Update README.md

README.md

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 license: mit
 ---
 
-# COIN collection dataset  (**👷 Under Construction 👷**)
+# COIN collection dataset
 
- This is the dataset for our paper "Predicting the Encoding Error of Implicit Neural Representations", currently under anonymous review. It consists of 300,000 small SIREN networks trained to encode square images from the MSCOCO dataset.
+ This is the dataset for our paper ["Predicting the Encoding Error of SIRENs"](https://openreview.net/forum?id=iKPC7N85Pf). It consists of 300,000 small SIREN networks trained to encode images from the MSCOCO dataset.
 
 We will publish a loading script for this dataset soon, but until then, see the following instructions:
 
@@ -31,8 +31,11 @@ This will download around 80Gb of images in `data/mscoco/mscoco`.
 ### SIREN run records
 The sirens are organized into two sub-datasets, `single-architecture` and `many-architecture`. Each `.json.gz` file contains one SIREN per line, which can be loaded as a JSON object. Each SIREN record contains the following fields:
 
-- `config`: The starting configuration of the SIREN training run. The `image_id` corresponds to the filename of the corresponding MSCOCO image. The `image_size` indicates what size the image was downsampled to, using PIL's `resize()` command with `BOX` resampling. The other arguments in `config` specify the arguments to be used in the [COIN](https://github.com/EmilienDupont/coin) training script to reproduce this SIREN run.
-- `psnr_history`: record of the PSNR curve during training time.
+- `config`: The starting configuration of the SIREN training run. contains the following subfields:
+  - `image_id` corresponds to the filename of the corresponding MSCOCO image, as downloaded using `download_mscoco.sh`. e.g. `image_id=123` corresponds to the filename `000000123.png`.
+  - `image_size` indicates what size the image was downsampled to, using PIL's `resize()` function with `BOX` resampling.
+  - The other arguments in `config` specify the arguments to be used in the [COIN training script](https://github.com/EmilienDupont/coin) to reproduce this SIREN run.
+- `psnr_history`: record of the PSNR curve during training time. PSNR is recorded once every 10 training iterations.
 - `best_psnr_history`: Similar to `psnr_history`, but stores the maximum value of `psnr history` seen up until this point during training.
 - `iteration_history`: Parallel to the psnr_history and best_psnr_history; the training iteration at wich those PSNRs are recorded.
 - `hp_bpp`: bits per pixel of the SIREN encoding of the image, with weights stored at half-precision (16-bit floats)