Train a Model

LLaVA-1.5 is an open-source, multi-modal language model. You can ask LLaVA-1.5 questions in text and optionally provide an image as context for your question. The code for LLaVA-1.5 was released to accompany the "Improved Baselines with Visual Instruction Tuning" paper. Use the demo.

Ultralytics YOLOv8 is a convolutional neural network that supports realtime object detection, instance segmentation, and other tasks. It can be deployed to a variety of edge devices.

In this guide, we show how to train a computer vision model without labelling using these two models.

Use LLaVA to train a YOLOv8 Object Detection model

Using autodistill, you can train computer vision models on custom datasets in a few lines of code. Autodistill allows you to use state-of-the-art foundation models that know a lot about a variety of objects to label data for your project. You can then train a new model with your labeled data. This whole process uses around a dozen lines of code.

To learn more about how autodistill works, read our overview guide or watch our YouTube tutorial.

In this guide, we will show you how to use


to train a



To use

to train a
model, we will:

1. Install autodistill
2. Create a dataset
3. Find a prompt to label images in the dataset
4. Label our data with autodistill
5. Train a new

Let's get started!

Step #1: Install Dependencies

First, install the following packages:

pip install autodistill autodistill-yolov8 autodistill-llava supervision

Step #2: Create a Dataset

Before you can label a dataset, you need a dataset with which to work.

Roboflow has a few resources that can help you create a dataset for your project:

You can use any folder of images you have on your local machine with Autodistill, too.

Step #3: Label Images with LLaVA

Autodistill has two model types:

  1. A Base Model, which automatically labels your data, and;
  2. A Target Model, which trains on your labeled data.

To label your dataset with a Base Model, you need to provide prompt(s) that are relevant to the classes you want to label.

Replace "example" below with the prompt you want to use. Replace "class" with the name of the class you want the prompt results to be saved as in your dataset. Also, replace the IMAGE_NAME with an image from your dataset.

The code cell below loads the base model with your prompt on the provided image, then visualizes the results.

You may need to experiment with a few prompts.

from autodistill_llava import LLaVA
from autodistill.detection import CaptionOntology

base_model = LLaVA(ontology=CaptionOntology({"shipping container": "container"}))

IMAGE_NAME = "valid/images/image.jpg"

image = os.path.join(DATASET_NAME, IMAGE_NAME)

predictions = base_model.predict(image)

Step #4: Label Data

To start labeling your images with


, run the following lines of code:

base_model.label(input_folder="./images", output_folder="./dataset")

Step #5: Train a YOLOv8 Model

To train a


model using your newly-labeled dataset, run the following code:

from autodistill_yolov8 import YOLOv8

target_model = YOLOv8("")
target_model.train("./dataset/data.yaml", epochs=200)

# run inference on the new model
pred = target_model.predict("./dataset/valid/your-image.jpg", confidence=0.5)


After running this cell, you will have model weights that you can use to run inference on your new model.

Step #5: Upload Model to Roboflow (Optional)

You can deploy your trained model to Roboflow. By deploying your model to Roboflow, you can run inference on our infinitely-scalable API. As your inference demands grow, our you will continue to see high levels of performance thanks to autoscaling infrastructure that is always on.

Roboflow offers:

Fully managed, infinitely-scalable APIs for deployed models

SDKs for common deployment targets (NVIDIA Jetson, Luxonis OAK, Docker, and more)

SOC II Type 1 Compliant

Trusted by 250,000+ developers

To deploy your model to Roboflow, run the following code:

import roboflow



rf = roboflow.Roboflow()

project = rf.workspace().project(PROJECT_ID)
project.version(DATASET_VERSION).deploy(model_type="yolov8", model_path=f"./runs/detect/train/")

Deploy your model with Roboflow

Through Roboflow, you can deploy your model to a range of targets. Below, we have listed devices to which you can deploy your model using Roboflow SDKs.

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Explore More Models for Auto-Labeling

Base models are models you can use to automatically label data. Below are more base models you can use with Autodistill to label data.

Explore More Models for Training

Target models are models you can train with custom data. Below are more target models you can use with Autodistill.

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