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README.md

@@ -35,8 +35,7 @@ This repo is a cleaned version of the original LAPIS release -- see
 
 ## Contents
 
-- **`images/`**: all 11,723 images as plain `.jpg` files, flat (no
-  subfolders). Download this folder either via the "Files and
+- **`images/`**: contains 2 folders, figurative and abstract, with the 11,723 images as plain `.jpg` files, split into figurative and abstract works according to the `2_styles` label. Download this folder either via the "Files and
   versions" tab above, `git clone`, or `huggingface_hub.snapshot_download`
   (see below).
 - **`giaa/{train,validation,test}.csv`**: one row per image, aggregated
@@ -52,7 +51,9 @@ This repo is a cleaned version of the original LAPIS release -- see
 ## Using the data without Python
 
 Download `images/`, `giaa/*.csv` and/or `piaa/piaa.csv` directly from this
-page (or `git clone` the whole repo). `image_filename` in the metadata files can be used to match metadata to the corresponding image in `images/`.
+page (or `git clone` the whole repo). 
+The `path` column (e.g.
+`figurative/jan-matejko_....jpg`) is exactly where to find that row's image inside `images/`. The `image_filename` column is the image's bare filename, useful for matching a row across different tables.
 
 ## Using the data for ML
 ```python
@@ -89,7 +90,7 @@ class LapisDataset(torch.utils.data.Dataset):
 
     def __getitem__(self, i):
         row = self.table[i]
-        image = Image.open(self.images_dir / row["image_filename"]).convert("RGB")
+        image = Image.open(self.images_dir / row["path"]).convert("RGB")
         return self.transform(image), row[self.rating_column]
 
 transform = transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor()])