pretrained
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20 hours ago •
GalacticExchange
GalacticExchange
Pretrained is the most complete and frequently updated list of pretrained top-performing models. Tensorflow, Theano and others. Want to add your model? File an issue, and we will add it.
Pretrained
The most complete and frequently updated list of pretrained top-performing models. Tensorflow, Theano and others.
Want to add your model? File an issue, and we will add your model, usually within 24 hours.
Image recognition
| Model | Url |
|---|---|
| Inception V1 | https://github.com/tensorflow/models/tree/master/slim#Pretrained |
| Inception V2 | https://github.com/tensorflow/models/tree/master/slim#Pretrained |
| Inception V3 | https://github.com/tensorflow/models/tree/master/slim#Pretrained |
| Inception-ResNet-v2 | https://github.com/tensorflow/models/tree/master/slim#Pretrained |
| ResNet V1 50 | https://github.com/tensorflow/models/tree/master/slim#Pretrained |
| ResNet V1 101 | https://github.com/tensorflow/models/tree/master/slim#Pretrained |
| ResNet V1 152 | https://github.com/tensorflow/models/tree/master/slim#Pretrained |
| ResNet V2 50 | https://github.com/tensorflow/models/tree/master/slim#Pretrained |
| ResNet V2 101 | https://github.com/tensorflow/models/tree/master/slim#Pretrained |
| ResNet V2 152 | https://github.com/tensorflow/models/tree/master/slim#Pretrained |
| ResNet V2 200 | https://github.com/tensorflow/models/tree/master/slim#Pretrained |
| VGG 16 | https://github.com/tensorflow/models/tree/master/slim#Pretrained |
| VGG 19 | https://github.com/tensorflow/models/tree/master/slim#Pretrained |
| vgg_19_2016_08_28.tar.gz | https://github.com/tensorflow/models/tree/master/slim#Pretrained |
| MobileNet_v1_1.0_224 | https://github.com/tensorflow/models/tree/master/slim#Pretrained |
| MobileNet_v1_0.50_160 | https://github.com/tensorflow/models/tree/master/slim#Pretrained |
| MobileNet_v1_0.25_128 | https://github.com/tensorflow/models/tree/master/slim#Pretrained |
| Pretrained ConvNets for pytorch: ResNeXt101, ResNet152, InceptionV4, InceptionResnetV2, etc. | https://github.com/Cadene/pretrained-models.pytorch |
| DenseNet | https://github.com/flyyufelix/DenseNet-Keras |
Object detection
| Model | Url |
|---|---|
| Fast-RCNN | https://github.com/rbgirshick/fast-rcnn |
| Faster-RCNN | https://github.com/ShaoqingRen/faster_rcnn |
Face recognition
| Model | Url |
|---|---|
| VGG-Face | http://www.vlfeat.org/matconvnet/pretrained/#face-recognition |
Pix2Pix style transfer
| Model | Url |
|---|---|
| Conditional adversarial nets | https://github.com/phillipi/pix2pix |
| Webcam Pix2Pix | https://github.com/memo/webcam-pix2pix-tensorflow |
Image to text generation
| Model | Url |
|---|---|
| Show and Tell | https://github.com/KranthiGV/Pretrained-Show-and-Tell-model |
| OpenNMT | http://opennmt.net/Models/ |
Text translation
| Model | Url |
|---|---|
| OpenNMT (English -> German, German -> English, Multi-way - FR,ES,PT,IT,RO<>FR,ES,PT,IT,RO) | http://opennmt.net/Models/ |
Text summarization
| Model | Url |
|---|---|
| OpenNMT (English) | http://opennmt.net/Models/ |
Word vectors
| Model | Url |
|---|---|
| FastText (294 languages) | https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md |
| Word2Vec | https://github.com/Kyubyong/wordvectors |
| Glove | https://nlp.stanford.edu/projects/glove/ |
| LexVec | https://github.com/alexandres/lexvec |
| Meta-Embeddings | http://cistern.cis.lmu.de/meta-emb/ |
Semantic segmentation
| Model | Url |
|---|---|
| Dilated Convolutions | https://github.com/ndrplz/dilation-tensorflow |
BVLC FCN | http://www.vlfeat.org/matconvnet/pretrained/#semantic-segmentation
Speech recognition
| Model | Url |
|---|---|
| Baidu Warp-CTC | https://github.com/SeanNaren/deepspeech.torch |
| Wavenet | https://github.com/buriburisuri/speech-to-text-wavenet |
Text to Emojis
| Model | Url |
|---|---|
| DeepMoji | https://github.com/bfelbo/DeepMoji |
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Pretrained is the most complete and frequently updated list of pretrained top-performing models. Tensorflow, Theano and others. Want to add your model? File an issue, and we will add it.