A fast, simple convolutional neural network that gets the job done for many tasks, including classification.
Here is an overview of the
model:
Resnet34 is a state-of-the-art image classification model, structured as a 34 layer convolutional neural network and defined in "Deep Residual Learning for Image Recognition". Restnet34 is pre-trained on the ImageNet dataset which contains 100,000+ images across 200 different classes.
However, RestNet is different from traditional neural networks in the sense that it takes residuals from each layer and uses them in the subsequent connected layers (similar to residual neural networks used for text prediction).
Below. on the right-hand side, is Resnet34's architecture where the 34 layers and the residuals from one layer to another are visualized.
Learn how to use Resnet34 Custom Resnet34 Model for Image Classification using fastai and PyTorch: https://blog.roboflow.com/custom-resnet34-classification-model/
Using the open-source Autodistill package, you can train efficient models ready for deployment in a few lines of code.
Get startedYOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
ResNet 34
uses the
annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.
Curious about how YOLOv5 compares to other models? Check out our model comparisons.
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