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https://github.com/dgurkaynak/tensorflow-cnn-finetune
Finetuning AlexNet, VGGNet and ResNet with TensorFlow
https://github.com/dgurkaynak/tensorflow-cnn-finetune
alexnet convolutional-neural-networks finetune resnet tensorflow vggnet
Last synced: 15 days ago
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Finetuning AlexNet, VGGNet and ResNet with TensorFlow
- Host: GitHub
- URL: https://github.com/dgurkaynak/tensorflow-cnn-finetune
- Owner: dgurkaynak
- License: mit
- Created: 2017-06-21T10:47:33.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2021-04-11T18:39:36.000Z (over 3 years ago)
- Last Synced: 2024-04-14T23:58:05.549Z (7 months ago)
- Topics: alexnet, convolutional-neural-networks, finetune, resnet, tensorflow, vggnet
- Language: Python
- Homepage:
- Size: 6.39 MB
- Stars: 194
- Watchers: 10
- Forks: 93
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# tensorflow-cnn-finetune
This repo is about finetuning some famous convolutional neural nets using TensorFlow.
ConvNets:
- [AlexNet](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
- [VGGNet](https://arxiv.org/pdf/1409.1556.pdf)
- [ResNet](https://arxiv.org/pdf/1512.03385.pdf)
Requirements:
- Python 2.7 or 3.x
- Tensorflow 1.x (tested with 1.15.1)
- OpenCV2 (for data augmentation)
## Dataset file
You need to setup two dataset files for training and validation. The format must be like following:
```
/absolute/path/to/image1.jpg class_index
/absolute/path/to/image2.jpg class_index
...
```
`class_index` must start from `0`.
Sample dataset files can be found at [data/train.txt](data/train.txt) and [data/val.txt](data/val.txt).
> Do not forget to pass `--num_classes` flag when running `finetune.py` script.
## AlexNet
Go into `alexnet` folder
```bash
cd alexnet
```
### Finetuning
Download the weights if you hadn't before.
```bash
./download_weights.sh
````
Run the `finetune.py` script with your options.
```bash
python finetune.py \
--training_file=../data/train.txt \
--val_file=../data/val.txt \
--num_classes 26
```
| Option | Default | Description |
|-|-|-|
| `--training_file` | ../data/train.txt | Training dataset file |
| `--val_file` | ../data/val.txt | Validation dataset file |
| `--num_classes` | 26 | Number of classes |
| `--train_layers` | fc8,fc7 | Layers to be finetuned, seperated by commas. Avaliable layers: `fc8`, `fc7`, `fc6`, `conv5`, `conv4`, `conv3`, `conv2`, `conv1` |
| `--num_epochs` | 10 | How many epochs to run training |
| `--learning_rate` | 0.0001 | Learning rate for ADAM optimizer |
| `--dropout_keep_prob` | 0.5 | Dropout keep probability |
| `--batch_size` | 128 | Batch size |
| `--multi_scale` | | As a preprocessing step, it scalse the image randomly between 2 numbers and crop randomly at network's input size. For example if you set it `228,256`: - Select a random number between 228 and 256 -- S - Scale input image to `S x S` pixels - Crop it 227x227 randomly |
| `--tensorboard_root_dir` | ../training | Root directory to put the training logs and weights |
| `--log_step` | 10 | Logging period in terms of a batch run |
You can observe finetuning with the tensorboard.
```bash
tensorboard --logdir ../training
```
### Testing a dataset file
At the end of each epoch while finetuning, the current state of the weights are saved into `../training` folder (or any folder you specified with `--tensorboard_root_dir` option). Go to that folder and locate the model and epoch you want to test.
You must have your test dataset file as mentinoned before.
```bash
python test.py \
--ckpt ../training/alexnet_XXXXX_XXXX/checkpoint/model_epoch1.ckpt \
--num_classes 26 \
--test_file ../data/test.txt
```
| Option | Default | Description |
|-|-|-|
| `--ckpt` | | Checkpoint path; it must end with ".ckpt" |
| `--num_classes` | 26 | Number of classes |
| `--test_file` | ../data/val.txt | Test dataset file |
| `--batch_size` | 128 | Batch size |
### Predicting a single image
```bash
python predict.py \
--ckpt ../training/alexnet_XXXXX_XXXX/checkpoint/model_epoch1.ckpt \
--input_image=/some/path/to/image.jpg
```
| Option | Default | Description |
|-|-|-|
| `--ckpt` | | Checkpoint path; it must end with ".ckpt" |
| `--num_classes` | 26 | Number of classes |
| `--input_image` | | The path of input image |
## VGGNet
Go into `vggnet` folder
```bash
cd vggnet
```
### Finetuning
Download the weights if you hadn't before.
```bash
./download_weights.sh
````
Run the `finetune.py` script with your options.
