Update README.md

README.md

@@ -21,35 +21,16 @@ pip install datasets astropy
 
 There are two datasets: `tiny` and `full`, each with `train` and `test` splits. The `tiny` dataset has 2 2D images in the `train` and 1 in the `test`. The `full` dataset contains all the images in the `data/` directory.
 
-## Use from Huggingface Directly
+## Local Use (RECOMMENDED)
 
-To directly use from this data from Huggingface, you'll want to log in on the command line before starting python:
+Alternatively, you can clone this repo and use directly without connecting to hf:
 
 ```bash
-huggingface-cli login
-```
-
-or
-
-```
-import huggingface_hub
-huggingface_hub.login(token=token)
-```
-
-Then in your python script:
-
-```python
-from datasets import load_dataset
-dataset = load_dataset("AstroCompress/GBI-16-2D", "tiny")
-ds = dataset.with_format("np")
+git clone https://huggingface.co/datasets/AstroCompress/GBI-16-2D
 ```
 
-## Local Use
-
-Alternatively, you can clone this repo and use directly without connecting to hf:
-
 ```bash
-git clone https://huggingface.co/datasets/AstroCompress/GBI-16-2D
+git lfs pull
 ```
 
 Then `cd SBI-16-3D` and start python like:
@@ -67,4 +48,27 @@ ds["test"][0]["image"].shape # -> (TBD)
 ```
 
 Note of course that it will take a long time to download and convert the images in the local cache for the `full` dataset. Afterward, the usage should be quick as the files are memory-mapped from disk.
-
+
+
+## Use from Huggingface Directly
+
+To directly use from this data from Huggingface, you'll want to log in on the command line before starting python:
+
+```bash
+huggingface-cli login
+```
+
+or
+
+```
+import huggingface_hub
+huggingface_hub.login(token=token)
+```
+
+Then in your python script:
+
+```python
+from datasets import load_dataset
+dataset = load_dataset("AstroCompress/GBI-16-2D", "tiny")
+ds = dataset.with_format("np")
+```