TensorFlow provides a wide range of pre-trained models that can be used for various tasks. Below is a summary of some of the key models available.
Pre-trained Models
- Image Classification: These models are designed to classify images into various categories. They include ResNet, Inception, and MobileNet.
- Object Detection: These models can detect and classify objects within an image. An example is SSD with MobileNet.
- Text Classification: These models are used for classifying text into predefined categories. Examples include BERT and DistilBERT.
- Reinforcement Learning: TensorFlow provides models for reinforcement learning tasks, such as Q-learning and policy gradients.
Model Details
Here are some details about the key models:
- ResNet: A deep convolutional neural network architecture known for its ability to handle large amounts of data.
- Inception: This model uses a multi-scale design, combining information from different feature maps.
- MobileNet: This model is optimized for mobile and edge devices, offering a balance between accuracy and performance.
Further Reading
For more detailed information about these models, you can visit the TensorFlow Models GitHub repository.