Squash history: VL-Adapter CLIP-RN101 features and annotations

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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
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+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
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+annotations/nlvr/train.json filter=lfs diff=lfs merge=lfs -text
+annotations/GQA/submit.json filter=lfs diff=lfs merge=lfs -text
+annotations/GQA/valid.json filter=lfs diff=lfs merge=lfs -text
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+annotations/GQA/paragraph_valid.json filter=lfs diff=lfs merge=lfs -text
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+annotations/lxmert/vgnococo.json filter=lfs diff=lfs merge=lfs -text
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+annotations/VG/relationships.json filter=lfs diff=lfs merge=lfs -text
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+annotations/COCO/dataset_coco.json filter=lfs diff=lfs merge=lfs -text
+annotations/vqa/karpathy_test.json filter=lfs diff=lfs merge=lfs -text
+annotations/vqa/v2_OpenEnded_mscoco_test2015_questions.json filter=lfs diff=lfs merge=lfs -text
+annotations/vqa/v2_mscoco_train2014_annotations.json filter=lfs diff=lfs merge=lfs -text
+annotations/vqa/test_3.json filter=lfs diff=lfs merge=lfs -text
+annotations/vqa/karpathy_train.json filter=lfs diff=lfs merge=lfs -text
+annotations/vqa/test_4.json filter=lfs diff=lfs merge=lfs -text
+annotations/vqa/v2_mscoco_val2014_annotations.json filter=lfs diff=lfs merge=lfs -text
+annotations/vqa/test.json filter=lfs diff=lfs merge=lfs -text
+annotations/vqa/karpathy_val.json filter=lfs diff=lfs merge=lfs -text
+annotations/vqa/test_2.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: mit
+language:
+- en
+pretty_name: VL-Adapter Datasets (CLIP-RN101 features)
+size_categories:
+- 100K<n<1M
+task_categories:
+- visual-question-answering
+- image-to-text
+tags:
+- vl-adapter
+- vl-bart
+- vl-t5
+- clip
+- vqa
+- gqa
+- nlvr2
+- coco-captions
+- parameter-efficient-fine-tuning
+configs:
+- config_name: COCO
+  data_files:
+  - split: train
+    path: features/COCO/data_clip_RN101_att/part-*.parquet
+- config_name: GQA
+  data_files:
+  - split: train
+    path: features/GQA/data_clip_RN101_att/part-*.parquet
+- config_name: VG
+  data_files:
+  - split: train
+    path: features/VG/data_clip_RN101_att/part-*.parquet
+- config_name: nlvr
+  data_files:
+  - split: train
+    path: features/nlvr/data_clip_RN101_att/part-*.parquet
+---
+
+# VL-Adapter Datasets
+
+Processed CLIP-ResNet101 grid features and annotations for
+[**VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks**](https://arxiv.org/abs/2112.06825)
+(Sung, Cho, Bansal — CVPR 2022), code at [ylsung/VL_adapter](https://github.com/ylsung/VL_adapter).
+
+This repository replaces the original Google Drive download, which is no longer
+available. It holds the same data in a Hub-native layout, plus a script that
+rebuilds the exact `datasets/` directory tree the training code expects.
+
+## Quick start — rebuild the original `datasets/` tree
+
+```bash
+pip install huggingface_hub pyarrow h5py numpy
+wget https://huggingface.co/datasets/ylsung/VL-Adapter-datasets/raw/main/restore_datasets.py
+python restore_datasets.py --out ./datasets
+```
+
+This downloads one shard at a time and deletes it after expanding, so it needs
+less than ~1 GB of scratch space beyond the final tree. It is resumable — rerun
+it and it skips what is already on disk. Budget **~47 GB of download** and
+**~132 GB for the restored tree** (the `.h5` files are uncompressed).
+
+To restore a subset:
+
+```bash
+python restore_datasets.py --out ./datasets --datasets GQA --datasets nlvr
+python restore_datasets.py --out ./datasets --only annotations
+```
+
+## Using the features directly
+
+If you just want the features and not the VL-Adapter directory layout:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("ylsung/VL-Adapter-datasets", "GQA", split="train", streaming=True)
+row = next(iter(ds))
+row["id"]        # '2336439'  -> the image id
+row["features"]  # (49, 2048) float16 CLIP-RN101 attention-pool grid features
+```
+
+Shards are written with 128-row row groups, so reading a single row costs about
+an 8.6 MB fetch rather than the whole 275 MB shard — measured at ~5 s to the
+first row, against ~9 minutes when the shards were a single row group.
