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add full readme for imagenet scans.

Konstantin Willeke 1 year ago
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a15aa2d9aa
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      imagenet scans/README.md

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imagenet scans/README.md

@@ -1,10 +1,7 @@
 # Dataset Structure
 
-Below we provide a brief explanation of the dataset structure and how to access all the information contained in them.
 
-Have a look at our white paper for in depth description of the data. [White paper on arXiv](https://arxiv.org/abs/2206.08666)
-
-We provide the datasets in the .zip format. Unzipping them will create two folders **data** and **meta**.
+We provide the imagenet scans in the .zip format. Unzipping them will create two folders **data** and **meta**.
 
 - **data:** includes the variables that were recorded during the experiment. The experimental variables are saved as a collection of numpy arrays. Each numpy array contains the value of that variable at a specific image presentation (i.e. trial). Note that the name of the files does not contain any information about the order or time at which the trials took place in experimental time. They are randomly ordered.
   - **images:** This directory contains NumPy arrays where each single `X.npy` contains the image that was shown to the mouse in trial `X`.
@@ -18,11 +15,7 @@ We provide the datasets in the .zip format. Unzipping them will create two folde
         - `layer.npy`: contains the cortex layer to which neuron belongs to
         - `unit_ids.npy`: contains a unique id for each neuron
     - **statistics:** This directory contains statistics (i.e. mean, median, etc.) of the experimental variables (i.e. behavior, images, pupil_center, and responses).
-      - **Note:** The statistics of the responses are or particular importance, because we provide the deconvolved calcium traces here in the responses.
-      
-        However, for the evaluation of submissions in the competition, we require the responses to be **standardized** (i.e. `r = r/(std_r)`), with the `std` computed across all images on the training set.
-        
-        For more information, please refer to the [**Submission Section**](../submission_tutorial/)
+   
     - **trials:** This directory contains trial-specific meta data. 
         They contain single 1-d NumPy arrays for each trial variable. 
         
@@ -42,21 +35,4 @@ We provide the datasets in the .zip format. Unzipping them will create two folde
         Below are a list of important variables in this directory.
         - `frame_image_id.npy`: contains unique image id. If the image is presented multiple times (which is the case in the test set) this image ID will be present multiple times.
         - `tiers.npy`: contains labels that are used to split the data into *train*, *validation*, and *test* set
-          - The *training* and *validation* split is only present for convenience, and is used by our ready-to-use PyTorch DataLoaders.
-          - The *test* set is used to evaluate the model preformance. In the competition datasets, the responses to all *test* images is withheld.
-          - In the 2 competition datasets, there is the additional tier *final_test*, which contains 100 images and their repetitions. The model performance on these *tiers* will be used to determine the winner of the competition. 
-
-        - `trial_idx.npy`: contains a unique index for each trial. While the true trial index is available for the “pre-training” datasets, it is hidden (i.e. hashed) in the competition datasets. 
-          - The *trial_idx* corresponds to the actual order of image presentations to the mouse. We hide the *trial_idx* in the competition scans (i.e. by hashing them).
-
-
-# Competition Datasets (Sensorium & Sensorium+)
-
-The datasets `26872-17-20` (Sensorium) `27204-5-13` (Sensorium+) are different from the 5 other full datasets in these ways:
-
-- They have 2 types of test images, that reflects how we evaluate the submissions:
-  - **live test images**: These are 100 images, and can be found under the tiers *test*. They are also present in all the pre-training datasets
-  - **final test images**: Another 100 images, that are not present in the other datasets. The tier for these images is * live_test* 
-- The responses to all *test* or *final_test* images is withheld (the response arrays are present, but zeroed out)
-- Information about order of trials is withheld (i.e. the *trial_idx* is hashed)
-- For **Sensorium**, the behavioral variables and eye position are withheld (arrays are present, but zeroed out)
+        - `trial_idx.npy`: contains the index for each trial. It corresponds to the actual order of image presentations to the mouse.