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coco2017_yolo_nas_pose_m.yaml
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# YoloNASPose training on COCO2017 Dataset
# All YoloNASPose models trained in 640x640 resolution
#
# Instructions:
# 0. Have super-gradients installed (pip install super-gradients==3.3 or clone the repo and `pip install -e .`)
# 1. Make sure that the data is stored folder specified at `dataset_params.dataset_dir` (Default is /data/coco) or
# add "dataset_params.data_dir=<PATH-TO-DATASET>" at the end of the command below
# 2. Run the command to start the training:
# yolo_nas_pose_n: python -m super_gradients.train_from_recipe --config-name=coco2017_yolo_nas_pose_n
# yolo_nas_pose_s: python -m super_gradients.train_from_recipe --config-name=coco2017_yolo_nas_pose_s
# yolo_nas_pose_m: python -m super_gradients.train_from_recipe --config-name=coco2017_yolo_nas_pose_m
# yolo_nas_pose_l: python -m super_gradients.train_from_recipe --config-name=coco2017_yolo_nas_pose_l
#
# Training times and accuracies ([email protected] (COCO API, confidence 0.01, IoU threshold 0.7, test on original resolution):
# yolo_nas_pose_n: 93h on 8 NVIDIA GeForce RTX 3090, AP: 59.68 (val)
# yolo_nas_pose_s: 52h on 8 NVIDIA GeForce RTX 3090, AP: 64.15 (val)
# yolo_nas_pose_m: 57h on 8 NVIDIA GeForce RTX 3090, AP: 67.87 (val)
# yolo_nas_pose_l: 80h on 8 NVIDIA GeForce RTX 3090, AP: 68.24 (val)
#
# Offline evaluation using COCOEval for M variant:
# Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.679
# Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.887
# Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.745
# Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.622
# Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.771
# Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.730
# Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.919
# Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.792
# Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.672
# Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.813
defaults:
- training_hyperparams: coco2017_yolo_nas_pose_train_params
- dataset_params: coco_pose_estimation_yolo_nas_mosaic_heavy_dataset_params
- arch_params: yolo_nas_pose_m_arch_params
- checkpoint_params: default_checkpoint_params
- _self_
- variable_setup
architecture: yolo_nas_pose_m
multi_gpu: DDP
num_gpus: 8
experiment_suffix: ""
experiment_name: coco2017_${architecture}_${experiment_suffix}_${dataset_params.dataset_params_suffix}
arch_params:
num_classes: ${dataset_params.num_joints}
training_hyperparams:
initial_lr: 1e-4 # Scale factor to account reduced batch size
criterion_params:
dfl_loss_weight: 0.5
pose_reg_loss_weight: 10
ema_params:
decay_type: exp
beta: 50
checkpoint_params:
# For training Yolo-NAS-S pose estimation model we use pretrained weights for Yolo-NAS-S object detection model.
# By setting strict_load: key_matching we load only those weights that match the keys of the model.
checkpoint_path: https://sghub.deci.ai/models/yolo_nas_m_coco.pth
strict_load:
_target_: super_gradients.training.sg_trainer.StrictLoad
value: key_matching
dataset_params:
mosaic_prob: 0.5
train_dataloader_params:
batch_size: 32
val_dataloader_params:
batch_size: 32