From e23301c0d184d0695a9d735eda573affcd972d0e Mon Sep 17 00:00:00 2001
From: Till Raab <till.raab@uni-tuebingen.de>
Date: Fri, 27 Oct 2023 13:12:24 +0200
Subject: [PATCH] README update

---
 README.md | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index e15edb3..a7333d6 100644
--- a/README.md
+++ b/README.md
@@ -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 
 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 structure
 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).
 
 ### 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.
 
 ## model.py
@@ -51,13 +54,13 @@ im2 = ImageOps.grayscale(im1)
 
 * check other pretrained models from torchvision.models.detection, e.g. fasterrcnn_resnet50_fpn_v2
 
-## dataset.py
-Contains custom datasets and dataloader. These are based on the images that are stored in 
-./data/dataset. 
+## datasets.py
+Contains custom datasets and dataloader functions and classes.  
 
 ### ToDos:
 * load/compute spectrogram directly and perform signal detection. E.g. spectrogram calculation as part of __getitem__
 
+
 ## config.py
 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 
 ./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:
 
 ## inference.py
-Currently, this code performs predictions based in the test dataset (img and corresponding csv file).
-However, this code shall be used to infer totally unknown images. Prediction results are ilustrated 
-and stored in ./inference_output
+Script is used to infere unknown .png images. Results are stored in ./inference_output/<Dataset-Name>
 
 ### ToDo:
-* implement path where no csv file is needed...