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coco_detection_ssd_lite_mobilenet_v2_dataset_params.yaml
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coco_detection_ssd_lite_mobilenet_v2_dataset_params.yaml
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defaults:
- coco_detection_dataset_params
train_dataset_params:
data_dir: /data/coco # root path to coco data
subdir: images/train2017 # sub directory path of data_dir containing the train data.
json_file: instances_train2017.json # path to coco train json file, data_dir/annotations/train_json_file.
input_dim: [320, 320]
cache_annotations: True
ignore_empty_annotations: True
transforms:
- DetectionRandomAffine:
degrees: 0. # rotation degrees, randomly sampled from [-degrees, degrees]
translate: 0.1 # image translation fraction
scales: [0.5, 1.5] # random rescale range (keeps size by padding/cropping) after mosaic transform.
shear: 0. # shear degrees, randomly sampled from [-degrees, degrees]
target_size: ${dataset_params.train_dataset_params.input_dim}
filter_box_candidates: True # whether to filter out transformed bboxes by edge size, area ratio, and aspect ratio.
wh_thr: 2 # edge size threshold when filter_box_candidates = True (pixels)
area_thr: 0.1 # threshold for area ratio between original image and the transformed one, when when filter_box_candidates = True
ar_thr: 20 # aspect ratio threshold when filter_box_candidates = True
- DetectionHSV:
prob: 1.0 # probability to apply HSV transform
hgain: 5 # HSV transform hue gain (randomly sampled from [-hgain, hgain])
sgain: 30 # HSV transform saturation gain (randomly sampled from [-sgain, sgain])
vgain: 30 # HSV transform value gain (randomly sampled from [-vgain, vgain])
- DetectionHorizontalFlip:
prob: 0.5 # probability to apply horizontal flip
- DetectionPaddedRescale:
input_dim: ${dataset_params.train_dataset_params.input_dim}
- DetectionTargetsFormatTransform:
input_dim: ${dataset_params.train_dataset_params.input_dim}
output_format: LABEL_NORMALIZED_CXCYWH
class_inclusion_list:
max_num_samples:
with_crowd: False
train_dataloader_params:
batch_size: 32
num_workers: 8
shuffle: True
drop_last: True
pin_memory: True
worker_init_fn:
_target_: super_gradients.training.utils.utils.load_func
dotpath: super_gradients.training.datasets.datasets_utils.worker_init_reset_seed
collate_fn: DetectionCollateFN
val_dataset_params:
data_dir: /data/coco # root path to coco data
subdir: images/val2017 # sub directory path of data_dir containing the train data.
json_file: instances_val2017.json # path to coco train json file, data_dir/annotations/train_json_file.
input_dim: [320, 320]
cache_annotations: True
ignore_empty_annotations: True
transforms:
- DetectionPaddedRescale:
input_dim: ${dataset_params.val_dataset_params.input_dim}
- DetectionTargetsFormatTransform:
input_dim: ${dataset_params.val_dataset_params.input_dim}
output_format: LABEL_NORMALIZED_CXCYWH
class_inclusion_list:
max_num_samples:
with_crowd: True
val_dataloader_params:
batch_size: 16
num_workers: 8
drop_last: False
pin_memory: True
collate_fn: CrowdDetectionCollateFN
_convert_: all