Jax, Flax, examples (ImageClassification, SemanticSegmentation, and more...)
I started to learn JAX, Flax, Optax, etc ...
I will be adding mainly computer vision tasks. I will start with code for model learning, inference, and export to other frameworks (such as TensorFlow).
- Implementation of inference code.
- Export TensorFlow Saved Mdoel or ONNX model, etc...
- Add more models...
- Segmentation model (LR-RASPP).
- Object detection model.
- GAN model.
- Training with Colab TPU.
- JAX: Autograd and XLA
- Flax: A neural network library and ecosystem for JAX designed for flexibility
- Optax
- Orbax
| Paper's | URL |
|---|---|
| MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications | https://arxiv.org/abs/1704.04861 |
| MobileNetV2: Inverted Residuals and Linear Bottlenecks | https://arxiv.org/abs/1801.04381 |
| Searching for MobileNetV3 | https://arxiv.org/abs/1905.02244 |
| Identity Mappings in Deep Residual Networks | https://arxiv.org/abs/1603.05027 |
| Deep Residual Learning for Image Recognition | https://arxiv.org/abs/1512.03385 |
| A ConvNet for the 2020s | https://arxiv.org/abs/2201.03545 |
| Paper's | URL |
|---|---|
| Fast-SCNN: Fast Semantic Segmentation Network | https://arxiv.org/abs/1902.04502 |
| Searching for MobileNetV3 | https://arxiv.org/abs/1905.02244 |
| Fully Convolutional Networks for Semantic Segmentation | https://arxiv.org/abs/1411.4038 |
| Simple and Efficient Architectures for Semantic Segmentation | https://arxiv.org/abs/2206.08236 |
| DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation | https://arxiv.org/abs/1907.11357 |
| LEDNet: A Lightweight Encoder-Decoder Network for Real-time Semantic Segmentation | https://arxiv.org/abs/1905.02423 |
| Loss | Paper's | URL | Task |
|---|---|---|---|
| Cross Entropy Loss with class weight | - | - | Semantic Segmentation |
| OHEM Loss | Training Region-based Object Detectors with Online Hard Example Mining | https://arxiv.org/abs/1604.03540 | Semantic Segmentation |
| Recall Loss | Striking the Right Balance: Recall Loss for Semantic Segmentation | https://arxiv.org/abs/2106.14917 | Semantic Segmentation |
| Focal Loss | Focal Loss for Dense Object Detection | https://arxiv.org/abs/1708.02002 | Semantic Segmentation |
| Soft IoU Loss | Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation | https://home.cs.umanitoba.ca/~ywang/papers/isvc16.pdf | Semantic Segmentation |
W.I.P
$ pip install jax flax ml_collections clu