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
1 maintainer
Metadata
1 maintainer
Release files for dnnlab 2.2.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| dnnlab-2.2.5.tar.gz | 80.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| dnnlab-2.2.5-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 199.6 kB
Release files / dnnlab-2.2.5.tar.gz
| Download URL | dnnlab-2.2.5.tar.gz |
|---|---|
| Size | 80.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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twine/3.7.0 importlib_metadata/4.8.2 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0
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Release files / dnnlab-2.2.5-py3-none-any.whl
| Download URL | dnnlab-2.2.5-py3-none-any.whl |
|---|---|
| Size | 118.9 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
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| Uploaded via |
twine/3.7.0 importlib_metadata/4.8.2 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0
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