| pvitoria/ChromaGAN |
167 |
|
0 |
0 |
almost 4 years ago |
0 |
|
5 |
agpl-3.0 |
Jupyter Notebook |
| Official Implementation of ChromaGAN: An Adversarial Approach for Picture Colorization |
| NVlabs/selfsupervised-denoising |
146 |
|
0 |
0 |
over 6 years ago |
0 |
|
0 |
other |
Python |
| High-Quality Self-Supervised Deep Image Denoising - Official TensorFlow implementation of the NeurIPS 2019 paper |
| scaleway/frontalization |
139 |
|
0 |
0 |
about 7 years ago |
0 |
|
5 |
|
Python |
| Pytorch deep learning face frontalization model |
| lmb-freiburg/flownet2-docker |
124 |
|
0 |
0 |
almost 7 years ago |
0 |
|
1 |
gpl-3.0 |
Shell |
| Dockerfile and runscripts for FlowNet 2.0 (estimation of optical flow) |
| mazenmel/Deep-homography-estimation-Pytorch |
89 |
|
0 |
0 |
about 4 years ago |
0 |
|
4 |
|
Jupyter Notebook |
| Deep homography network with Pytorch |
| GitiHubi/deepAI |
74 |
|
0 |
0 |
almost 7 years ago |
0 |
|
1 |
gpl-3.0 |
Jupyter Notebook |
| Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch. |
| lmb-freiburg/dispnet-flownet-docker |
72 |
|
0 |
0 |
about 9 years ago |
0 |
|
0 |
gpl-3.0 |
Shell |
| Dockerfile and runscripts for DispNet and FlowNet1 (estimation of disparity and optical flow) |
| zhangzhengde0225/CDNet |
66 |
|
0 |
0 |
over 4 years ago |
0 |
|
4 |
|
Python |
| The tutorials, datasets and source codes of the crosswalk detection (zebra crossing detection) network, which is robust in real scenes and real-time in Jetson nano. cross. detect. pedestrian. |
| tlc-pack/TLCBench |
51 |
|
0 |
0 |
over 4 years ago |
0 |
|
1 |
|
Python |
| Benchmark scripts for TVM |
| masahi/nnvm-vision-demo |
49 |
|
0 |
0 |
over 8 years ago |
0 |
|
0 |
|
Python |
| Demos interesting image-in, image-out networks running on both NVIDIA and AMD GPUs, with NNVM |
