You can use tools/train.py to train a model.
Here is the full usage of the script:
python tools/train.py [ARGS]You can define all of your parameters in config file. It will override the arguments parser. I supply a default config file general_backbone/configs/image_clf_config.py for your study. To execute with your config file
python tools/train.py general_backbone/configs/image_clf_config.py
The meaning of config file parameters and arguments parser are the same as below table:
| ARGS | Type | Description |
|---|---|---|
| Priority config file | ||
--config or -c |
str | Path of config fire for training. Your config file's arguments overrides the arguments parser (default: None) |
| General Parameters | ||
--model |
str | Name of model to train (default: "resnet50") |
--epochs |
int | Number of epochs to train (default: 300) |
--start-epoch |
int | Manual number of start epoch (default: 0) |
--num-classes |
int | Number of categores in image classification task (default: None) |
--pretrained |
action_store:True | Whether using pretrained model (default: False) |
--eval-metric |
str | Best metric to evaluate (default: 'top1') |
| Checkpoint | ||
--output |
str | Path to output folder (default: current directory folder) |
--initial-checkpoint |
str | Initialize model from this checkpoint (default: none) |
--checkpoint-hist |
int | Number of checkpoints to keep (default: 10) |
--recovery_interval |
int | Number of interval checkpoint to save (default:10) |
--resume |
str | Resume full model and optimizer state from checkpoint (default: none) |
--no-resume-opt |
str | prevent resume of optimizer state when resuming model |
| Logging | ||
--log-interval |
str | how many batches to wait before logging training status (default:50) |
--log-wandb |
str | log training and validation metrics to wandb (default:False) |
--local-rank |
int | if equal 0, log information on local (default: 0) |
| DataLoader & Datset | ||
--data-dir |
str | path to root dataset |
--img-size |
int | input image size |
--batch-size or -b |
int | input batch size for training (default: 32) |
--num-workers or -j |
int | how many training processes to use (default: 4) |
--pin-memory |
action_store:True | Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU. |
--shuffle or -j |
action_store:True | Is shuffle dataset before training |
| Learning rate schedule parameters | ||
--sched |
str | LR scheduler (default: "cosine") |
--lr |
float | learning rate (default: 0.05) |
--lr-cycle-mul |
float | learning rate cycle len multiplier (default: 1.0) |
--lr-cycle-decay |
float | amount to decay each learning rate cycle (default: 0.5) |
--lr-cycle-limit |
float | learning rate cycle limit, cycles enabled if > 1 |
--lr-k-decay |
float | 'learning rate k-decay for cosine/poly (default: 1.0)' |
--warmup-lr |
float | 'warmup learning rate (default: 0.0001)' |
--min-lr |
float | 'lower lr bound for cyclic schedulers that hit 0 (1e-5)' |
--epoch-repeats |
int | epoch repeat multiplier (number of times to repeat dataset epoch per train epoch). |
--start-epoch |
int | manual epoch number (useful on restarts |
--decay-epochs |
int | epoch interval to decay LR |
--warmup-epochs |
int | epochs to warmup LR, if scheduler supports |
--cooldown-epochs |
int | epochs to cooldown LR at min_lr, after cyclic schedule ends |
--patience-epochs |
int | patience epochs for Plateau LR scheduler (default: 10) |
--decay-rate or dr |
float | LR decay rate (default: 0.1) |