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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Amazing Semantic Segmentation on Tensorflow && Keras (include FCN, UNet, SegNet, PSPNet, PAN, RefineNet, DeepLabV3, DeepLabV3+, DenseASPP, BiSegNet)
| Date | Stars |
|---|---|
| 2026-07-24 | 482 |
| 2026-07-25 | 482 |
| 2026-07-28 | 482 |
| 2026-07-30 | 482 |
| 2026-08-06 | 482 |
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# Amazing-Semantic-Segmentation [](https://github.com/luyanger1799/Amazing-Semantic-Segmentation.git) [](https://github.com/luyanger1799/Amazing-Semantic-Segmentation.git) [](https://github.com/luyanger1799/Amazing-Semantic-Segmentation.git) [](https://github.com/luyanger1799/Amazing-Semantic-Segmentation.git) >Amazing Semantic Segmentation on Tensorflow && Keras (include FCN, UNet, SegNet, PSPNet, PAN, RefineNet, DeepLabV3, DeepLabV3+, DenseASPP, BiSegNet ...) *** ## Models The project supports these semantic segmentation models as follows: >1. FCN-8s/16s/32s - [Fully Convolutional Networks for Semantic Segmentation](https://arxiv.org/pdf/1411.4038.pdf) >2. UNet - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/pdf/1505.04597.pdf) >3. SegNet - [SegNet:A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://arxiv.org/pdf/1511.00561.pdf) >4. Bayesian-SegNet - [Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding](https://arxiv.org/pdf/1511.02680v2.pdf) >5. PSPNet - [Pyramid Scene Parsing Network](https://arxiv.org/pdf/1612.01105.pdf) >6. RefineNet - [RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation](https://arxiv.org/pdf/1611.06612.pdf) >7. PAN - [Pyramid Attention Network for Semantic Segmentation](https://arxiv.org/pdf/1805.10180.pdf) >8. DeepLabV3 - [Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/pdf/1706.05587.pdf) >9. DeepLabV3Plus - [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/pdf/1802.02611.pdf) >10. DenseASPP - [DenseASPP for Semantic Segmentation in Street Scenes](http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_DenseASPP_for_Semantic_CVPR_2018_paper.pdf) >11. BiSegNet - [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://arxiv.org/pdf/1808.00897.pdf) *** ## Base Models The project supports these backbone models as follows, and your can choose suitable base model according to your needs. >1. VGG16/19 - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/pdf/1409.1556.pdf) >2. ResNet50/101/152 - [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf) >3. DenseNet121/169/201/264 - [Densely Connected Convolutional Networks](https://arxiv.org/pdf/1608.06993.pdf) >4. MobileNetV1 - [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/pdf/1704.04861.pdf) >5. MobileNetV2 - [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/pdf/1801.04381.pdf) >6. Xception - [Xception: Deep Learning with Depthwise Separable Convolutions](https://arxiv.org/pdf/1610.02357.pdf) >7. Xception-DeepLab - [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/pdf/1802.02611.pdf) *** ## Losses The project supports these loss functions: >1. Cross Entropy >2. Focal Loss >3. MIoU Loss >4. Self Balanced Focal Loss `original` >5. ... *** ## Optimizers The project supports these optimizers: >1. SGD >2. Adam >3. Nadam >4. AdamW >5. NadamW >5. SGDW *** ## Learning Rate Scheduler The project supports these learning rate schedule strategies: >1. step decay >2. poly decay >3. cosine decay >4. warm up ## Dataset Setting The folds of your dataset must satisfy the following structures: ```buildoutcfg |-- dataset | |-- train | | |-- images | | |-- labels | |-- valid | | |-- images | | |-- labels | |-- test | | |-- images | | |-- labels | |-- class_dict.csv | |-- evaluated_classes ``` *** ## Installation - Numpy `
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a8b33b2f3960deb1, topic:computer-vision, readme:image segmentation, name:semantic segmentation
matched fp:a8b33b2f3960deb1, topic:deep-learning, topic:tensorflow