This repository contains python jupyter notebooks of keras models with their summaries. In order for someone to view a model summary, has to load the model and run model summary. As simple as it is, it can be time-consuming when you are looking for the right model and have to load one model after another.
In Keras model summary we can find informations about:
- number pamameters
- default input size
- layer name and shape
Keras Models are loaded either from official keras library or from classification_models library. Keras library's collection of pre-trained models doesn' t contain some models that can be useful at this point. classification_models library contains some models that keras doesn't but also contains weights from other sources too (ex. resnet50 weights from mxnet).
From keras package (keras.applications)
| Model | Features | Size | Stem | input size (default) |
|---|---|---|---|---|
| DenseNet121 | 1024 | 8.1M | 7.0M | 224 |
| DenseNet169 | 1664 | 14.3M | 12.6M | 224 |
| DenseNet201 | 1920 | 20.2M | 18.3M | 224 |
| InceptionResNetV2 | 1536 | 55.9M | 54.3M | 299 |
| InceptionV3 | 2048 | 23.9M | 21.8M | 299 |
| MobileNet(alpha=1.0) | 1024 | 4.3M | 3.2M | 224 |
| MobileNetV2(alpha=1.0) | 1024 | 3.5M | 2.3M | 224 |
| NASNetLarge | 4032 | 93.5M | 84.9M | 331 |
| NASNetMobile | 1056 | 7.7M | 4.3M | 224 |
| Resnet50 | 2048 | 25.6M | 23.6M | 224 |
| VGG16 | 512 | 138.4M | 14.7M | 224 |
| VGG19 | 512 | 143.7M | 20.0M | 224 |
| Xception | 2048 | 22.9M | 20.9M | 299 |
From image-classifiers package (classification_models)
| Model | Features | Size | Stem |
|---|---|---|---|
| Resnet18 | 512 | 11.7M | 11.2M |
| Resnet34 | 512 | 21.8M | 21.3M |
| Resnet101 | 2048 | 44.6M | 42.6M |
| Resnet152 | 2048 | 60.3M | 58.3M |
| Resnext50 | 2048 | 25.1M | 23.0M |
| Resnext101 | 2048 | 44.3M | 42.3M |
| Senet154 | 2048 | 115.3M | 113.3M |
| SeResnet18 | 512 | 11.8M | 11.3M |
| SeResnet34 | 512 | 22.0M | 21.5M |
| SeResnet50 | 2048 | 28.1M | 26.1M |
| SeResnet101 | 2048 | 49.4M | 47.4M |
| SeResnet152 | 2048 | 67.0M | 64.9M |
| SeResnext50 | 2048 | 27.6M | 25.6M |
| SeResnext101 | 2048 | 49.1M | 47.0M |