latest changes

README.md

@@ -9,8 +9,6 @@ modalities as inputs: [dronescapes-2024](https://huggingface.co/datasets/Meehai/
 
 # 1. Downloading the data
 
-## Option 1. Download the pre-processed dataset from HuggingFace repository
-
 ```
 git lfs install # Make sure you have git-lfs installed (https://git-lfs.com)
 git clone https://huggingface.co/datasets/Meehai/dronescapes
@@ -22,20 +20,16 @@ Note: the dataset has about 200GB, so it may take a while to clone it.
 
 As per the split from the paper:
 
-<details>
 <summary> Split </summary>
-<img src="split.png">
-</details>
+<img src="split.png" width="500px">
 
-The data is in `data/*` (see the `ls` call above, it should match even if you download from huggingface).
+The data is in the `data*` directory with 1 sub-directory for each split above (and a few more variants).
 
-## 2.1 Using the provided viewer
+The simplest way to explore the data is to use the [provided notebook](scripts/dronescapes_viewer.ipynb). Upon running
+it, you should get a collage with all the default tasks, like this:
 
 ![Collage](collage.png)
 
-The simplest way to explore the data is to use the [provided notebook](scripts/dronescapes_viewer.ipynb). Upon running
-it, you should get a collage with all the default tasks, like the picture at the top.
-
 For a CLI-only method, you can use the provided reader as well:
 
 ```
@@ -118,7 +112,9 @@ python scripts/evaluate_semantic_segmentation.py y_dir gt_dir -o results.csv --c
 <summary> Script explanation </summary>
 The script is a bit convoluted, so let's break it into parts:
 
-- `y_dir` and `gt_dir` Two directories of .npz files in the same format as the dataset (y_dir/1.npz, gt_dir/55.npz etc.)
+- `y_dir` and `gt_dir` Two directories of .npz files in the same format as the dataset
+  - y_dir/1.npz, ..., y_dir/N.npz
+  - gt_dir/1.npz, ..., gt_dir.npz
 - `classes` A list of classes in the order that they appear in the predictions and gt files
 - `class_weights` (optional, but used in paper) How much to weigh each class. In the paper we compute these weights as
 the number of pixels in all the dataset (train/val/semisup/test) for each of the 8 classes resulting in the numbers
@@ -134,7 +130,7 @@ outputs one csv file with predictions for each npz file, the scenes are used for
 
 ```
 python scripts/evaluate_semantic_segmentation.py \
-  data/test_set_annotated_only/semantic_mask2former_swin_mapillary_converted/ \
+  data/test_set_annotated_only/semantic_mask2former_swin_mapillary_converted/ \ # Mask2Former example, use yours here!
   data/test_set_annotated_only/semantic_segprop8/ \
   -o results.csv \
   --classes land forest residential road little-objects water sky hill \