True)

For teams using a hosted MLflow service (like Databricks), you'll typically need to set:

MLFLOW_TRACKING_URI: The URL of your MLflow tracking server
MLFLOW_TRACKING_TOKEN: Authentication token for your MLflow server

Viewing Logs¶

Start the MLflow UI:

mlflow ui --backend-store-uri <OUTPUT_PATH>

Then open http://localhost:5000 in your browser to access the MLflow dashboard.

Using Multiple Loggers¶

You can enable multiple logging systems simultaneously:

model.train(
    dataset_dir="path/to/dataset",
    epochs=100,
    tensorboard=True,
    wandb=True,
    clearml=True,
    mlflow=True,
    project="my-project",
    run="experiment-001",
)

This allows you to leverage the strengths of different platforms:

TensorBoard: Local visualization and debugging
W&B: Cloud-based collaboration and experiment comparison
ClearML: End-to-end MLOps pipeline automation
MLflow: Model registry and deployment tracking