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 # AT_BlueFin
 
+*Resnet-50 [1] trained on the Acoustic Trends Blue Fin Library open access dataset [2]*
+
+## Dependencies
+`torch` `torchvision` `scipy` `soundfile` `numpy` `pandas` `tqdm` `argparse`
+
+install with `pip install pacakge-name`
+
+## Project description
+
+The CNN is ran using the run_model.py script.
+For interface description run `python run_model.py --help`
+
+The script forwards the CNN over a given list of sound files and writes a .csv file with chunk and class wise predictions.
+The CNN was trained as a multi-label classifier over the dataset proposed by Miller et al. [2] (every station except kerguelen 2005).
+
+Known classes are :
+Bp_20Plus, Bp-Downsweep,  Bp_20Hz,  Bm_Ant-A, Bm_Ant-Z, Bm_Ant-B, and Bm_D.
+Bp standing for fin whale (Balaenoptera Physalus) and Bm for blue whale (Balaenoptera Musculus)
+
+The model reached an 0.66 mAP score over the test set (kerguelen 2005) and 0.89 over the train set (remaining data).
+
+### Refererences
+[1] He, Kaiming, et al. "Deep residual learning for image recognition." arXiv preprint arXiv:1512.03385 (2015).
+
+[2] Miller, Brian S., et al. "An open access dataset for developing automated detectors of Antarctic baleen whale sounds and performance evaluation of two commonly used detectors." Scientific Reports 11.1 (2021): 1-18.
+
+
+### Contact info
+paul.best@univ-tln.fr