small updates to the README

README.md

@@ -6,7 +6,7 @@ tags:
 - compression
 - images
 dataset_info:
-  config_name: tiny
+- config_name: full
   features:
   - name: image
     dtype:
@@ -18,13 +18,32 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 307620692
+    num_bytes: 3509045373
+    num_examples: 120
+  - name: test
+    num_bytes: 970120060
+    num_examples: 32
+  download_size: 2240199274
+  dataset_size: 4479165433
+- config_name: tiny
+  features:
+  - name: image
+    dtype:
+      image:
+        mode: I;16
+  - name: telescope
+    dtype: string
+  - name: image_id
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 307620695
     num_examples: 10
   - name: test
-    num_bytes: 168984694
+    num_bytes: 168984698
     num_examples: 5
   download_size: 238361934
-  dataset_size: 476605386
+  dataset_size: 476605393
 ---
 
 # GBI-16-2D-Legacy Dataset
@@ -36,10 +55,10 @@ GBI-16-2D-Legacy is a Huggingface `dataset` wrapper around a compression dataset
 You first need to install the `datasets` and `astropy` packages:
 
 ```bash
-pip install datasets astropy
+pip install datasets astropy PIL
 ```
 
-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.
+There are two datasets: `tiny` and `full`, each with `train` and `test` splits. The `tiny` dataset has 5 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
 
@@ -60,7 +79,8 @@ Then in your python script:
 
 ```python
 from datasets import load_dataset
-dataset = load_dataset("AstroCompress/GBI-16-2D-Legacy", "tiny")
+dataset = load_dataset("AstroCompress/GBI-16-2D-Legacy", "tiny", \ 
+                       trust_remote_code=True)
 ds = dataset.with_format("np")
 ```
 
@@ -76,15 +96,15 @@ Then `cd SBI-16-3D` and start python like:
 
 ```python
 from datasets import load_dataset
-dataset = load_dataset("./GBI-16-2D-Legacy", "tiny", data_dir="./data/")
+dataset = load_dataset("./GBI-16-2D-Legacy.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 # -> (9, 2048, 2048)
+ds["test"][0]["image"].shape # -> (4200, 2154)
 ```
 
-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.
+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. If you run into issues with downloading the `full` dataset, try changing `num_proc` in `load_dataset` to >1 (e.g. 5). You can also set the `writer_batch_size` to ~10-20.