+
+Streaming a full pass still transfers everything, so for repeated training runs
+restore the tree once with `restore_datasets.py` rather than streaming it each
+epoch.
+
+## Contents
+
+| Path | Rows / files | Download | Restored | Notes |
+|---|---|---|---|---|
+| `features/COCO/data_clip_RN101_att/` | 245,496 | 16.5 GB | 49.9 GB | train2014 82,783 · val2014 40,504 · test2015 122,209 |
+| `features/GQA/data_clip_RN101_att/` | 148,854 | 10.1 GB | 30.2 GB | |
+| `features/VG/data_clip_RN101_att/` | 108,079 | 7.3 GB | 22.0 GB | |
+| `features/nlvr/data_clip_RN101_att/` | 119,354 | 7.9 GB | 24.3 GB | NLVR2 train/dev/test1 |
+| `annotations/` | 43 files | 5.7 GB | 5.7 GB | verbatim copies of the original JSON/TXT/PY files |
+| `manifest.json.gz` | — | small | — | every original path with its size and CRC-32, used to verify a restore |
+| **total** | | **47.4 GB** | **132 GB** | |
+
+"Download" is the zstd-compressed parquet on the Hub; "Restored" is the
+uncompressed `.h5` tree that `restore_datasets.py` writes. Restoring a single
+dataset costs only its own row — `--datasets GQA` needs 30.2 GB, not 132 GB.
+
+### Feature schema
+
+Each row is one image:
+
+| column | type | meaning |
+|---|---|---|
+| `id` | `string` | image id — the `.h5` filename stem and the HDF5 group name |
+| `features` | `fixed_size_list<fixed_size_list<float16>[2048]>[49]` | CLIP-RN101 attention-pool grid features, shape `(49, 2048)` |
+
+In the original release each of these rows was a standalone `.h5` file holding a
+single group named `id` with one `(49, 2048)` float16 dataset called `features`.
+621,783 such files exceed the Hub's recommended limits (<100k files per repo,
+<10k per folder), so they are packed into 154 zstd-compressed parquet shards of
+4,096 rows each, subdivided into 128-row row groups. `restore_datasets.py` unpacks them back to identical `.h5`
+files — the arrays it writes are bit-exact and the files are the same 203,136
+bytes; only an internal HDF5 heap offset differs.
+
+### Annotations
+
+`annotations/` mirrors the original folder layout byte-for-byte
+(`annotations/vqa/karpathy_train.json` → `datasets/vqa/karpathy_train.json`).
+They are left as raw JSON rather than converted to parquet because their schemas
+vary widely and the training code parses them directly.
+
+`annotations/GQA/paragraph_testdev.json` is 0 bytes, and
+`COCO/clip_features/data_clip_RN101_fc/` and `paragraphs/` are empty, in the
+original release too.
+
+## NLVR2 images are not included
+
+The original archive also contained 119,354 NLVR2 photographs. The NLVR2 authors
+do not own their copyright and [ask that they not be shared
+publicly](https://github.com/lil-lab/nlvr/tree/master/nlvr2), so only the CLIP
+features derived from them are published here.
+
+Every script under `VL-T5/scripts/image/` reads `clip_features` and runs without
+the images. They are needed only for the end-to-end pixel training paths
+(`VL-T5/src/*_raw_data.py`). Request them from the
+[NLVR2 repository](https://github.com/lil-lab/nlvr/tree/master/nlvr2) and place
+them at `datasets/nlvr/images/`.
+
+Raw COCO, GQA and Visual Genome images are likewise not included; get them from
+[COCO](https://cocodataset.org/#download),
+[GQA](https://cs.stanford.edu/people/dorarad/gqa/download.html) and
+[Visual Genome](https://homes.cs.washington.edu/~ranjay/visualgenome/api.html).
+
+## Extracting your own features
+
+See [`feature_extraction/`](https://github.com/ylsung/VL_adapter/tree/main/feature_extraction)
+in the code repository to build features for other CLIP backbones.
+
+## Licensing
+
+The packaging code and the VL-Adapter project are MIT licensed. The underlying
+annotations and images keep the licenses of their original sources —
+[MS COCO](https://cocodataset.org/#termsofuse),
+[Visual Genome](https://homes.cs.washington.edu/~ranjay/visualgenome/api.html),
+[GQA](https://cs.stanford.edu/people/dorarad/gqa/about.html),
+[VQA v2](https://visualqa.org/terms.html) and
+[NLVR2](https://github.com/lil-lab/nlvr/tree/master/nlvr2) — and the derived
+features are released for research use on the same terms.
+
+## Citation
+
+```bibtex
+@inproceedings{sung2022vladapter,
+  title     = {VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks},
+  author    = {Yi-Lin Sung, Jaemin Cho, Mohit Bansal},
+  booktitle = {CVPR},
+  year      = {2022}
+}
+```

