Update README.md

README.md

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----
-license: cc-by-4.0
----
+---
+tags:
+- computer-vision
+- audio
+- keypoint-detection
+- animal-behavior
+- multi-modal
+- jsonl
+---
+
+# Bird3m Dataset
+
+## Dataset Description
+
+This dataset contains multi-modal data for bird tracking and behavior analysis, primarily focused on Zebrafinches (based on category names in the source data). Each data entry corresponds to a specific bird instance within a video frame.
+
+The data includes:
+
+*   3D keypoints derived from multi-view reconstruction.
+*   2D keypoints and bounding boxes for top, side, and back camera views.
+*   Information about the video frame and associated processed audio/radio data files.
+*   Metadata about the bird and the experimental setup.
+*   Linked vocalization events associated with the specific bird in the frame.
+
+This dataset is designed to facilitate research in areas such as multi-view 3D reconstruction, multi-modal data fusion, and fine-grained animal behavior analysis.
+
+[More details about the dataset origin, collection process, and purpose can be added here.]
+
+## Dataset Structure
+
+The dataset is structured into splits based on the `split` field in the original data. The standard splits are `train`, `validation` (mapped from `val`), and `test`.
+
+Each split is a standard Hugging Face `Dataset` object. Each row in the dataset corresponds to a single detected bird instance in a single frame, with associated multi-modal data.
+
+```python
+# Example of accessing splits
+from datasets import load_dataset
+
+dataset = load_dataset("anonymous-submission000/bird3m") # Replace with your actual repo_id
+
+train_dataset = dataset["train"]
+validation_dataset = dataset["validation"] # Or whatever your split is named if not 'val'
+test_dataset = dataset["test"]
+```
+
+## Dataset Fields
+
+Each example in the dataset has the following fields:
+
+*   `bird_id` (`string`): A unique identifier for the specific bird instance within the context of its frame (e.g., "bird_1", "bird_2").
+*   `back_bbox_2d` (`Sequence[float64]`): 2D bounding box coordinates for the bird instance in the **back** camera view. Format is likely `[x, y, width, height]` or `[x_min, y_min, x_max, y_max]`.
+*   `back_keypoints_2d` (`Sequence[float64]`): 2D keypoint coordinates and visibility flags for the bird instance in the **back** camera view. Format is likely `[x1, y1, v1, x2, y2, v2, ...]`, where `v` is the visibility status (e.g., 0: not labeled, 1: labeled but not visible, 2: visible and labeled).
+*   `back_view_boundary` (`Sequence[int64]`): Boundary coordinates defining the relevant area for the **back** view within the full image dimensions. Format is likely `[x, y, width, height]`.
+*   `bird_name` (`string`): The biological or specific identifier assigned to the bird (e.g., "b13k20_f", "b13o15_m", "dead", "2U7a_j").
+*   `video_name` (`string`): Identifier for the original video file the frame belongs to (e.g., "BP_2020-10-13_19-44-38_564726_0240000").
+*   `frame_name` (`string`): Filename of the individual frame (e.g., "img00961.png").
+*   `frame_path` (`Image`): Path to the image file (`.png`) for this specific frame. When accessing this field using the `datasets` library, it will automatically load the image.
+*   `keypoints_3d` (`Sequence[Sequence[float64]]`): 3D keypoint coordinates for the bird instance. Each inner sequence is a 3D point, likely `[x, y, z]`.
+*   `radio_path` (`Value(dtype='binary')`): Path to an associated radio data file (`.npz`). Stored as generic binary data. You would need external libraries (like `numpy`) to load/interpret the content of the `.npz` file after reading the path.
+*   `reprojection_error` (`Sequence[float64]`): Reprojection error values, likely corresponding to each 3D keypoint.
+*   `side_bbox_2d` (`Sequence[float64]`): 2D bounding box for the **side** camera view. (Format similar to `back_bbox_2d`).
+*   `side_keypoints_2d` (`Sequence[float64]`): 2D keypoint coordinates and visibility for the **side** camera view. (Format similar to `back_keypoints_2d`).
+*   `side_view_boundary` (`Sequence[int64]`): View boundary coordinates for the **side** view. (Format similar to `back_view_boundary`).
+*   `backpack_color` (`string`): Color of the backpack tag on the bird (e.g., "purple", "yellow", "red").
+*   `experiment_id` (`string`): Simplified experiment identifier (e.g., "copExpBP03", "juvExpBP05").
+*   `split` (`string`): Dataset split for this example ("train", "validation", "test").
+*   `top_bbox_2d` (`Sequence[float64]`): 2D bounding box for the **top** camera view. (Format similar to `back_bbox_2d`).
+*   `top_keypoints_2d` (`Sequence[float64]`): 2D keypoint coordinates and visibility for the **top** camera view. (Format similar to `back_keypoints_2d`).
+*   `top_view_boundary` (`Sequence[int64]`): View boundary coordinates for the **top** view. (Format similar to `back_view_boundary`).
