Update README.md

README.md

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 ---
 license: cc-by-nc-4.0
+task_categories:
+  - video-classification
+  - object-detection
+  - visual-question-answering
+language:
+  - en
+tags:
+  - egocentric-video
+  - activity-recognition
+  - hand-object-interaction
+  - segmentation
+  - relation-extraction
+  - vision-language-models
+  - benchmark
+  - coffee
+pretty_name: BARISTA
+size_categories:
+  - 100<n<1K
 ---
+
+# BARISTA
+
+BARISTA is a densely annotated egocentric video dataset of coffee preparation, designed for unified benchmarking of vision-language models across spatial, temporal, relational, and procedural understanding tasks.
+
+The dataset contains **185 egocentric videos** (~4.4 hours, 30 FPS, 1280×720 to 1920×1080) covering three coffee preparation methods: **capsule machines**, **portafilter machines**, and **fully automatic machines**. Videos were recorded in controlled indoor setups using iPhones, Apple Vision Pro, RayBan Meta 3, and RayBan Wayfarer smart glasses.
+
+## Dataset structure
+
+Each video is stored in its own directory:
+
+```
+<video_id>/
+    coco_annotation.json   # COCO-style annotations (masks, bboxes, attributes, relations, activities)
+    video.mp4              # raw video
+```
+
+`coco_annotation.json` follows the COCO format extended with additional top-level keys:
+
+| Key | Description |
+|-----|-------------|
+| `annotations` | Per-frame instance annotations. Fields: `id`, `image_id` (0-based frame index), `object_id` (UUID), `bbox` ([x, y, w, h]), `segmentation` (COCO RLE with `counts` and `size`), `area` |
+| `attributes` | Segment-level key-value attributes per object. Fields: `id`, `object_id`, `attribute_type` (e.g. `"color"`, `"state"`), `value`, `image_ranges` (list of `{image_id_start, image_id_end}`) |
+| `relations` | Directed typed relations between object pairs. Fields: `id`, `source_object_id`, `target_object_id`, `relation_type` (e.g. `"position"`, `"human_actions"`), `value`, `image_ranges` |
+| `categories` | Object categories. Fields: `id` (UUID), `name`. |
+| `activities` | Fine-grained verb+noun activity segments. Fields: `id`, `display_name`, `activity_class_id` (UUID), `image_range` (`{image_id_start, image_id_end}`) |
+| `process_steps` | High-level process step segments. Same fields as `activities` |
+| `object_id_to_category_id` | Map from object UUID to category UUID (needed to resolve annotation `object_id` → category) |
+| `video_metadata` | List with one entry. Fields: `document_id`, `video_index`, `width`, `height`, `frame_count`, `fps`, `length_in_ms`, `recording_device_type`, `recording_device_version` |
+| `split` | Dataset split: `"train"` or `"test"` |
+
+## Loading the data and running evaluations
+
+See the [project repository](https://github.com/Ramblr-GmbH/BARISTA) for the dataset loader and the VLM benchmarking pipeline.