Train Computer Vision Models with Roboflow

Training Infrastructure for Every Task and Environment

Hosted training for state of the art models, customized for your dataset, in no time.

Nano

2H 5M

Small

2H 5MI

Medium

2H 5M

Large

2H 5M

Extra Large

2H 5M

Accuracy

Select From 5 Model Sizes

Train nano models for fast iteration and low compute deployments or XL models for the highest level of accuracy

vials on a conveyor belt with bounding boxes around them

Object Detection

Classification

Keypoint Detection

Instance Segmentation

bottles in a factory with caps and body segmented

Semantic Segmentation

worker getting into a forklift - text over it says worker is wearing a helmet

Vision Language

Train Across 6 Task Types

Vision-language, object detection, classification, keypoint detection, instance segmentation, and semantic segmentation

NEURAL ARCHITECTURE SEARCH

Discover and train the best model for your data

Automatic architecture search

Evaluate thousands of architectures to find the top performing candidates. Generate 10-100 models at once, to find the perfect model the first time.

Improved model performance

Customers see improvements in both accuracy and latency, even compared to fine-tuned RF-DETR models.

Reduce Training Costs

A NAS run generates many models at a lower cost-per-model than traditional training strategies, replacing costly guess-and-check training strategies

Tools to Improve Your Models

Prototype, experiment, test, integrate, and deploy models to production.

Test your model in the browser before deploying to production.

Manage model versions to compare performance across multiple iterations.

Monitor metrics on mean average precision, recall, and more.

Model Understanding and Evaluation

Insights into how your vision models are performing to locate edge cases, anomalies, and areas of low performance.

Vector Analysis

Bad

Good

Vector Analysis

Use semantic embeddings to find patterns in model failures and what type of data your models needs to better handle edge cases

Confusion Matrix

Gain visibility into exactly where your model failed and use that data to improve the next version of your model

Immediate ROI From Your Custom Model

Deploy to production, use for model assisted labeling, or as a training checkpoint.

ARM CPU

x86 CPU

Luxonis OAK

NVIDIA GPU

NVIDIA TRT

NVIDIA Jetson

Raspberry Pi

ARM CPU

x86 CPU

Luxonis OAK

NVIDIA GPU

NVIDIA TRT

NVIDIA Jetson

Raspberry Pi

Python


CLIENT = InferenceHTTPClient(
    api_url="https://detect.roboflow.com",
    api_key="****"
)

result = CLIENT.infer("YOUR_IMAGE.jpg", model_id="safety-vest-segmentation/4")

Deploy using out-of-the-box options with pre-built code snippets

cans on a conveyor belt with bounding boxes

22 Annotations added

Your model enables assisted labeling to speed up annotation work

Transfer learning from your model helps increase performance and get to production with less data

Simplify Model Building with Hosted Training