Improve dataset card and add preview config

README.md

@@ -1,6 +1,17 @@
 ---
+pretty_name: Doom Frame Dataset
+tags:
+- doom
+- vizdoom
+- reinforcement-learning
+- imitation-learning
+- webdataset
 configs:
-- config_name: default
+- config_name: preview
+  data_files:
+  - split: train
+    path: data/train-000000.tar
+- config_name: full
   data_files:
   - split: train
     path: data/train-*.tar
@@ -8,7 +19,124 @@ configs:
 
 # DoomFrameDataset
 
-ViZDoom frame/action dataset packaged as WebDataset tar shards.
+DoomFrameDataset is a ViZDoom frame-action dataset generated from policy rollouts. It is packaged as WebDataset tar shards for streaming training, imitation learning, behavior cloning, and offline reinforcement-learning experiments.
+
+The dataset contains RGB game frames paired with the action selected by the rollout policy and per-step metadata such as reward, episode id, step id, terminal flag, and value estimate.
+
+## Dataset Size
+
+| Config | Files | Samples | Intended use |
+| --- | ---: | ---: | --- |
+| `preview` | 1 shard | ~79k | Hugging Face preview and quick sanity checks |
+| `full` | 31 shards | 2,398,745 | Training and full streaming reads |
+
+The packaged dataset is about 68 GB.
+
+## Files
+
+```text
+data/
+  train-000000.tar
+  train-000001.tar
+  ...
+  train-000030.tar
+action_map.json
+README.md
+```
+
+Each tar shard contains paired files with the same numeric key:
+
+```text
+000000000000.png
+000000000000.json
+000000000001.png
+000000000001.json
+...
+```
+
+The PNG is the game frame. The JSON is the metadata for that frame.
+
+## Sample Metadata
+
+```json
+{
+  "action_id": 1,
+  "action_name": "TURN_RIGHT",
+  "action_vector": [0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
+  "curriculum_level": 1,
+  "done": false,
+  "episode": 1,
+  "frame_path": "frames/episode_001/step_000000.png",
+  "global_step": 0,
+  "reward": 0.0,
+  "source_frame_path": "frames/episode_001/step_000000.png",
+  "step": 0,
+  "value": 1.7968196868896484,
+  "webdataset_key": "000000000000"
+}
+```
+
+See `action_map.json` for the full action id, action name, and action vector mapping.
+
+## Load The Preview Config
+
+Use `preview` when you only want to verify the dataset or inspect examples in the Hugging Face Dataset Viewer.
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset(
+    "brahmandam/DoomFrameDataset",
+    "preview",
+    split="train",
+    streaming=True,
+)
+
+sample = next(iter(ds))
+print(sample.keys())
+print(sample["json"])
+image = sample["png"]
+```
+
+## Stream The Full Dataset
+
+Use `full` for training.
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset(
+    "brahmandam/DoomFrameDataset",
+    "full",
+    split="train",
+    streaming=True,
+)
+
+for sample in ds:
+    image = sample["png"]
+    metadata = sample["json"]
+    action_id = metadata["action_id"]
+    break
+```
+
+You can also read the shards directly with WebDataset:
+
+```python
+import webdataset as wds
+
+urls = "https://huggingface.co/datasets/brahmandam/DoomFrameDataset/resolve/main/data/train-{000000..000030}.tar"
+
+dataset = (
+    wds.WebDataset(urls)
+    .decode("pil")
+    .to_tuple("png", "json")
+)
+
+image, metadata = next(iter(dataset))
+```
+
+## Notes
+
+The `preview` config intentionally points to a single shard so the Hub can inspect a small part of the dataset without processing the full 68 GB. For training, use the `full` config.
 
-Each sample contains a PNG frame and a paired JSON metadata file with
-the policy action, reward, episode, step, and source frame path.
+This dataset was generated from automated ViZDoom policy rollouts. It should be treated as gameplay observation/action data, not human demonstrations.