Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Resume #45

Merged
merged 2 commits into from
Sep 22, 2023
Merged

Resume #45

merged 2 commits into from
Sep 22, 2023

Conversation

naseemap47
Copy link
Owner

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

Example:

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

@naseemap47 naseemap47 linked an issue Sep 22, 2023 that may be closed by this pull request
@naseemap47 naseemap47 merged commit 0e8a725 into master Sep 22, 2023
1 check passed
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

training with more epochs
1 participant