annotations/COCO/dataset_coco.json

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+oid sha256:2fd999220673258012acfb411a4e7e66af7d488050b2519b0badcc49b7600b8d
+size 144186139

annotations/GQA/eval.py

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+# Evaluation code for GQA. 
+# Computes a suite of metrics such as accuracy, consistency, plausibility and scores per question type and length.
+# Visit https://gqadataset.org/ for all information about the dataset, including examples, visualizations, paper and slides.
+#
+#
+# Metrics:
+# - Accuracy: Standard accuracy, computed over the balanced version of the dataset, which is more robust against 
+#             cheating by making educated guesses. For each question-answer pair (q,a), we give 1 point if the 
+#             predicted answer p matches a and 0 otherwise, and average over all questions in the dataset. 
+#
+# - Consistency: A metric for the level of model's consistency across different questions. For each question-answer 
+#                pair (q,a), we define a set Eq={q1, q2, ..., qn} of entailed questions, the answers to which can 
+#                be unambiguously inferred given (q,a). 
+#                Denote Q the set of all questions the model answered correctly. For each question q in Q, we 
+#                measure the model's accuracy over the entailed questions Eq to get the score sq and finally 
+#                average these results across all questions in Q. 
+#
+# - Validity: Measures whether the model gives a "valid" answer - one that can theoretically be an answer
+#             to the question (e.g. a color to a color question, yes/no to a binary question etc.).
+#             We provide a set of valid answers to each questions over the final answer vocabulary, in
+#             the choices file, and use it to compute average validity across the dataset.
+# 
+# - Plausibility: Measures whether the model answers are plausible, e.g. one that make sense in the real world,
+#                 e.g. not answering "purple" to a question about apple color (unless it's really purple).
+#                 We provide a set of all plausible answers to each questions, computed by looking at all
+#                 attributes and relations hold for various objects throughout the whole dataset scene graphs, 
+#                 and use it to compute average model plausibility across the data.
+#
+# - Grounding: Only for attention models. Measures whether the model looks at the relevant regions in the 
+#              image when answering a question. Each question in the dataset is annotated with the visual regions
+#              they refer to, which are then used to compute the level to which the model has a correct visual attention,
+#              which will allow to identify whether it really answers based on the image of by language-based guesses.
+#              Supports both spatial features and object-based features.
+#
+# - Distribution: Measures the overall match between the true answer distribution for different questions,
+#                 vs the overall distribution predicted by the model through its answers for all the data.
+#                 We use chi-square statistic to measure the degree of similarity between the distributions,
+#                 giving indication to the level of overall world-knowledge of the model
+# 
+# - Accuracy per type: accuracy per question structural types (logic, compare, choose), and semantic type
+#                      (questions about attributes, relations, categories, objects or the whole scene).
+#
+# - Accuracy for length: accuracy as a function of the question length, in terms of (1) words number, and semantic
+#                        complexity - number of reasoning steps.
+#
+# We may support additional metrics (e.g. coverage) in the future.
+#
+#
+# Files format:
+# - predictions file format: JSON array: [{"questionId": str, "prediction": str}]
+# - attentions file format: JSON array: 
+#   Spatial attention: [{"questionId": str, "attention": [mapSize x mapSize: float] }].
+#   Object-based attention:[{"questionId": str, "attention": [[x0, y0, x1, y1, float] x #regions] }]. 0 < x,y < 1. 
+# - questions and choices files are provided as part of the dataset.
+#   see https://gqadataset.org/download.html for information about their format.
+#
+#
+# If you have any questions or comments, please feel free to send an email,
+# at dorarad@cs.stanford.edu. We hope you'll enjoy using the GQA dataset! :)
+#
+#
+
+from collections import defaultdict
+from tqdm import tqdm
+import argparse
+import os.path
+import glob
+import json
+import math
+
+##### Arguments
+##########################################################################################
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--tier',           default = "val",                     type = str,    help = "Tier, e.g. train, val")
+parser.add_argument('--scenes',         default="{tier}_sceneGraphs.json",   type = str,    help = "Scene graphs file name format.")
+parser.add_argument('--questions',      default="{tier}_all_questions.json", type = str,    help = "Questions file name format.")
