raab/efishSignalDetector
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getting back. start to implement greyscale images as inputs

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Till Raab 2023-11-06 08:43:25 +01:00
parent e23301c0d1
commit 4e1784a399
2 changed files with 17 additions and 5 deletions
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2
README.md
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@ -29,6 +29,8 @@ training and one for testing (both also stored in ./data/dataset).
### ToDos: ### ToDos:
* on a long scale: only save raw file bounding boxes in frequency and time (t0, t1, f0, f1) and the hyperparameters of the corresponding spectrogram. USE THESE PARAMETERS IN DATASET_FN. * on a long scale: only save raw file bounding boxes in frequency and time (t0, t1, f0, f1) and the hyperparameters of the corresponding spectrogram. USE THESE PARAMETERS IN DATASET_FN.
* rescale image input to (7, 7) * 256 --> width/height in inch * dpi
* when dataset input it spectrogram use resize transform.
## model.py ## model.py

20
confic.py
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@ -1,24 +1,34 @@
import torch import torch
import pathlib import pathlib
# training parameters
BATCH_SIZE = 8 BATCH_SIZE = 8
RESIZE_TO = 416
NUM_EPOCHS = 10 NUM_EPOCHS = 10
NUM_WORKERS = 4 NUM_WORKERS = 4
DEVICE = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
# input parameters
IMG_SIZE = (7, 7) # inches IMG_SIZE = (7, 7) # inches
IMG_DPI = 256 IMG_DPI = 256
RESIZE_TO = 416 # ToDo: alter this parameter to (7, 7) * 256 [IMG_SIZE * IMG_DPI]
DEVICE = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') # dataset parameters
CLASSES = ['__backgroud__', '1'] CLASSES = ['__backgroud__', '1']
NUM_CLASSES = len(CLASSES) NUM_CLASSES = len(CLASSES)
# data snippet paramters
MIN_FREQ = 200
MAX_FREQ = 1500
DELTA_FREQ = 200
FREQ_OVERLAP = 25
DELTA_TIME = 60*10
TIME_OVERLAP = 60*1
# output parameters
DATA_DIR = 'data/dataset' DATA_DIR = 'data/dataset'
OUTDIR = 'model_outputs' OUTDIR = 'model_outputs'
INFERENCE_OUTDIR = 'inference_outputs' INFERENCE_OUTDIR = 'inference_outputs'
for required_folders in [DATA_DIR, OUTDIR, INFERENCE_OUTDIR]: for required_folders in [DATA_DIR, OUTDIR, INFERENCE_OUTDIR]:
if not pathlib.Path(required_folders).exists(): if not pathlib.Path(required_folders).exists():
pathlib.Path(required_folders).mkdir(parents=True, exist_ok=True) pathlib.Path(required_folders).mkdir(parents=True, exist_ok=True)