**Note: YOLOv4 was released recently. You may also want to consider visiting our post on how to train YOLOv4 in Darknet.
At Roboflow, we're constantly adapting our product to make it as easy as possible for users to create custom computer vision models on high quality data. While we have an implementation of YOLOv3 in TensorFlow (and Keras), we continuously heard requests for support of YOLOv3 in PyTorch.
Today, we're introducing support for a PyTorch implementation of YOLOv3, originally introduced by the talented team at Ultralytics.
In our initial tests, we found this YOLOv3 implementation to be even more performant than our last. In fact, in 300 training epochs on Google Colab lasting slightly over an hour the model achieved 0.93+ recall and 0.978 mAP@50 on our (challenging) 12-class chess piece identification task.
If you are currently using our YOLOv3 TensorFlow implementation, we recommend attempting your results with the new notebook.
User Guide: Training a Custom PyTorch Model
If you're an existing Roboflow user with an uploaded object detection dataset, training a model with our PyTorch implementation is trivial.
Navigate to the dataset version you seek to use. When exporting, select "YOLO Darknet Weights," copy your download code Roboflow generates for you, and paste them into the PyTorch notebook at the cell marked
# REPLACE THIS LINK WITH YOUR OWN.
If you're new to Roboflow but want to make use of this implementation, getting stated is easy. Roboflow is free for small datasets. Follow our Getting Started Guide or jump right into https://app.roboflow.ai and follow the guided onboarding tutorial.
Roboflow accelerates your computer vision workflow through automated annotation
quality assurance, universal annotation format conversion (like
PASCAL VOC XML to COCO JSON and
), team sharing and versioning, and easy integration with popular
open source computer vision models.
Getting started with your first 1GB of data is completely free.