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: CastError
Message: Couldn't cast
schema_version: string
fingerprint: string
detections: list<item: struct<image_id: int64, category_id: int64, bbox: list<item: double>, score: double>>
child 0, item: struct<image_id: int64, category_id: int64, bbox: list<item: double>, score: double>
child 0, image_id: int64
child 1, category_id: int64
child 2, bbox: list<item: double>
child 0, item: double
child 3, score: double
inputs: struct<conf: double, dataset: string, dataset_version: int64, format: string, image_id_sha256: strin (... 729 chars omitted)
child 0, conf: double
child 1, dataset: string
child 2, dataset_version: int64
child 3, format: string
child 4, image_id_sha256: string
child 5, implementation_sha256: struct<coco_eval: string, rf100vl: string>
child 0, coco_eval: string
child 1, rf100vl: string
child 6, iou: double
child 7, limit: null
child 8, max_det: int64
child 9, model_key: string
child 10, model_spec: struct<constructor_size: string, input_size: int64, weight_file: string>
child 0, constructor_size: string
child 1, input_size: int64
child 2, weight_file: string
child 11, protocol_version: string
child 12, recipe_sha256: string
child 13, runtime: struct<hardware: struct<cpu: string, cpu_cores: int64, cuda_version: string, driver_version: string, (... 294 chars omitted)
child 0, hardware: struct<cpu: string, cpu_cores: int64, cuda_version: string, driver_version: string, gpu: string, gpu (... 34 cha
...
olo_commit: string, libreyolo_dirty: null, onnx: string, onnxruntime (... 58 chars omitted)
child 0, libreyolo: string
child 1, libreyolo_commit: string
child 2, libreyolo_dirty: null
child 3, onnx: string
child 4, onnxruntime: string
child 5, python: string
child 6, tensorrt: string
child 7, torch: string
child 14, split: string
child 15, weights_file: null
child 16, weights_sha256: string
result: struct<imgsz: int64, metrics: struct<AR1: double, AR10: double, AR100: double, AR_large: double, AR_ (... 348 chars omitted)
child 0, imgsz: int64
child 1, metrics: struct<AR1: double, AR10: double, AR100: double, AR_large: double, AR_max_det: double, AR_medium: do (... 140 chars omitted)
child 0, AR1: double
child 1, AR10: double
child 2, AR100: double
child 3, AR_large: double
child 4, AR_max_det: double
child 5, AR_medium: double
child 6, AR_small: double
child 7, mAP: double
child 8, mAP50: double
child 9, mAP75: double
child 10, mAP_large: double
child 11, mAP_medium: double
child 12, mAP_small: double
child 13, max_det: int64
child 2, num_classes: int64
child 3, num_detections: int64
child 4, num_images: int64
child 5, params_m: double
child 6, predictions_file: string
child 7, provider: string
child 8, timing_ms: list<item: double>
child 0, item: double
child 9, wall_seconds: double
to
{'fingerprint': Value('string'), 'inputs': {'conf': Value('float64'), 'dataset': Value('string'), 'dataset_version': Value('int64'), 'format': Value('string'), 'image_id_sha256': Value('string'), 'implementation_sha256': {'coco_eval': Value('string'), 'rf100vl': Value('string')}, 'iou': Value('float64'), 'limit': Value('null'), 'max_det': Value('int64'), 'model_key': Value('string'), 'model_spec': {'constructor_size': Value('string'), 'input_size': Value('int64'), 'weight_file': Value('string')}, 'protocol_version': Value('string'), 'recipe_sha256': Value('string'), 'runtime': {'hardware': {'cpu': Value('string'), 'cpu_cores': Value('int64'), 'cuda_version': Value('string'), 'driver_version': Value('string'), 'gpu': Value('string'), 'gpu_memory_gb': Value('float64'), 'ram_gb': Value('int64')}, 'harness': {'commit': Value('string'), 'dirty': Value('null')}, 'requested_device': Value('string'), 'software': {'libreyolo': Value('string'), 'libreyolo_commit': Value('string'), 'libreyolo_dirty': Value('null'), 'onnx': Value('string'), 'onnxruntime': Value('string'), 'python': Value('string'), 'tensorrt': Value('string'), 'torch': Value('string')}}, 'split': Value('string'), 'weights_file': Value('null'), 'weights_sha256': Value('string')}, 'result': {'imgsz': Value('int64'), 'metrics': {'AR1': Value('float64'), 'AR10': Value('float64'), 'AR100': Value('float64'), 'AR_large': Value('float64'), 'AR_max_det': Value('float64'), 'AR_medium': Value('float64'), 'AR_small': Value('float64'), 'mAP': Value('float64'), 'mAP50': Value('float64'), 'mAP75': Value('float64'), 'mAP_large': Value('float64'), 'mAP_medium': Value('float64'), 'mAP_small': Value('float64'), 'max_det': Value('int64')}, 'num_classes': Value('int64'), 'num_detections': Value('int64'), 'num_images': Value('int64'), 'params_m': Value('float64'), 'predictions_file': Value('string'), 'provider': Value('string'), 'timing_ms': List(Value('float64')), 'wall_seconds': Value('float64')}, 'schema_version': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
