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roboflow_yolox.yaml
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roboflow_yolox.yaml
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# Checkout the datasets at https://universe.roboflow.com/roboflow-100?ref=blog.roboflow.com
#
# `dataset_name` refers to the official name of the dataset.
# You can find it in the url of the dataset: https://universe.roboflow.com/roboflow-100/digits-t2eg6 -> digits-t2eg6
#
# Example: python -m super_gradients.train_from_recipe --config-name=roboflow_yolox dataset_name=digits-t2eg6
defaults:
- training_hyperparams: coco2017_yolox_train_params
- dataset_params: roboflow_detection_dataset_params
- checkpoint_params: default_checkpoint_params
- _self_
- variable_setup
dataset_name: ??? # Placeholder for the name of the dataset you want to use (e.g. "digits-t2eg6")
dataset_params:
dataset_name: ${dataset_name}
num_classes: ${roboflow_dataset_num_classes:${dataset_name}}
architecture: yolox_m
arch_params:
num_classes: ${num_classes}
yolo_type: 'yoloX'
depth_mult_factor: 0.67
width_mult_factor: 0.75
train_dataloader: roboflow_train_yolox
val_dataloader: roboflow_val_yolox
load_checkpoint: False
checkpoint_params:
pretrained_weights: coco
result_path: # By defaults saves results in checkpoints directory
resume: False
training_hyperparams:
max_epochs: 100
resume: ${resume}
criterion_params:
num_classes: ${num_classes}
train_metrics_list:
- DetectionMetrics:
normalize_targets: True
post_prediction_callback:
_target_: super_gradients.training.models.detection_models.yolo_base.YoloXPostPredictionCallback
iou: 0.65
conf: 0.01
num_cls: 80
valid_metrics_list:
- DetectionMetrics:
normalize_targets: True
post_prediction_callback:
_target_: super_gradients.training.models.detection_models.yolo_base.YoloXPostPredictionCallback
iou: 0.65
conf: 0.01
num_cls: 80
multi_gpu: DDP
num_gpus: 3
experiment_name: ${architecture}_roboflow_${dataset_name}