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https://github.com/yuyang-huang/keras-inception-resnet-v2
The Inception-ResNet v2 model using Keras (with weight files)
https://github.com/yuyang-huang/keras-inception-resnet-v2
deep-learning keras machine-learning
Last synced: about 1 month ago
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The Inception-ResNet v2 model using Keras (with weight files)
- Host: GitHub
- URL: https://github.com/yuyang-huang/keras-inception-resnet-v2
- Owner: yuyang-huang
- Created: 2017-07-22T11:03:24.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2021-03-23T08:30:26.000Z (over 3 years ago)
- Last Synced: 2024-05-10T09:32:24.331Z (7 months ago)
- Topics: deep-learning, keras, machine-learning
- Language: Python
- Homepage:
- Size: 67.4 KB
- Stars: 179
- Watchers: 11
- Forks: 54
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-image-classification - unofficial-keras : https://github.com/yuyang-huang/keras-inception-resnet-v2
- awesome-image-classification - unofficial-keras : https://github.com/yuyang-huang/keras-inception-resnet-v2
README
# keras-inception-resnet-v2
The Inception-ResNet v2 model using Keras (with weight files)
Tested with `tensorflow-gpu==1.15.3` and `Keras==2.2.5` under Python 3.6
(although there are lots of deprecation warnings since this code was written way before TF 1.15).
Layers and namings follow the TF-slim implementation:
https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py
## News
This implementation has been merged into the `keras.applications` module!
Install the latest version Keras on GitHub and import it with:
```python
from keras.applications.inception_resnet_v2 import InceptionResNetV2, preprocess_input
```
## Usage
Basically the same with the `keras.applications.InceptionV3` model.
```python
from inception_resnet_v2 import InceptionResNetV2
# ImageNet classification
model = InceptionResNetV2()
model.predict(...)
# Finetuning on another 100-class dataset
base_model = InceptionResNetV2(include_top=False, pooling='avg')
outputs = Dense(100, activation='softmax')(base_model.output)
model = Model(base_model.inputs, outputs)
model.compile(...)
model.fit(...)
```
### Extract layer weights from TF checkpoint
```
python extract_weights.py
```
By default, the TF checkpoint file will be downloaded to `./models` folder, and the layer weights (`.npy` files) will be saved to `./weights` folder.
### Load NumPy weight files and save to a Keras HDF5 weights file
```
python load_weights.py
```
The following weight files:
- models/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5
- models/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5
will be generated.
### Test model prediction on single image
To test whether this implementation gives the same prediction as TF-slim implementation:
```
PYTHONPATH=../tensorflow-models/research/slim python test_inception_resnet_v2.py
```
`PYTHONPATH` should point to the `research/slim` folder under the https://github.com/tensorflow/models repo.
The image file `elephant.jpg` (and basically the entire idea of converting weights from TF-slim to Keras) comes from:
https://github.com/kentsommer/keras-inception-resnetV2
### Evaluate the model on ImageNet 2012 dataset
First, follow the
[instructions](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data)
from TF-slim to download and process the data.
Suppose that the dataset is saved to the `imagenet_2012` directory, to evaluate:
```
PYTHONPATH=../tensorflow-models/research/slim python evaluate_imagenet.py ../tensorflow-models/research/slim/datasets/imagenet_2012 --verbose
```
The script should print out top-1 and top-5 accuracy on validation set:
Implementation | Top-1 Accuracy | Top-5 Accuracy
--- | --- | ---
[TF-slim](https://github.com/tensorflow/models/tree/master/research/slim) | 80.4 | 95.3
This repo | 80.4 | 95.3
## Current status
- [X] Extract weights from TF-slim
- [X] Convert weights to HDF5 files
- [X] Test weight loading and image prediction (`elephant.jpg`)
- [X] Release weight files
- [X] Evaluate accuracy on ImageNet benchmark dataset