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@@ -24,7 +24,7 @@ We provide the imagenet scans in the .zip format. Unzipping them will create two
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The indices of these arrays correspond to the `.npy` files in **data**. For example:
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```
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# get meta data array
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- image_ids = np.load('./meta/trials/frame_image_id.npy')
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+ image_ids = np.load('./meta/trials/colorframeprojector_image_id.npy')
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# relate meta data with neuronal data
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trial_image_id = image_ids[0]
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@@ -33,6 +33,6 @@ We provide the imagenet scans in the .zip format. Unzipping them will create two
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```
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Below are a list of important variables in this directory.
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- - `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.
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+ - `colorframeprojector_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.
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- `tiers.npy`: contains labels that are used to split the data into *train*, *validation*, and *test* set
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- `trial_idx.npy`: contains the index for each trial. It corresponds to the actual order of image presentations to the mouse.
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