t pushMerge branch 'main' of https://huggingface.co/datasets/AstroCompress/SBI-16-3D into main

README.md

@@ -50,6 +50,37 @@ pip install datasets astropy
 
 There are two datasets: `tiny` and `full`, each with `train` and `test` splits. The `tiny` dataset has 2 4D images in the `train` and 1 in the `test`. The `full` dataset contains all the images in the `data/` directory.
 
+
+## Local Use (RECOMMENDED)
+
+Alternatively, you can clone this repo and use directly without connecting to hf:
+
+```bash
+git clone https://huggingface.co/datasets/AstroCompress/SBI-16-3D
+```
+
+```bash
+git lfs pull
+```
+
+Then `cd SBI-16-3D` and start python like:
+
+```python
+from datasets import load_dataset
+dataset = load_dataset("./SBI-16-3D.py", "tiny", data_dir="./data/")
+ds = dataset.with_format("np")
+```
+
+Now you should be able to use the `ds` variable like:
+
+```python
+ds["test"][0]["image"].shape # -> (5, 2048, 2048)
+```
+
+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:
@@ -79,29 +110,4 @@ dst = dataset.with_format("torch", columns=["image"], dtype=torch.uint8)
 
 # or pandas
 dsp = dataset.with_format("pandas", columns=["image"], dtype=numpy.uint8)
-```
-
-## Local Use
-
-Alternatively, you can clone this repo and use directly without connecting to hf:
-
-```bash
-git clone https://huggingface.co/datasets/AstroCompress/SBI-16-3D
-```
-
-Then `cd SBI-16-3D` and start python like:
-
-```python
-from datasets import load_dataset
-dataset = load_dataset("./SBI-16-3D.py", "tiny", data_dir="./data/")
-ds = dataset.with_format("np")
-```
-
-Now you should be able to use the `ds` variable like:
-
-```python
-ds["test"][0]["image"].shape # -> (5, 2048, 2048)
-```
-
-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.
-
+```