raab/efishSignalDetector
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README update

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Till Raab 2023-10-27 13:12:24 +02:00
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@ -6,6 +6,10 @@ spectrogram images. The model itself is a pretrained **FasterRCNN** Model with a
**ResNet50** Backbone. Only the final predictor is replaced to not predict the 91 classes **ResNet50** Backbone. Only the final predictor is replaced to not predict the 91 classes
present in the coco-dataset the model is trained to but the (currently) 1 category it should detect. present in the coco-dataset the model is trained to but the (currently) 1 category it should detect.
## Long-Term and major ToDos:
* implement gui to correct bounding boxes
* implement reinforced learning
## Data preparation ## Data preparation
### Data structure ### Data structure
The algorithm learns patterns based on **.png-images** and corresponding bounding boxes The algorithm learns patterns based on **.png-images** and corresponding bounding boxes
@ -24,7 +28,6 @@ Use the script **./data/train_test_split.py** to split the original .csv file in
training and one for testing (both also stored in ./data/dataset). training and one for testing (both also stored in ./data/dataset).
### ToDos: ### ToDos:
* FIX: name of generated png images. HINT: {XXX:6.0f}.replace(' ', '0')
* 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.
## model.py ## model.py
@ -51,13 +54,13 @@ im2 = ImageOps.grayscale(im1)
* check other pretrained models from torchvision.models.detection, e.g. fasterrcnn_resnet50_fpn_v2 * check other pretrained models from torchvision.models.detection, e.g. fasterrcnn_resnet50_fpn_v2
## dataset.py ## datasets.py
Contains custom datasets and dataloader. These are based on the images that are stored in Contains custom datasets and dataloader functions and classes.
./data/dataset.
### ToDos: ### ToDos:
* load/compute spectrogram directly and perform signal detection. E.g. spectrogram calculation as part of __getitem__ * load/compute spectrogram directly and perform signal detection. E.g. spectrogram calculation as part of __getitem__
## config.py ## config.py
Containes Hyperparameters used by the scripts. Containes Hyperparameters used by the scripts.
@ -77,14 +80,14 @@ gradient tracking) is computed and used to infer whether the model is better tha
of the previous epochs. If the new model is the best model, the model.state_dict is saved in of the previous epochs. If the new model is the best model, the model.state_dict is saved in
./model_outputs as best_model.pth. ./model_outputs as best_model.pth.
After training a final validation is performed where images showing both, the predicted and the
true bounding boxes in an image. Images are stored in ./inference_output/dataset.
### ToDos: ### ToDos:
## inference.py ## inference.py
Currently, this code performs predictions based in the test dataset (img and corresponding csv file). Script is used to infere unknown .png images. Results are stored in ./inference_output/<Dataset-Name>
However, this code shall be used to infer totally unknown images. Prediction results are ilustrated
and stored in ./inference_output
### ToDo: ### ToDo:
* implement path where no csv file is needed...