Open Source Computer Vision Classification Models

The Roboflow Model Library contains pre-configured model architectures for easily training computer vision models. Just add the link from your Roboflow dataset and you're ready to go! We even include the code to export to common inference formats like TFLite, ONNX, and CoreML.

If you'd like to request a model we haven't yet implemented, please get in touch.

PyTorch Classification :: YOLOv5 TXT

YOLOv5 Classification

An easy to use PyTorch model that achieves state of the art (or near state of the art) results for classification. Classification assigns a given image to an array of possible classes and can be binary or multi-class. Read More...

PyTorch Classification

Vision Transformer

The Vision Transformer leverages powerful natural language processing embeddings (BERT) and applies them to images Read More... v2 Classification


Resnet34 for state of the art image classification implemented in fastai v2 and PyTorch Read More...

PyTorch Classification :: CLIP

OpenAI Clip

CLIP (Contrastive Language-Image Pre-Training) is an impressive multimodal zero-shot image classifier that achieves impressive results in a wide range of domains with no fine-tuning. It applies the recent advancements in large-scale transformers like GPT-3 to the vision arena. Read More...

Keras Classification


EfficientNet is a family of state of the art classification models from GoogleAI that efficiently scale up as you increase the number of parameters in the network. Read More...

Tensorflow 2 Classification

MobileNetV2 Classification

MobileNet is a GoogleAI model well-suited for on-device, real-time classification (distinct from MobileNetSSD, Single Shot Detector). This implementation leverages transfer learning from ImageNet to your dataset. Read More... v2 Classification


A fast, simple convolutional neural network that gets the job done for many tasks, including classification here. Read More...