+parser.add_argument('--choices',        default="{tier}_choices.json",       type = str,    help = "Choices file name format.")
+parser.add_argument('--predictions',    default="{tier}_predictions.json",   type = str,    help = "Answers file name format.")
+parser.add_argument('--attentions',     default="{tier}_attentions.json",    type = str,    help = "Attentions file name format.")
+parser.add_argument('--consistency',    action="store_true",        help = "True to compute consistency score (Need to provide answers to questions in val_all_questions.json).")
+parser.add_argument('--grounding',      action="store_true",        help = "True to compute grounding score (If model uses attention).")
+parser.add_argument('--objectFeatures', action="store_true",        help = "True for object-based attention (False for spatial).")
+parser.add_argument('--mapSize',    default = 7,    type = int, help = "Optional, only to get attention score. Images features map size, mapSize * mapSize")
+args = parser.parse_args()
+
+print("Please make sure to use our provided visual features as gqadataset.org for better comparability. We provide both spatial and object-based features trained on GQA train set.") 
+print("In particular please avoid using features from https://github.com/peteanderson80/bottom-up-attention since they were trained on images contained in the GQA validation set and thus may give false scores improvement.\n")
+
+if not args.consistency:
+    print("Please consider using --consistency to compute consistency scores for entailed questions.")
+    print("If you do so, please provide answers to all questions in val_all_questions.json.\n")
+
+if not args.grounding:
+    print("Please consider using --grounding to compute attention scores.")
+    print("If you do so, please provide attention maps through --attentions.\n")
+
+
+##### Files Loading
+##########################################################################################
+
+def loadFile(name):
+    # load standard json file
+    if os.path.isfile(name):
+        with open(name) as file:
+            data = json.load(file)
+    # load file chunks if too big 
+    elif os.path.isdir(name.split(".")[0]):
+        data = {}
+        chunks = glob.glob('{dir}/{dir}_*.{ext}'.format(dir = name.split(".")[0], ext = name.split(".")[1]))
+        for chunk in chunks:
+            with open(chunk) as file: 
+                data.update(json.load(file))
+    else:
+        raise Exception("Can't find {}".format(name))
+    return data
+
+# Load scene graphs
+print("Loading scene graphs...")
+scenes = loadFile(args.scenes.format(tier = args.tier))
+
+# Load questions
+print("Loading questions...")
+questions = loadFile(args.questions.format(tier = args.tier))
+
+# Load choices
+print("Loading choices...")
+choices = loadFile(args.choices.format(tier = args.tier))
+
+# Load predictions and turn them into a dictionary
+print("Loading predictions...")
+predictions = loadFile(args.predictions.format(tier = args.tier))
+predictions = {p["questionId"]: p["prediction"] for p in predictions}
+
+# Make sure all question have predictions
+for qid in questions:
+    if (qid not in predictions) and (args.consistency or questions[qid]["isBalanced"]):
+        print("no prediction for question {}. Please add prediction for all questions.".format(qid))
+        raise Exception("missing predictions")
+
+# Load attentions and turn them into a dictionary
+attentions = None
+if args.grounding:
+    with open(args.attentions.format(tier = args.tier)) as attentionsFile:
+        attentions = json.load(attentionsFile)
+        attentions = {a["questionId"]: a["attention"] for a in attentions}
+
+##### Scores data structures initialization
+##########################################################################################
+
+# book to float
+def toScore(b):
+    return float(1 if b else 0)
+
+# Compute average of a list
+def avg(l):
+    if len(l) == 0:
+        return 0
+    return float(sum(l)) / len(l)
+
+def wavg(l, w):
+    if sum(w) == 0:
+        return None
+    return float(sum(l[i] * w[i] for i in range(len(l)))) / sum(w)
+
+# Initialize data structure to track all metrics: e.g. accuracy, validity and plausibility, as well as 
+# accuracy per question type, length and number of reasoning steps.
+scores = {
+    "accuracy": [], # list of accuracies per question (1 if correct else 0). Will be averaged ultimately.
+    "binary": [], # list of accuracies per a binary question (1 if correct else 0). Will be averaged ultimately.
+    "open": [], # list of accuracies per an open question (1 if correct else 0). Will be averaged ultimately.
+    "validity": [], # list of validity per question (1 if valid else 0).
+    "plausibility": [], # list of plausibility per question (1 if plausible else 0). 
+    "consistency": [], # list of consistency scores for entailed questions.
+    "accuracyPerStructuralType": defaultdict(list), # list of question accuracies for each structural type (e.g. compare, logic questions).