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 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
schema_version: string
fingerprint: string
detections: list<item: struct<image_id: int64, category_id: int64, bbox: list<item: double>, score: double>>
child 0, item: struct<image_id: int64, category_id: int64, bbox: list<item: double>, score: double>
child 0, image_id: int64
child 1, category_id: int64
child 2, bbox: list<item: double>
child 0, item: double
child 3, score: double
inputs: struct<conf: double, dataset: string, dataset_version: int64, format: string, image_id_sha256: strin (... 729 chars omitted)
child 0, conf: double
child 1, dataset: string
child 2, dataset_version: int64
child 3, format: string
child 4, image_id_sha256: string
child 5, implementation_sha256: struct<coco_eval: string, rf100vl: string>
child 0, coco_eval: string
child 1, rf100vl: string
child 6, iou: double
child 7, limit: null
child 8, max_det: int64
child 9, model_key: string
child 10, model_spec: struct<constructor_size: string, input_size: int64, weight_file: string>
child 0, constructor_size: string
child 1, input_size: int64
child 2, weight_file: string
child 11, protocol_version: string
child 12, recipe_sha256: string
child 13, runtime: struct<hardware: struct<cpu: string, cpu_cores: int64, cuda_version: string, driver_version: string, (... 294 chars omitted)
child 0, hardware: struct<cpu: string, cpu_cores: int64, cuda_version: string, driver_version: string, gpu: string, gpu (... 34 cha
...
olo_commit: string, libreyolo_dirty: null, onnx: string, onnxruntime (... 58 chars omitted)
child 0, libreyolo: string
child 1, libreyolo_commit: string
child 2, libreyolo_dirty: null
child 3, onnx: string
child 4, onnxruntime: string
child 5, python: string
child 6, tensorrt: string
child 7, torch: string
child 14, split: string
child 15, weights_file: null
child 16, weights_sha256: string
result: struct<imgsz: int64, metrics: struct<AR1: double, AR10: double, AR100: double, AR_large: double, AR_ (... 348 chars omitted)
child 0, imgsz: int64
child 1, metrics: struct<AR1: double, AR10: double, AR100: double, AR_large: double, AR_max_det: double, AR_medium: do (... 140 chars omitted)
child 0, AR1: double
child 1, AR10: double
child 2, AR100: double
child 3, AR_large: double
child 4, AR_max_det: double
child 5, AR_medium: double
child 6, AR_small: double
child 7, mAP: double
child 8, mAP50: double
child 9, mAP75: double
child 10, mAP_large: double
child 11, mAP_medium: double
child 12, mAP_small: double
child 13, max_det: int64
child 2, num_classes: int64
child 3, num_detections: int64
child 4, num_images: int64
child 5, params_m: double
child 6, predictions_file: string
child 7, provider: string
child 8, timing_ms: list<item: double>
child 0, item: double
child 9, wall_seconds: double
to
{'fingerprint': Value('string'), 'inputs': {'conf': Value('float64'), 'dataset': Value('string'), 'dataset_version': Value('int64'), 'format': Value('string'), 'image_id_sha256': Value('string'), 'implementation_sha256': {'coco_eval': Value('string'), 'rf100vl': Value('string')}, 'iou': Value('float64'), 'limit': Value('null'), 'max_det': Value('int64'), 'model_key': Value('string'), 'model_spec': {'constructor_size': Value('string'), 'input_size': Value('int64'), 'weight_file': Value('string')}, 'protocol_version': Value('string'), 'recipe_sha256': Value('string'), 'runtime': {'hardware': {'cpu': Value('string'), 'cpu_cores': Value('int64'), 'cuda_version': Value('string'), 'driver_version': Value('string'), 'gpu': Value('string'), 'gpu_memory_gb': Value('float64'), 'ram_gb': Value('int64')}, 