DnnLab
Project description
DnnLab
Dnnlab is a small framework for deep learning models based on TensorFlow.
It provides custom training loops for:
- Generative Models (GAN, cGan, cycleGAN)
- Image Detection (custom YOLO)
Additonaly custom Keras Layer:
- Non-Local-Blocks (Self-Attention)
- Squeeze and Excitation Blocks (SEBlocks)
- YOLO-Decoding Layer
Input pipeline functionality:
- YOLO (Tfrecords to Datasets)
- YOLO data augmentation
- Generative Models (Tfrecords to Datasets)
TensorBoard output:
- YOLO coco metrics (Precision (mAP) & Recall)
- YOLO loss (loss_class, loss_conf, loss_xywh, total_loss)
- YOLO bounding boxes
- Generative Models (Loss & Images)
Requirements
TensorFlow 2.3.0
Installation
Run the following to install:
pip install dnnlab
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| Filename, size dnnlab-1.1.10-py3-none-any.whl (98.7 kB) | File type Wheel | Python version py3 | Upload date | Hashes View |
| Filename, size dnnlab-1.1.10.tar.gz (67.0 kB) | File type Source | Python version None | Upload date | Hashes View |
