README initialized

README.md

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+# efishSignalDetector
+
+Welcome to the efishSignalDetector, a **neural network** framework adapted 
+for detecting electrocommunication signals in wavetype electric fish based on 
+spectrogram images. The model itself is a pretrained **FasterRCNN** Model with a 
+**ResNet50** Backbone. Only the final predictor is replaced to not predict 91 classes 
+the coco-dataset it is trained to but the (currently) 1 category it should detect.
+
+## Data preparation
+### Data structure
+The algorithm learns patterns based on **.png-images** and corresponding bounding boxes 
+which are stored in **.csv-files**.
+* image name includes the file it is derived form as well as time and frequency bounds
+* image size defined in *config.py* with size (IMG_SIZE=(7, 7)) and dpi (IMG_DPI=256).
+* .cvs file where each row represents one assigned signal:
+  * image: image name
+  * x0, x1, y0, y1: image coordinates of bounding box
+  * t0, t1, f0, f1: time and frequency boinding box
+* .png images and .csv file is stored in ./data/dataset
+
+### Test-train-split
+
+Use the script **./data/train_test_split.py** to split the original .csv file into one for
+training and one for testing (both also stored in ./data/dataset).
+
+### ToDos:
+* FIX: name of generated png images. HINT {XXX:6.0f}.replace(' ', '0')
+* transfere images from ./data/train to ./data/dataset
+
+## model.py
+
+Contains the model and adjustements.
+
+### ToDos:
+* replace backbone entry to not take RGB input, but grayscale images or even spectrograms.
+~~~ py
+# Hint:
+model.backbone.body.conv1 = torch.nn.Conv2d(1, 64,
+                            kernel_size=(7, 7), stride=(2, 2),
+                            padding=(3, 3), bias=False).requires_grad_(True)
+~~~
+~~~ py
+# Hint:
+from PIL import Image, ImageOps   
+
+im1 = Image.open(img_path) 
+im2 = ImageOps.grayscale(im1) 
+~~~
+
+* check other pretrained models from torchvision.models.detection, e.g. fasterrcnn_resnet50_fpn_v2
+
+## config.py
+
+Containes Hyperparameters used by the scripts.
+
+## ToDos:
+* replace TRAIN_DIR with DATA_DIR everywhere !!!
+
+### custom_utils.py
+Classes and functions to save models and store loss values for later illustration.
+Also includes helper functions...
+
+### train.py
+Code training the model using the stored images in ./data/dataset and the .csv files
+containing the bounding boxes meant for training. For each epoch test-loss (without 
+gradient tracking) is computed and used to infer whether the model is better than the one
+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.
+
+
+### inference.py
+
+
+

train.py

@@ -79,8 +79,6 @@ if __name__ == '__main__':
 
     train_loss_hist = Averager()
     val_loss_hist = Averager()
-    # train_itr = 1
-    # val_itr = 1
     train_loss = []
     val_loss = []