FastAI vs Keras: Which is Better?
FastAI and Keras are both high-level deep learning frameworks designed to simplify model training and development. However, they have different design philosophies and are built on different deep learning backends.
- FastAI is built on PyTorch and focuses on ease of use and automation for deep learning tasks.
- Keras is built on TensorFlow and provides a simple and modular API for creating deep learning models.
1. Overview of FastAI and Keras
FastAI
FastAI is a deep learning library built on top of PyTorch. It is designed to make deep learning accessible to beginners while still being powerful enough for researchers.
✅ Built on PyTorch: Inherits PyTorch’s dynamic computation graphs.
✅ Automated Training Features: Handles learning rate selection, data augmentation, and model fine-tuning.
✅ Prebuilt Models: Comes with state-of-the-art models for vision, NLP, and tabular data.
✅ Strong in Transfer Learning: Makes it easy to apply transfer learning for various tasks.
✅ Less Boilerplate Code: Requires less manual coding compared to PyTorch.
Keras
Keras is a high-level deep learning API that runs on TensorFlow. It is known for its simplicity and ease of use, making it popular among beginners and production-level applications.
✅ Built on TensorFlow: Leverages TensorFlow’s powerful ecosystem.
✅ Easy-to-Use API: Simple syntax for building neural networks.
✅ Good for Prototyping: Quickly test different architectures.
✅ Production-Ready: TensorFlow supports large-scale deployment.
✅ Pretrained Models: Includes models like VGG, ResNet, and Inception.
2. Performance and Ease of Use
| Feature | FastAI | Keras |
|---|---|---|
| Ease of Use | Very easy (built-in automation) | Very easy (intuitive API) |
| Training Speed | Fast (automated optimizations) | Fast (optimized for TensorFlow) |
| Customization | Moderate | Moderate |
| Prebuilt Models | Yes | Yes |
| Production Deployment | Moderate | Excellent (via TensorFlow) |
| GPU Support | Yes (via PyTorch) | Yes (via TensorFlow) |
| Community Support | Growing | Very large |
3. Strengths and Weaknesses
FastAI Strengths
✔ Beginner-Friendly: High-level API simplifies deep learning.
✔ Automated Features: Learning rate finder, augmentation, and transfer learning.
✔ Strong in Computer Vision & NLP: Optimized for image and text classification.
✔ Based on PyTorch: Inherits PyTorch’s flexibility and performance.
FastAI Weaknesses
❌ Less Customization: Harder to build completely custom architectures.
❌ Smaller Ecosystem: Compared to TensorFlow and Keras.
Keras Strengths
✔ Simple & Modular: Easy to define and train neural networks.
✔ Large Community: Well-documented and widely used.
✔ Production-Ready: TensorFlow’s ecosystem supports mobile and cloud deployment.
✔ Excellent for Beginners: Requires minimal code to build models.
Keras Weaknesses
❌ Less Automated: Requires more manual tuning compared to FastAI.
❌ Less Flexibility than PyTorch: Not as easy to modify computation graphs.
4. When to Use FastAI vs Keras?
Use FastAI When:
✔ You need automated deep learning tools for quick training.
✔ You are working with images, text, or tabular data.
✔ You want prebuilt models for transfer learning.
✔ You are comfortable using PyTorch as a backend.
Use Keras When:
✔ You want a simple API for defining deep learning models.
✔ You need production-ready AI (TensorFlow’s ecosystem).
✔ You are working on mobile and cloud deployment.
✔ You are familiar with TensorFlow.
5. Conclusion: Which is Better?
- For beginners and quick training → FastAI is better
- For TensorFlow users and production deployment → Keras is better
- For prebuilt automation (vision, NLP) → FastAI is better
- For large-scale deployment (cloud, mobile) → Keras is better
If your goal is quick training and automation, go with FastAI. If you need TensorFlow’s ecosystem and production support, Keras is the better choice.