```bash
python finetune.py \
--training_file=../data/train.txt \
--val_file=../data/val.txt \
--num_classes 26
```
| Option | Default | Description |
|-|-|-|
| `--training_file` | ../data/train.txt | Training dataset file |
| `--val_file` | ../data/val.txt | Validation dataset file |
| `--num_classes` | 26 | Number of classes |
| `--train_layers` | fc8,fc7 | Layers to be finetuned, seperated by commas. Avaliable layers: `fc8`, `fc7`, `fc6`, `conv5_1`, `conv5_2`, `conv5_3`, `conv4_1`, `conv4_2`, `conv4_3`, `conv3_1`, `conv3_2`, `conv3_3`, `conv2_1`, `conv2_2`, `conv1_1`, `conv1_2` |
| `--num_epochs` | 10 | How many epochs to run training |
| `--learning_rate` | 0.0001 | Learning rate for ADAM optimizer |
| `--dropout_keep_prob` | 0.5 | Dropout keep probability |
| `--batch_size` | 128 | Batch size |
| `--multi_scale` | | As a preprocessing step, it scalse the image randomly between 2 numbers and crop randomly at network's input size. For example if you set it `228,256`: - Select a random number between 228 and 256 -- S - Scale input image to `S x S` pixels - Crop it 224x224 randomly |
| `--tensorboard_root_dir` | ../training | Root directory to put the training logs and weights |
| `--log_step` | 10 | Logging period in terms of a batch run |
You can observe finetuning with the tensorboard.
```bash
tensorboard --logdir ../training
```
### Testing a dataset file
At the end of each epoch while finetuning, the current state of the weights are saved into `../training` folder (or any folder you specified with `--tensorboard_root_dir` option). Go to that folder and locate the model and epoch you want to test.
You must have your test dataset file as mentinoned before.
```bash
python test.py \
--ckpt ../training/vggnet_XXXXX_XXXX/checkpoint/model_epoch1.ckpt \
--num_classes 26 \
--test_file ../data/test.txt
```
| Option | Default | Description |
|-|-|-|
| `--ckpt` | | Checkpoint path; it must end with ".ckpt" |
| `--num_classes` | 26 | Number of classes |
| `--test_file` | ../data/val.txt | Test dataset file |
| `--batch_size` | 128 | Batch size |
### Predicting a single image
```bash
python predict.py \
--ckpt ../training/vggnet_XXXXX_XXXX/checkpoint/model_epoch1.ckpt \
--input_image=/some/path/to/image.jpg
```
| Option | Default | Description |
|-|-|-|
| `--ckpt` | | Checkpoint path; it must end with ".ckpt" |
| `--num_classes` | 26 | Number of classes |
| `--input_image` | | The path of input image |
## ResNet
Go into `resnet` folder
```bash
cd resnet
```
### Finetuning
Download the weights if you hadn't before.
```bash
./download_weights.sh
````
Run the `finetune.py` script with your options.
```bash
python finetune.py \
--training_file=../data/train.txt \
--val_file=../data/val.txt \
--num_classes 26
```
| Option | Default | Description |
|-|-|-|
| `--resnet_depth` | 50 | ResNet architecture to be used: 50, 101 or 152
| `--training_file` | ../data/train.txt | Training dataset file |
| `--val_file` | ../data/val.txt | Validation dataset file |
| `--num_classes` | 26 | Number of classes |
| `--train_layers` | fc | Layers to be finetuned, seperated by commas. Fully-connected last layer: `fc`, tho whole 5th layer: `scale5`, or some blocks of a layer: `scale4/block6,scale4/block5` |
| `--num_epochs` | 10 | How many epochs to run training |
| `--learning_rate` | 0.0001 | Learning rate for ADAM optimizer |
| `--dropout_keep_prob` | 0.5 | Dropout keep probability |
| `--batch_size` | 128 | Batch size |
| `--multi_scale` | | As a preprocessing step, it scalse the image randomly between 2 numbers and crop randomly at network's input size. For example if you set it `228,256`: - Select a random number between 228 and 256 -- S - Scale input image to `S x S` pixels - Crop it 224x224 randomly |
| `--tensorboard_root_dir` | ../training | Root directory to put the training logs and weights |
| `--log_step` | 10 | Logging period in terms of a batch run |
You can observe finetuning with the tensorboard.
```bash
tensorboard --logdir ../training
```
### Testing a dataset file
At the end of each epoch while finetuning, the current state of the weights are saved into `../training` folder (or any folder you specified with `--tensorboard_root_dir` option). Go to that folder and locate the model and epoch you want to test.
You must have your test dataset file as mentinoned before.
```bash
python test.py \
--ckpt ../training/resnet_XXXXX_XXXX/checkpoint/model_epoch1.ckpt \
--num_classes 26 \
--test_file ../data/test.txt
```
| Option | Default | Description |
|-|-|-|
| `--ckpt` | | Checkpoint path; it must end with ".ckpt" |
| `--resnet_depth` | 50 | ResNet architecture to be used: 50, 101 or 152
| `--num_classes` | 26 | Number of classes |
| `--test_file` | ../data/val.txt | Test dataset file |
| `--batch_size` | 128 | Batch size |
### Predicting a single image
```bash
python predict.py \
--ckpt ../training/resnet_XXXXX_XXXX/checkpoint/model_epoch1.ckpt \
--input_image=/some/path/to/image.jpg
```
| Option | Default | Description |
|-|-|-|
| `--ckpt` | | Checkpoint path; it must end with ".ckpt" |
| `--resnet_depth` | 50 | ResNet architecture to be used: 50, 101 or 152
| `--num_classes` | 26 | Number of classes |
| `--input_image` | | The path of input image |