To solve #44 issue.
added resume option for to continue training from last ckpt.
🤖 Train
You can train your YOLO-NAS model with Single Command Line
Args
`-i`, `--data`: path to data.yaml
`-n`, `--name`: Checkpoint dir name
`-b`, `--batch`: Training batch size
`-e`, `--epoch`: number of training epochs.
`-s`, `--size`: Input image size
`-j`, `--worker`: Training number of workers
`-m`, `--model`: Model type (Choices: `yolo_nas_s`, `yolo_nas_m`, `yolo_nas_l`)
`-w`, `--weight`: path to pre-trained model weight (default: `coco` weight)
`--gpus`: Train on multiple gpus
`--cpu`: Train on CPU
`--resume`: To resume model training
**Other Training Parameters:**
`--warmup_mode`: Warmup Mode, eg: Linear Epoch Step
`--warmup_initial_lr`: Warmup Initial LR
`--lr_warmup_epochs`: LR Warmup Epochs
`--initial_lr`: Inital LR
`--lr_mode`: LR Mode, eg: cosine
`--cosine_final_lr_ratio`: Cosine Final LR Ratio
`--optimizer`: Optimizer, eg: Adam
`--weight_decay`: Weight Decay
To solve #44 issue. added resume option for to continue training from last ckpt.
🤖 Train
You can train your YOLO-NAS model with Single Command Line
Args
`-i`, `--data`: path to data.yaml `-n`, `--name`: Checkpoint dir name `-b`, `--batch`: Training batch size `-e`, `--epoch`: number of training epochs. `-s`, `--size`: Input image size `-j`, `--worker`: Training number of workers `-m`, `--model`: Model type (Choices: `yolo_nas_s`, `yolo_nas_m`, `yolo_nas_l`) `-w`, `--weight`: path to pre-trained model weight (default: `coco` weight) `--gpus`: Train on multiple gpus `--cpu`: Train on CPU `--resume`: To resume model training **Other Training Parameters:** `--warmup_mode`: Warmup Mode, eg: Linear Epoch Step `--warmup_initial_lr`: Warmup Initial LR `--lr_warmup_epochs`: LR Warmup Epochs `--initial_lr`: Inital LR `--lr_mode`: LR Mode, eg: cosine `--cosine_final_lr_ratio`: Cosine Final LR Ratio `--optimizer`: Optimizer, eg: Adam `--weight_decay`: Weight DecayExample: