Awesome
tensorflow-cnn-finetune
This repo is about finetuning some famous convolutional neural nets using TensorFlow.
ConvNets:
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 and data/val.txt.
Do not forget to pass
--num_classesflag when runningfinetune.pyscript.
AlexNet
Go into alexnet folder
cd alexnet
Finetuning
Download the weights if you hadn't before.
./download_weights.sh
Run the finetune.py script with your options.
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.
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.
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
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
cd vggnet
Finetuning
Download the weights if you hadn't before.
./download_weights.sh
Run the finetune.py script with your options.
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.
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.
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
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
cd resnet
Finetuning
Download the weights if you hadn't before.
./download_weights.sh
Run the finetune.py script with your options.
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.
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.
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
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 |