Update README.md

README.md

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 # KonIQ-10k models 
 Deep Learning Models for the KonIQ-10k Image Quality Assessment Database
 
-This is part of the code for the paper ["KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment"](). The included notebooks rely on the [kutils library](https://github.com/subpic/kutils).
+This is part of the code for the paper ["KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment"](). The included notebooks rely on the [kutils library](https://github.com/subpic/kutils). Project data is available for download from [osf.io](https://osf.io/hcsdy/).
 
 ## Overview
 
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 **`train_koncept512.ipynb`**:
 
 - Training and testing code for the KonCept512 model (on KonIQ-10k).
-- Ready-trained model weights for [KonCept512](https://www.dropbox.com/s/7ci22gx5c3c8xo3/bsz32_i1%5B384%2C512%2C3%5D_lMSE_o1%5B1%5D_best_weights.h5?dl=1&raw=1
-).
+- Ready-trained model weights for [KonCept512](https://osf.io/uznf8/download) and [KonCept224](https://osf.io/cxtyp/download).
 
 **`train_deeprn.ipynb`**
 
 - Reimplementation of the [DeepRN](https://www.uni-konstanz.de/mmsp/pubsys/publishedFiles/VaSaSz18.pdf) model trained on KonIQ-10k, following the advice of the original author, Domonkos Varga.
-- Re-trained model weights (on SPP features) are available [here](https://www.dropbox.com/s/z6hpj66et6o8rjr/i1%5B768%2C1024%2C3%5D_lSPP_o1%5B2048%5D_r2.h5?dl=1&raw=1).
-- The features extracted from KonIQ-10k are available [here](https://www.dropbox.com/s/1c7hkxrhlnzphjg/bsz128_i1%5B18432%5D_imsz%5B768%2C%201024%5D_lcustom_l_o1%5B5%5D_best_weights.h5?dl=1&raw=1).
+- Re-trained model weights (on SPP features) are available [here](https://osf.io/avyd5/download).
+- The features extracted from KonIQ-10k are available [here](https://osf.io/y6brn/download).
 
 **`metadata/koniq10k_distributions_sets.csv`**