+    "accuracyPerSemanticType": defaultdict(list), # list of question accuracies for each semantic type (e.g. questions about an object, an attribute, a relation).
+    "accuracyPerLength": defaultdict(list), # list of question accuracies per question's word number.
+    "accuracyPerSteps": defaultdict(list), # list of question accuracies per question's reasoning length (steps number).
+    "grounding": [] # list of grounding scores for each question.
+}
+
+# Initialize golden and predicted histograms per each question group. Used to compute the distribution metric.
+dist = {
+    "gold": defaultdict(lambda: defaultdict(int)),
+    "predicted": defaultdict(lambda: defaultdict(int))
+}
+
+
+##### Question lengths - words numbers and reasoning steps number
+##########################################################################################
+
+# Compute question length (words number)
+def getWordsNum(question):
+    return len(question["question"].split())
+
+# Compute number of reasoning steps (excluding the final "querying" step which doesn't increase effective reasoning length)
+def getStepsNum(question):
+    return len([c for c in question["semantic"] if not (any([o in "{}: {}".format(c["operation"], c["argument"]) 
+        for o in ["exist", "query: name", "choose name"]]))])
+
+
+##### Functions for question annotations
+##########################################################################################
+
+# Utility function for converting question annotations string keys to slices
+def toSlice(strSlice):
+    sliceLims = (int(n) for n in strSlice.split(':'))
+    return apply(slice, sliceLims)
+
+# Utility function for converting question annotations string keys to indexes list:
+# "1" => [0]
+# "1:3" => [1, 2]
+# "4:9:2" => [4, 6, 8]
+def intsFromSlice(strSlice):
+    slice_obj = get_slice_obj(slicearg)
+    return(range(slice_obj.start or 0, slice_obj.stop or -1, slice_obj.step or 1))
+
+##### Functions for validity and plausibility
+##########################################################################################
+
+def belongs(element, group, question):
+    # normalization ()
+    if "Common" in question["types"]["detailed"]:
+        group = ["color", "material", "shape"]
+
+    return element in group
+
+##### Functions for consistency scores (for entailed questions ("inferred"))
+##########################################################################################
+
+def updateConsistency(questionId, question, questions):
+    inferredQuestions = [eid for eid in question["entailed"] if eid != questionId]
+
+    if correct and len(inferredQuestions) > 0:        
+        
+        cosnsitencyScores = []
+        for eid in inferredQuestions:
+            gold = questions[eid]["answer"]
+            predicted = predictions[eid]
+            score = toScore(predicted == gold)
+            cosnsitencyScores.append(score)
+        
+        scores["consistency"].append(avg(cosnsitencyScores))
+
+##### Functions for grounding score (optional, only for attention models)
+##########################################################################################
+
+# Utility functions for working with bounding boxes. 
+# c = (x0, y0, x1, y1), r = (r0, r1)
+
+def yrange(c):
+    return (c[1], c[3])
+
+def xrange(c):
+    return (c[0], c[2])
+
+def length(r):
+    if r is None:
+        return 0    
+    return float(r[1] - r[0])
+
+def size(c):
+    return length(xrange(c)) * length(yrange(c))
+
+def intersection(r1, r2):
+    ir = (max(r1[0], r2[0]), min(r1[1], r2[1]))
+    if ir[1] > ir[0]:
+        return ir
+    return None
+
+def intersectionSize(c1, c2):
+    return length(intersection(xrange(c1), xrange(c2))) * length(intersection(yrange(c1), yrange(c2)))
+
+def intersectionRate(c1, c2):
+    return float(intersectionSize(c1, c2)) / size(c1)
+
+# Get spatial cell
+def getCell(i, j):
+    edge = float(1) / args.mapSize
+    return (edge * i, edge * j, edge * (i + 1), edge * (j + 1))
+
+# Get bounding box of objectId in sceneGraph
+def getRegion(sceneGraph, objectId):
+    obj = sceneGraph["objects"][objectId]
+    x0 = float(obj["x"]) / sceneGraph["width"]
+    y0 = float(obj["y"]) / sceneGraph["height"]
+    x1 = float(obj["x"] + obj["w"]) / sceneGraph["width"]
+    y1 = float(obj["y"] + obj["h"]) / sceneGraph["height"]
+    return (x0, y0, x1, y1)
+
+# Compute grounding score. Computer amount of attention (probability) given to each of the regions
+# the question and answers refer to.
+def computeGroundingScore(question, sceneGraph, attentionMap):
+    ## prepare gold regions
+    regions = []
+    # add question regions
+    regions += [getRegion(sceneGraph, pointer) for pointer in question["annotations"]["question"].values()]
+    # add answer regions
+    regions += [getRegion(sceneGraph, pointer) for pointer in question["annotations"]["fullAnswer"].values()]
+    # add all the image if the question refers to the whole scene
+    if any(("scene" in c) for c in question["semantic"]):
+        regions.append((0, 0, 1, 1))
+    
+    # prepare attention map
+    if args.objectFeatures:
+        cells = [((x0, y0, x1, y1), attention) for x0, y0, x1, y1, attention in cells]
