File size: 1,431 Bytes
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general:
workers: 4
no_prefetcher: true
display_figures: False
model:
framework: 'torch'
model_name: 'mobilenetv2_w035_pt'
pretrained: True
pretrained_dataset: "imagenet"
input_shape: [3, 224, 224]
quantization:
quantizer: Onnx_quantizer
quantization_type: PTQ
quantization_input_type: uint8
quantization_output_type: float
export_dir: quantized_models
dataset:
dataset_name: "imagenet" # options "flowers102", "food101", "imagenet"
class_names: ''
classes_file_path: ./datasets/deployment_labels_imagenet.txt
#data_dir: "/local/datasets/" # there shud be imagenet folder inside this directory # can also be used for quantization
num_classes: 1000 # change according to your dataset
#train_split: "train"
#val_split: "val"
quantization_split: 0.01
quantization_path: "/local/datasets/ic_imagenet_2012/val/"
preprocessing:
rescaling:
scale: 1/255.0 # TODO scale node is already present under data_augmentation
offset: 0
resizing:
interpolation: nearest # nearest 'Image resize interpolation type (overrides model)'
aspect_ratio: fit
color_mode: rgb
# mean: [0.485, 0.456, 0.406] # 'Override mean pixel value of dataset'
# std: [0.229, 0.224, 0.225] # 'Override std deviation of dataset'
mlflow:
uri: ./pt/src/experiments_outputs/mlruns
hydra:
run:
dir: ./pt/src/experiments_outputs/${now:%Y_%m_%d_%H_%M_%S} |