Datasets:
Languages:
English
Size:
10K<n<100K
Tags:
multi-object-tracking
referring-expression
CARLA
autonomous-driving
text-guided-tracking
Synthetic
License:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: ValueError
Message: Invalid string class label carla_referring_target_evaluation_set@52807245ef0916ce0f88f6ab2ec0e4afedf9d9be
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2240, in __iter__
example = _apply_feature_types_on_example(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2157, in _apply_feature_types_on_example
encoded_example = features.encode_example(example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2152, in encode_example
return encode_nested_example(self, example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1437, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1460, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1143, in encode_example
example_data = self.str2int(example_data)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1080, in str2int
output = [self._strval2int(value) for value in values]
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1101, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label carla_referring_target_evaluation_set@52807245ef0916ce0f88f6ab2ec0e4afedf9d9beNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
CARLA Referring Target Evaluation Set
A synthetic benchmark for referring expression tracking — the task of tracking a single described target (e.g. "red sedan") while ignoring all other vehicles in the scene.
Unlike standard MOT datasets that evaluate all objects equally, this benchmark tests whether a tracker can isolate and continuously follow exactly the object that matches a natural language description, across challenging conditions including weather variation, lighting changes, camera motion, and the presence of visually similar distractors.
Generated using the CARLA simulator (Towns 10HD, 03, 05).
Dataset Structure
The dataset is provided as a single zip file: eval_scenarios.zip.
After extracting, the structure is:
eval_scenarios/
master_index.json # metadata for all 24 scenarios
clear_day_baseline/
images/ # per-frame PNG images (1920×1080)
labels/ # per-frame YOLO-style labels with is_target flags
gt.json # ground truth with prompt, bboxes, target flags
overcast/
heavy_rain/
...
Each gt.json contains:
prompt: the referring expression (e.g."red sedan")- per-frame bounding boxes for all vehicles
is_target: true/falseflag distinguishing the referred object from distractors
Scenarios
| # | Name | Camera | Condition |
|---|---|---|---|
| 1 | clear_day_baseline |
static | Clear sky, high sun |
| 2 | overcast |
static | Heavy cloud cover |
| 3 | heavy_rain |
loose-follow | Rain, wet road reflections |
| 4 | dusk_golden_hour |
loose-follow | Low sun, warm directional light |
| 5 | night |
loose-follow | Night lighting |
| 6 | dense_fog |
loose-follow | Low visibility fog |
| 7 | color_confusable |
loose-follow | Other red vehicles as distractors |
| 8 | same_color_diff_class |
loose-follow | Same color, different vehicle class |
| 9 | high_density |
static | Dense mixed traffic |
| 10 | high_altitude |
static | Elevated camera angle |
| 11 | low_altitude_steep |
loose-follow | Low steep camera angle |
| 12 | side_follow |
loose-follow | Side-on perspective |
| 13 | town03_suburban |
static | Suburban map (Town03) |
| 14 | town05_highway |
loose-follow | Highway map (Town05) |
| 15 | multiple_red_sedans |
static | 3 target vehicles simultaneously |
| 16 | long_sequence |
static | Extended duration |
| 17 | dense_urban_traffic |
static | Dense urban scene |
| 18–24 | follow_base / follow_variant_* |
follow | Follow-camera variants |
Evaluation Metrics
Evaluated with the companion eval_carla.py script:
| Metric | Description |
|---|---|
| Semantic Precision | Fraction of tracked detections that are actually the target |
| Semantic Recall | Fraction of target frames where the target was tracked |
| Prompt Coverage Ratio | How consistently the described object is tracked overall |
| Distractor Confusion Rate | How often a non-target was incorrectly tracked as the target |
| Semantic ID Switches (SID) | Times the tracker switched from the correct target to a distractor |
Usage
# Download
hf download azzy13/carla_referring_target_evaluation_set eval_scenarios.zip \
--repo-type dataset --local-dir dataset/carla_eval/
unzip dataset/carla_eval/eval_scenarios.zip -d dataset/carla_eval/
# Evaluate
python3 eval/eval_carla.py \
--carla_scenarios dataset/carla_eval/eval_scenarios \
--tracker clip --fp16
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