+*   `video_path` (`Video`): Path to the video clip file (`.mp4`) containing this frame. When accessing this field, it will automatically load the video object.
+*   `acc_ch_map` (`{'0': string, ...}`): Dictionary mapping accelerometer channel indices (as strings) to bird identifiers or descriptions.
+*   `acc_sr` (`float64`): Accelerometer sampling rate in Hz.
+*   `has_overlap` (`bool`): Boolean indicating whether the accelerometer event overlaps with the vocalization event (based on previous modification logic).
+*   `mic_ch_map` (`{'0': string, ...}`): Dictionary mapping microphone channel indices (as strings) to microphone names or descriptions.
+*   `mic_sr` (`float64`): Microphone sampling rate in Hz.
+*   `acc_path` (`Audio`): Path to the processed accelerometer audio file (`.wav`). When accessing this field, it will automatically load the audio signal.
+*   `mic_path` (`Audio`): Path to the processed microphone audio file (`.wav`). When accessing this field, it will automatically load the audio signal.
+*   `vocalization` (`Sequence[Dict]`): A list (Sequence) containing dictionaries (Dict) representing vocalization events associated with this bird in this frame. Each dictionary within this list has the following fields:
+    *   `overlap_type` (`string`): Describes the type or confidence of overlap/attribution (mapped from `attribution_confidence_step2`).
+    *   `has_bird` (`bool`): Boolean indicating if this vocalization event was attributed to a bird (mapped from `is_attributed_to_bird`).
+    *   `2ddistance` (`bool`): Boolean indicating if the 2D keypoint match distance for this vocalization attribution was within a certain threshold (likely < 20px, mapped from `keypoint_match_is_close_lt_20px`).
+    *   `small_2ddistance` (`float64`): The minimum 2D keypoint match distance associated with this vocalization event attribution (mapped from `keypoint_match_min_distance_px`).
+    *   `voc_metadata` (`Sequence[float64]`): Likely the onset and offset times of the vocalization event within the associated audio clip (e.g., `[onset_sec, offset_sec]`). Mapped from `vocalization_onset_offset_sec_in_clip`.
+
+## How to Use
+
+```python
+from datasets import load_dataset
+import numpy as np # Needed to load .npz radio data
+
+# Load the dataset from the Hub
+dataset = load_dataset("anonymous-submission000/bird3m") # Replace with your actual repo_id
+
+# Access a split
+train_data = dataset["train"]
+
+# Access an example
+example = train_data[0]
+
+# Access fields
+bird_id = example["bird_id"]
+keypoints_3d = example["keypoints_3d"]
+top_bbox = example["top_bbox_2d"]
+vocalizations = example["vocalization"] # This is a list of dicts
+
+# Access multimedia files (they are lazy-loaded)
+image = example["frame_path"] # This loads the PIL Image
+video = example["video_path"] # This loads a Video object
+mic_audio = example["mic_path"] # This loads the Audio signal (dict with 'array' and 'sampling_rate')
+acc_audio = example["acc_path"] # This loads the Audio signal
+
+# To access the audio arrays and sampling rates:
+mic_array = mic_audio['array']
+mic_sr = mic_audio['sampling_rate']
+acc_array = acc_audio['array']
+acc_sr_actual = acc_audio['sampling_rate'] # Note: This comes from the file metadata, compare with example['acc_sr']
+
+# To access the binary radio data (if you know how to parse it, e.g., with numpy)
+radio_bytes = example["radio_path"] # This loads the binary content
+# Example (assuming it's a numpy .npz file):
+# try:
+#     from io import BytesIO
+#     radio_data = np.load(BytesIO(radio_bytes))
+#     # Access data inside the npz file, e.g., radio_data['some_key']
+#     print("Radio data keys:", list(radio_data.keys()))
+# except Exception as e:
+#     print(f"Could not load radio data: {e}")
+
+
+print(f"Bird ID: {bird_id}")
+print(f"Number of 3D keypoints: {len(keypoints_3d)}")
+print(f"Top Bounding Box: {top_bbox}")
+print(f"Number of vocalization events: {len(vocalizations)}")
+
+if vocalizations:
+    first_vocal = vocalizations[0]
+    print(f"First vocal event metadata: {first_vocal.get('voc_metadata')}")
+    print(f"First vocal event overlap type: {first_vocal.get('overlap_type')}")
+
+# Example of how to get a slice of the audio clip based on voc_metadata
+# if vocalizations and mic_sr is not None:
+#     onset, offset = vocalizations[0]['voc_metadata']
+#     onset_sample = int(onset * mic_sr)
+#     offset_sample = int(offset * mic_sr)
+#     vocal_audio_clip = mic_array[onset_sample:offset_sample]
+#     print(f"Duration of first vocal clip: {offset - onset:.3f} seconds")
+#     print(f"Shape of first vocal audio clip: {vocal_audio_clip.shape}")
+
+```