+    else:
+        cells = [(getCell(i, j), attentionMap[i][j]) for i in range(args.mapSize) for j in range(args.mapSize)]
+    
+    # compare attention map to gold regions
+    scores = []
+    for region in regions:
+        for cell, attention in cells:
+            scores.append(attention * intersectionRate(cell, region))
+    return sum(scores)
+
+##### Functions for distribution score
+##########################################################################################
+
+# Compute chi square statistic of gold distribution vs predicted distribution,
+# averaged over all question groups
+def chiSquare(goldDist, predictedDist):
+    sumScore, sumOverall = 0, 0
+    
+    for group in goldDist:
+        score, overall = 0, 0
+        
+        for ans in goldDist[group]:
+            e = goldDist[group][ans]
+            o = predictedDist[group].get(ans, 0)
+            score += ((float(o - e) ** 2) / e)
+            overall += goldDist[group][ans]
+        
+        sumScore += score * overall
+        sumOverall += overall
+
+    avgScore = float(sumScore) / sumOverall
+
+    return avgScore
+
+
+##### Main score computation 
+##########################################################################################
+
+# Loop over the questions and compute mterics
+for qid, question in tqdm(questions.items()):
+    gold = question["answer"]
+    predicted = predictions[qid]
+
+    correct = (predicted == gold)
+    score = toScore(correct)
+
+    wordsNum = getWordsNum(question)
+    stepsNum = getStepsNum(question)
+    
+    # Compute scores over the balanced dataset (more robust against cheating by making educated guesses)
+    if question["isBalanced"]:
+        # Update accuracy
+        scores["accuracy"].append(score)
+        scores["accuracyPerLength"][wordsNum].append(score)
+        scores["accuracyPerSteps"][stepsNum].append(score)
+        scores["accuracyPerStructuralType"][question["types"]["structural"]].append(score)
+        scores["accuracyPerSemanticType"][question["types"]["semantic"]].append(score)
+        answerType = "open" if question["types"]["structural"] == "query" else "binary"
+        scores[answerType].append(score)
+
+        # Update validity score
+        valid = belongs(predicted, choices[qid]["valid"], question)
+        scores["validity"].append(toScore(valid))
+
+        # Update plausibility score
+        plausible = belongs(predicted, choices[qid]["plausible"], question)
+        scores["plausibility"].append(toScore(plausible))
+
+        # Optionally compute grounding (attention) score
+        if attentions is not None:
+            groundingScore = computeGroundingScore(question, scenes[question["imageId"]], attentions[qid])
+            if groundingScore is not None:
+                scores["grounding"].append(groundingScore)
+        
+        # Update histograms for gold and predicted answers
+        globalGroup = question["groups"]["global"]
+        if globalGroup is not None:
+            dist["gold"][globalGroup][gold] += 1
+            dist["predicted"][globalGroup][predicted] += 1
+
+        # Compute consistency (for entailed questions)
+        updateConsistency(qid, question, questions)
+
+# Compute distribution score
+scores["distribution"] = chiSquare(dist["gold"], dist["predicted"]) / 100
+
+# Average scores over all questions (in the balanced dataset) and print scores
+
+metrics = [
+    "binary",
+    "open",
+    "accuracy",
+    "consistency",
+    "validity",
+    "plausibility",
+    "grounding",
+    "distribution"
+]
+
+detailedMetrics = [
+    ("accuracyPerStructuralType", "Accuracy / structural type"), 
+    ("accuracyPerSemanticType", "Accuracy / semantic type"), 
+    ("accuracyPerSteps", "Accuracy / steps number"),
+    ("accuracyPerLength", "Accuracy / words number") 
+]
+
+subMetrics = {
+    "attr": "attribute",
+    "cat": "category",
+    "global": "scene",
+    "obj": "object",
+    "rel": "relation" 
+}
+# average
+for k in metrics:
+    if isinstance(scores[k], list):
+        scores[k] = avg(scores[k]) * 100
+
+for k, _ in detailedMetrics:
+    for t in scores[k]:
+        scores[k][t] = avg(scores[k][t]) * 100, len(scores[k][t])
+
+# print
+print("")
+for m in metrics:
+    # skip grounding and consistency scores if not requested
+    if m == "grounding" and not args.grounding:
+        continue
+    if m == "consistency" and not args.consistency:
+        continue
+
+    # print score
+    print("{title}: {score:.2f}{suffix}".format(title = m.capitalize(), score = scores[m], 
+        suffix = " (lower is better)" if m == "distribution" else "%"))
+
+for m, mPrintName in detailedMetrics:
+    print("")
+    # print metric title
+    print("{}:".format(mPrintName))
+    
+    for t in sorted(list(scores[m].keys())):
+        # set sub-metric title
+        tName = t
+        if isinstance(scores[k], list):
+            tName = subMetrics.get(t, t).capitalize()
+
+        # print score
+        print("  {title}: {score:.2f}{suffix} ({amount} questions)".format(title = tName, 
+            score = scores[m][t][0], suffix = "%", amount = scores[m][t][1]))    

