PaddleClas/ppcls/configs/DeepHash/LCDSH.yaml

138 lines
2.9 KiB
YAML

# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output
device: gpu
save_interval: 15
eval_during_train: True
eval_interval: 15
epochs: 150
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: ./inference
eval_mode: retrieval
use_dali: False
to_static: False
#feature postprocess
feature_normalize: False
feature_binarize: "sign"
# model architecture
Arch:
name: RecModel
infer_output_key: features
infer_add_softmax: False
Backbone:
name: AlexNet
pretrained: True
class_num: 48
# loss function config for train/eval process
Loss:
Train:
- LCDSHLoss:
weight: 1.0
_lambda: 3
n_class: 10
Eval:
- LCDSHLoss:
weight: 1.0
_lambda: 3
n_class: 10
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Piecewise
learning_rate: 0.00001
decay_epochs: [200]
values: [0.00001, 0.000001]
# data loader for train and eval
DataLoader:
Train:
dataset:
name: ImageNetDataset
image_root: ./dataset/CIFAR10/
cls_label_path: ./dataset/CIFAR10/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
batch_size: 128
drop_last: False
shuffle: True
loader:
num_workers: 4
use_shared_memory: True
Eval:
Query:
dataset:
name: ImageNetDataset
image_root: ./dataset/CIFAR10/
cls_label_path: ./dataset/CIFAR10/test_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
batch_size: 128
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Gallery:
dataset:
name: ImageNetDataset
image_root: ./dataset/CIFAR10/
cls_label_path: ./dataset/CIFAR10/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
batch_size: 128
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Metric:
Eval:
- mAP: {}
- Recallk:
topk: [1, 5]