mAP, Precision and Recall scores are provided for all models trained with Roboflow Train, and through the notebooks in the Roboflow Model Zoo.

• "When evaluating an object detection model in computer vision, mean average precision is the most commonly cited metric for assessing performance."
• ​What is Mean Average Precision (mAP) in Object Detection?​

  • ​Video​
• ​Evaluating Object Detection Models with mAP by Class​

• A measure of how precise a model is at prediction time. True positives are divided by all positives that have been guessed.

• A measure of performance for a prediction system. Recall is used to assess whether a prediction system is guessing enough. True positives are divided by all possible true positives.

• An attempt by a model to replicate the ground truth. A prediction usually contains a confidence value for each class.

• Formats available for importing and exporting on Roboflow

• ​How to Use the Roboflow Dataset Health Check (Video)

  • ​Documentation (Dataset Health Check)

• ​Dataset Health Check Tour​

This old machine learning adage conveys a salient machine learning point: unless input data is of high quality, model accuracy — even with the best computer vision architectures — will suffer. Understanding what preprocessing and augmentation are at their core enables data scientists to get the most out of their input data.

• ​What is Image Preprocessing? (Video)

• ​Advanced Augmentations in Roboflow​
• ​Getting Started with Image Augmentation (Video)

• ​Bounding Box Level Augmentations (Roboflow Blog)

• ​Documentation​

• AWS & Azure Custom Vision require Third-Party keys to be entered into your project's workspace. These access points must be set up on your target AWS or Azure account to enable integration with Roboflow.

• ​What is Zero Shot Object Tracking? (Blog post) | Video​
• ​How to Implement Object Tracking (Blog post)
• Zero Shot Object Tracking - Github Repository​

• What will our model experience "in the wild…?" 🤔

Through the dataset assembly process, our laser focus might miss a whole host of edge cases that we would naturally consider out-of-scope for our model. While out-of-scope instances are intuitive to us, our model has no way of knowing anything beyond the scope of what it has been shown.

• Using pre-trained weights to bootstrap your model’s learning. You are “transferring” the knowledge learned on another dataset and then “fine-tuning” it to learn about your new domain.