Qat

[naseemap47]

🤖 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 (ckpt_best.pth) (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

Example:

python3 train.py --data /dir/dataset/data.yaml --batch 6 --epoch 100 --model yolo_nas_m --size 640

# From Pre-trained weight
python3 train.py --data /dir/dataset/data.yaml --batch 6 --epoch 100 --model yolo_nas_m --size 640 \
                 --weight runs/train2/ckpt_latest.pth

Quantization Aware Training

Args

-i, --data: path to data.yaml
-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 (ckpt_best.pth)
--gpus: Train on multiple gpus
--cpu: Train on CPU

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

Example:

python3 qat.py --data /dir/dataset/data.yaml --weight runs/train2/ckpt_best.pth --batch 6 --epoch 100 --model yolo_nas_m --size 640