annotations/GQA/paragraph_valid.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3919b9e4a50dd3498fbabda79cee0481c1349f2352f43a7e97ced22cf71063f8
+size 40756212

annotations/GQA/readme.txt

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+Evaluation script for the GQA dataset. 
+eval.py computes metrics such as accuracy, consistency, plausibility, grounding scores etc.
+choices.json files are supporting files for the evaluation.
+
+Pleae visit gqadataset.org for all information about the dataset, including examples, visualizations, paper and slides.

annotations/GQA/submit.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d59e8799633fce8ef99d853e4f2e66081d1a5f1c7b991ccc34b8feb6e648914b
+size 590734987

annotations/GQA/train.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:62f99b931825bb6114ce497ed130232a6f097528b05e16f4d6ff4548781313e4
+size 180592312

annotations/GQA/trainval_ans2label.json

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+}

annotations/GQA/trainval_label2ans.json

@@ -0,0 +1 @@
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annotations/GQA/valid.json

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+size 25299820

annotations/VG/attributes.json

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annotations/VG/attributes_vocab.txt

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+gray,grey
+multi colored,multi-colored,multicolored
+double decker,double-decker
+unmade
+red
+camouflage
+blue
+white
+green
+pink
+yellow
+black
+ivory
+throwing
+orange
+spiky
+plaid
+purple
+soccer
+brake
+blonde
+american
+flat screen
+brown
+wooden
+performing
+pulled back
+windshield
+bald
+chocolate
+khaki
+apple
+blowing
+parked
+sticking out
+fluorescent
+glazed
+cooking
+brick
+home
+palm
+curly
+cheese
+crashing
+calm
+christmas
+gravel
+chain link,chainlink
+clear
+cloudy
+curled
+striped
+flying
+pine
+arched
+hardwood
+silver
+framed
+one way,oneway
+tall
+muscular
+skiing
+tiled
+bare
+surfing
+stuffed
+wii
+taking off
+sleeping
+jumping
+metal
+fire
+neon green
+soap
+park
+chalk
+license
+powdered
+up
+woven
+baby
+polar
+floppy
+toasted
+coffee
+potted
+wet
+tennis
+dry
+balding
+carpeted
+deep blue
+cardboard
+pointed
+sandy
+snow-covered,snow covered
+sheer
+wood
+swimming
+traffic
+crouching
+short
+melted
+marble
+rock
+open
+paper
+stacked
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+dirt
+waving
+ripe
+salt
+rolling
+long
+clock
+maroon
+little
+triangle
+large
+sand
+fallen
+foamy
+stack
+sliced
+blond
+plain
+straw
+busy
+checkered
+extended
+stainless steel,stainless-steel
+stone
+rocky
+laying down
+grazing
+porcelain
+snowboarding
+stop
+leather
+gold
+cargo
+playing tennis
+winter
+walking
+roman
+peeled
+plastic
+colorful
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+burnt
+messy
+tile
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+smiling
+fruit
+overcast
+adult
+water
+round
+birthday
+dark
+snowy
+leafless
+young
+wicker
+skateboarding
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+huge
+dress
+wire
+cracked
+concrete
+laying
+grassy
+foggy
+fried
+slice
+batting
+mountain
+halved
+ski
+statue
+still
+octagonal
+side view
+sitting
+wavy
+floral
+running
+moving
+small
+door
+wine
+closed
+cement
+splashing
+empty
+eating
+skating
+playing
+old
+tan
+leafy
+down
+electrical
+manicured
+standing
+blurry
+choppy
+driving
+watching
+parking
+pointy
+covering
+for sale
+reflecting
+railroad
+golden brown
+steep
+granite
+roll
+train
+spotted
+fluffy
+bending
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annotations/VG/objects.json

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+oid sha256:b91429c777881d48708cce0b0bdb2048904c14da3e063babd618d215dd174cfa
+size 412590096

annotations/VG/objects_vocab.txt

@@ -0,0 +1,1600 @@
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+toothpick
+alarm clock
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+photos
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+Each image is associated with a scene graph the provides information about the objects, attributes and relations in the image.
+Please visit gqadataset.org for all information about the dataset, including examples, visualizations, paper and slides. 

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+right of,to right of,right,on right side of,on right of
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+drinking,drinking from,drinks,drinking out of,drinks from,drink,drinking a,drink from
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+}