'harness': {'commit': Value('string'), 'dirty': Value('null')}, 'requested_device': Value('string'), 'software': {'libreyolo': Value('string'), 'libreyolo_commit': Value('string'), 'libreyolo_dirty': Value('null'), 'onnx': Value('string'), 'onnxruntime': Value('string'), 'python': Value('string'), 'tensorrt': Value('string'), 'torch': Value('string')}}, 'split': Value('string'), 'weights_file': Value('null'), 'weights_sha256': Value('string')}, 'result': {'imgsz': Value('int64'), 'metrics': {'AR1': Value('float64'), 'AR10': Value('float64'), 'AR100': Value('float64'), 'AR_large': Value('float64'), 'AR_max_det': Value('float64'), 'AR_medium': Value('float64'), 'AR_small': Value('float64'), 'mAP': Value('float64'), 'mAP50': Value('float64'), 'mAP75': Value('float64'), 'mAP_large': Value('float64'), 'mAP_medium': Value('float64'), 'mAP_small': Value('float64'), 'max_det': Value('int64')}, 'num_classes': Value('int64'), 'num_detections': Value('int64'), 'num_images': Value('int64'), 'params_m': Value('float64'), 'predictions_file': Value('string'), 'provider': Value('string'), 'timing_ms': List(Value('float64')), 'wall_seconds': Value('float64')}, 'schema_version': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
RF100-VL campaign artifacts
Raw artifacts from RF100-VL benchmark campaigns run with LibreYOLO: the per-dataset training configs, per-epoch metrics, logs, GPU telemetry, scoring inputs and submissions. Published so a result can be checked rather than believed.
Protocol: fine-tune one checkpoint per dataset across the 100 RF100-VL datasets, score each on its test split with pycocotools at maxDets 500, and report the unweighted mean AP50:95. Epochs, batch size, seed and selection metric are fixed by the recipe recorded in each run.
Layout
<model_key>/<run_id>/
state/manifest.json which code, recipe and data produced this run
state/summary.json orchestrator outcome
state/logs/ one worker log per dataset
runs/<dataset>/<variant>/
train_config.yaml the exact config the trainer received
metrics.jsonl per-epoch metrics
results.csv per-epoch metrics, flat
train.log trainer log
status.json final per-dataset status
gpu_trace.jsonl.gz 1 Hz GPU telemetry for this dataset
gpu_summary.json utilization, power, idle time, attribution
stats/<dataset>.json training stats used to validate protocol conformance
eval/ per-dataset scores and raw prediction dumps
submissions/ submission JSON and markdown report
provenance/ the recipe and the dataset version lock
Read manifest.json first
Every run carries one. It records the resolved commit of both LibreYOLO and
the benchmark harness (from pip's direct_url.json, since a campaign box
installs from git and has no .git to interrogate), the recipe hash and its
protocol block, the dataset version-lock hash, the host and GPU inventory, and
the count of datasets in each state. The hashes the workers actually recorded
are stored alongside the ones derived at upload time, so a mismatch is visible
rather than reconciled away.
A result whose exact commit cannot be identified is an anecdote, not evidence. That is what this file is for.
Runs
| run id | model | status | datasets | use it for |
|---|---|---|---|---|
20260731-yolov9t-partial |
yolov9-t | EXPERIMENTAL, not a result | 7 of 100 trained | harness development only |
About 20260731-yolov9t-partial
This run exists because it was used to develop and debug the harness, and it is kept for that record. Do not cite it, and do not compare it to anything. Specifically:
- Only 7 of 100 datasets completed. The submission is correctly marked invalid, and no mean AP over 100 datasets exists for it.
- Its GPU telemetry is wrong. Datasets were packed several to a card, and the sampler of that version attributed a card to a single dataset: 16 datasets have no trace at all, and the 21 that do include work done by their cardmates. Later versions record every dataset on the card and label shared attribution honestly.
- Datasets within it were produced across more than one harness commit, so the single commit in its manifest does not describe all of them.
A campaign intended as a result runs all 100 datasets from a clean state under one set of commits.
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