Classifier-trains
Install as a package
pip3 install classifier-trains
Examples of Pipeline Configuration
For example of pipeline configuration, please see pipeline_config_only_train.yml, pipeline_config_only_eval.yml, full_pipeline_config.yml.
# Run training or evaluation
python3 -m classifier_trains run --config <path to yml config> --output_dir <output_dir>
# Run training or evaluation with profiling, which will generate a profile report
python3 -m classifier_trains profile --config <path to yml config> --output_dir <output_dir>
# Compute mean and std of dataset
python3 -m classifier_trains compute-mean-and-std --dir-path <path to dataset>
# Get output mapping of dataset
python3 -m classifier_trains get-output-mapping --dir-path <path to dataset>
Expected Folder Structure for Dataset
Dataset Directory
├── train
│ ├── <class1>
│ │ ├── <image1>
│ │ └── ...
│ └── <class2>
│ ├── <image1>
│ └── ...
├── val
│ ├── <class1>
│ │ ├── <image1>
│ │ └── ...
│ └── <class2>
│ ├── <image1>
│ └── ...
└── eval
├── <class1>
│ ├── <image1>
│ └── ...
└── <class2>
├── <image1>
└── ...
Pipeline Parameter Table
Pipeline Parameters
| Parameter |
Description |
Type |
Default |
Choices |
| enable_training |
Enable training |
bool |
False |
True, False |
| enable_evaluation |
Enable evaluation |
bool |
False |
True, False |
Model Parameters
| Parameter |
Description |
Type |
Default |
Choices |
| model |
Model architecture to use |
str |
/ |
resnet18, resnet34, resnet50, resnet152, vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, vgg19_bn, squeezenet1_0, squeezenet1_1, densenet121, densenet161, densenet169, densenet201, inception_v3 |
| num_classes |
Number of classes |
int |
/ |
Any positive integer |
| weights |
Pretrained weights |
str |
DEFAULT |
Check PyTorch Documentation |
| checkpoint_path |
Path to checkpoint |
Optional[str] |
None |
Self defined path |
Dataloader Parameters
| Parameter |
Description |
Type |
Default |
Choices |
| batch_size |
Batch size |
int |
/ |
Any positive integer |
| num_workers |
Number of workers |
int |
0 |
Any non negative integer |
Resize Parameters
| Parameter |
Description |
Type |
Default |
Choices |
| width |
Width of resized image |
int |
/ |
Any positive integer |
| height |
Height of resized image |
int |
/ |
Any positive integer |
| interpolation |
Interpolation method |
str |
bicubic |
nearest, nearest_exact, bilinear, bicubic |
| padding |
Padding |
Optional[str] |
None |
top_left, top_right, bottom_left, bottom_right, center |
| maintain_aspect_ratio |
Maintain aspect ratio |
bool |
False |
True, False |
Spatial Transform Parameters
| Parameter |
Description |
Type |
Default |
Choices |
| hflip_prob |
Horizontal flip probability |
float |
/ |
Any float between 0 and 1 |
| vflip_prob |
Vertical flip probability |
float |
/ |
Any float between 0 and 1 |
| max_rotate_in_degree |
Maximum rotation in degree |
float |
0.0 |
Any non negative float |
| allow_center_crop |
Allow center crop |
bool |
False |
True, False |
| allow_random_crop |
Allow random crop |
bool |
False |
True, False |
Color Transform Parameters
| Parameter |
Description |
Type |
Default |
Choices |
| allow_gray_scale |
Allow gray scale |
bool |
False |
True, False |
| allow_random_color |
Allow random color |
bool |
False |
True, False |
Preprocessing Parameters
| Parameter |
Description |
Type |
Default |
Choices |
| mean |
Mean of dataset |
List[float] |
/ |
Any list of floats |
| std |
Standard deviation of dataset |
List[float] |
/ |
Any list of floats |
Optimizer Parameters
| Parameter |
Description |
Type |
Default |
Choices |
| name |
Optimizer to use |
str |
/ |
sgd, adam, rmsprop, adamw |
| lr |
Learning rate |
float |
/ |
Any positive float |
| weight_decay |
Weight decay |
float |
0.0 |
Any non negative float |
| momentum |
Momentum |
Optional[float] |
None |
Any non negative float |
| alpha |
Alpha |
Optional[float] |
None |
Any non negative float |
| betas |
Betas |
Optional[Tuple[float, float]] |
None |
Any tuple of non negative floats |
Scheduler Parameters
| Parameter |
Description |
Type |
Default |
Choices |
| name |
Scheduler to use |
str |
/ |
step, consine |
| lr_min |
Minimum learning rate |
Optional[float] |
None |
Any non negative float |
| step_size |
Step size |
Optional[int] |
None |
Any positive integer |
| gamma |
Gamma |
Optional[float] |
None |
Any non negative float |
Training Parameters
| Parameter |
Description |
Type |
Default |
Choices |
| name |
Name of the experiment |
str |
/ |
Any string |
| num_epochs |
Number of epochs |
int |
/ |
Any positive integer |
| trainset_dir |
Path to training dataset |
str |
/ |
Any string |
| valset_dir |
Path to validation dataset |
str |
/ |
Any string |
| testset_dir |
Path to testing dataset |
Optional[str] |
None |
Any string |
| device |
Device to use |
str |
cuda |
cpu, cuda, etc. |
| max_num_hrs |
Maximum number of hours to run |
Optional[float] |
None |
Any non negative float |
| criterion |
What criterion to use for best model |
str |
loss |
loss, accuracy |
| validate_every |
Validate every n epochs |
int |
1 |
Any positive integer |
| save_every |
Save model every n epochs |
int |
3 |
Any positive integer |
| patience |
Patience for early stopping |
int |
5 |
Any positive integer |
| random_seed |
Random seed |
int |
42 |
Any positive integer |
| precision |
Precision |
int |
64 |
16, 32, 64 |
| export_last_as_onnx |
Export last model as ONNX |
bool |
False |
True, False |
| export_best_as_onnx |
Export best model as ONNX |
bool |
False |
True, False |
Evaluation Parameters
| Parameter |
Description |
Type |
Default |
Choices |
| name |
Name of the experiment |
str |
/ |
Any string |
| evalset_dir |
Path to evaluation dataset |
str |
/ |
Any string |
| device |
Device to use |
str |
cuda |
cpu, cuda, etc. |
| precision |
Precision |
int |
64 |
16, 32, 64 |
| random_seed |
Random seed |
int |
42 |
Any positive integer |
