Models
Resnet-32 vs. YOLOv4 PyTorch

Resnet-32 vs. YOLOv4 PyTorch

Both ResNet 32 and YOLOv4 PyTorch are commonly used in computer vision projects. Below, we compare and contrast ResNet 32 and YOLOv4 PyTorch.

Models

icon-model

ResNet 32

A fast, simple convolutional neural network that gets the job done for many tasks, including classification.
Learn more about ResNet 32
icon-model

YOLOv4 PyTorch

YOLOv4 has emerged as the best real time object detection model. YOLOv4 carries forward many of the research contributions of the YOLO family of models along with new modeling and data augmentation techniques. This implementation is in PyTorch.
Learn more about YOLOv4 PyTorch
Model Type
Classification
--
Object Detection
--
Model Features
Item 1 Info
Item 2 Info
Architecture
--
YOLO
--
Frameworks
Fast.ai v2
--
PyTorch
--
Annotation Format
Instance Segmentation
Instance Segmentation
GitHub
--
View Repo
--
View Repo
GitHub Stars
32+
--
4.4k+
--
License
--
Apache-2.0
--
Paper
--
View Paper
--
View Paper
Training Notebook
Train on Colab
--
Train on Colab
--
Deploy Model
--
rocket icon
Deploy with Roboflow
--
rocket icon
Deploy with Roboflow
Compare Alternatives
--
Compare with...
--
Compare with...

Compare ResNet 32 and YOLOv4 PyTorch with Autodistill

Compare Resnet-32 vs. YOLOv4 PyTorch

Provide your own image below to test YOLOv8 and YOLOv9 model checkpoints trained on the Microsoft COCO dataset.

COCO can detect 80 common objects, including cats, cell phones, and cars.