annotations/vqa/trainval_label2ans.json

@@ -0,0 +1,3131 @@
+[
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+    "tennis court",
+    "planes",
+    "curb",
+    "cafe",
+    "mayonnaise",
+    "cooked",
+    "pink and blue",
+    "dessert",
+    "legos",
+    "packing",
+    "orange and yellow",
+    "ketchup and mustard",
+    "mustache",
+    "horns",
+    "potato salad",
+    "backwards",
+    "yard",
+    "skateboard",
+    "yarn",
+    "bulldog",
+    "nobody",
+    "beets",
+    "mercedes",
+    "daffodils",
+    "racket",
+    "to dry",
+    "hammock",
+    "statues",
+    "denim",
+    "flashlight",
+    "carnations",
+    "hair",
+    "shaking hands",
+    "socks",
+    "female",
+    "looking at camera",
+    "coke",
+    "flip",
+    "circus",
+    "dresser",
+    "cocker spaniel",
+    "school",
+    "feathers",
+    "umbrella",
+    "luggage",
+    "leaves",
+    "to left",
+    "2:05",
+    "400",
+    "3:15",
+    "3:10",
+    "detroit",
+    "public market center",
+    "small",
+    "soda",
+    "fire truck",
+    "700",
+    "schnauzer",
+    "sparrow",
+    "fork and knife",
+    "wedding",
+    "beads",
+    "mud",
+    "mug",
+    "finger",
+    "herding",
+    "news",
+    "behind fence",
+    "elm",
+    "t shirt and jeans",
+    "conference",
+    "monkey",
+    "n",
+    "nike",
+    "ocean",
+    "cherry",
+    "teddy bears",
+    "wii controllers",
+    "descending",
+    "coffee maker",
+    "khaki",
+    "dinner",
+    "toothpicks",
+    "fern",
+    "bats",
+    "sunlight",
+    "towing",
+    "kiting",
+    "setting",
+    "papers",
+    "picture",
+    "football",
+    "long time",
+    "posts",
+    "cloudy",
+    "pork",
+    "tuxedo",
+    "1 in middle",
+    "pickle",
+    "nursing",
+    "black and brown",
+    "sailboat",
+    "2 years",
+    "platform",
+    "farmer",
+    "cutting hair",
+    "catching",
+    "100 year party ct",
+    "turn",
+    "men's",
+    "batting",
+    "surf",
+    "equestrian",
+    "wii remote",
+    "1 hour",
+    "guitar",
+    "turkey",
+    "direction",
+    "pilot",
+    "case",
+    "statue",
+    "peanut butter",
+    "towel",
+    "tower",
+    "competition",
+    "moon",
+    "burton",
+    "on floor",
+    "barber shop",
+    "flats",
+    "grass",
+    "roses",
+    "pillows",
+    "qantas",
+    "apartment",
+    "dairy",
+    "crest",
+    "sub",
+    "sun",
+    "suv",
+    "christian",
+    "donuts",
+    "horses",
+    "flat",
+    "flag",
+    "lighting",
+    "short",
+    "shore",
+    "10:50",
+    "10:55",
+    "soccer",
+    "clouds",
+    "alcohol",
+    "hill",
+    "snowboarding",
+    "urinal",
+    "stork",
+    "storm",
+    "store",
+    "surfboard",
+    "king",
+    "1 on right",
+    "8:35",
+    "skyscrapers",
+    "wilson",
+    "electric",
+    "patterned",
+    "national express",
+    "opponent",
+    "triangle",
+    "9:05",
+    "sweet",
+    "shirt",
+    "9",
+    "cement",
+    "prince",
+    "ping pong",
+    "phone",
+    "playing video game",
+    "sports",
+    "sepia",
+    "rainbow",
+    "peach",
+    "peace",
+    "parking meter",
+    "windy",
+    "very high",
+    "gray and white",
+    "compaq",
+    "kangaroo",
+    "leaving",
+    "cell phones",
+    "duck",
+    "frog",
+    "parrots",
+    "hammer time",
+    "wrist",
+    "stuffed animal",
+    "on bed",
+    "out",
+    "brown and white",
+    "organic",
+    "g",
+    "tennis racket",
+    "umbrellas",
+    "unknown",
+    "blue and black",
+    "dreadlocks",
+    "clip",
+    "pipe",
+    "cubs",
+    "married",
+    "identification",
+    "on his face",
+    "stucco",
+    "unclear",
+    "motorbike",
+    "rooster",
+    "camel",
+    "name",
+    "can't tell",
+    "in middle",
+    "pasta",
+    "spiral",
+    "coffee cup",
+    "cake",
+    "maroon",
+    "space shuttle",
+    "left and right",
+    "yellow",
+    "electricity",
+    "deli",
+    "dell",
+    "eagle",
+    "behind woman",
+    "military",
+    "on dresser",
+    "machine",
+    "gaming",
+    "in sky",
+    "wing",
+    "wind",
+    "wine",
+    "baseball uniform",
+    "new orleans",
+    "silver",
+    "bowls",
+    "lego",
+    "guitar hero",
+    "legs",
+    "man on left",
+    "person",
+    "in stands",
+    "zebras",
+    "victoria",
+    "little girl",
+    "recliner",
+    "menu",
+    "fair",
+    "looking out window",
+    "pirate",
+    "vaio",
+    "chrome",
+    "life",
+    "lift",
+    "child",
+    "chili",
+    "above toilet",
+    "gray and red",
+    "babies",
+    "bird feeder",
+    "roast beef",
+    "main street",
+    "train car",
+    "pavement",
+    "steps",
+    "people",
+    "fox",
+    "fog",
+    "happy",
+    "tigers",
+    "lays",
+    "4 way",
+    "peacock",
+    "venice",
+    "ollie",
+    "subway",
+    "teal",
+    "team",
+    "current",
+    "no smoking",
+    "love",
+    "sunbathing",
+    "heineken",
+    "winter",
+    "elephant",
+    "wheelchair",
+    "8 feet",
+    "colorado",
+    "bagels",
+    "polka dot",
+    "2 men",
+    "gatorade",
+    "arriving",
+    "bowling",
+    "not there",
+    "container",
+    "dodgers",
+    "12:10",
+    "12:15",
+    "ostrich",
+    "2",
+    "typing",
+    "give way",
+    "hockey",
+    "robe",
+    "bridge",
+    "barbed wire",
+    "cantaloupe",
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+    "1:35",
+    "1:30",
+    "twin",
+    "teddy",
+    "toothpaste",
+    "vacation",
+    "orange juice",
+    "no dog",
+    "skier",
+    "orange and black",
+    "clay",
+    "tying tie",
+    "tube",
+    "6:40",
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+    "take off",
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+    "am",
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+    "5:40",
+    "for sale",
+    "dinosaur",
+    "2 hours",
+    "dead end",
+    "bottom left",
+    "using laptop",
+    "young",
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+    "parking garage",
+    "pitching",
+    "4:00",
+    "4:05",
+    "information",
+    "not very",
+    "no parking",
+    "countryside",
+    "backyard",
+    "hauling",
+    "pans",
+    "rottweiler",
+    "mexican",
+    "swimsuit",
+    "harbor",
+    "air canada",
+    "highway",
+    "porcelain",
+    "hydrant",
+    "w",
+    "2 people",
+    "cartoon",
+    "obama",
+    "refrigerators",
+    "play",
+    "cover",
+    "sleeve",
+    "crosswalk",
+    "1 year",
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+    "hitting",
+    "fire",
+    "farmers market",
+    "white and brown",
+    "sesame",
+    "knee pads",
+    "audi",
+    "cucumber",
+    "stainless steel",
+    "baseball glove",
+    "santa",
+    "cobblestone",
+    "taking picture",
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+    "bird",
+    "leg",
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+    "mariners",
+    "standing",
+    "casserole",
+    "high",
+    "animal",
+    "stop",
+    "wallpaper",
+    "notebook",
+    "logitech",
+    "next to toilet",
+    "catch frisbee",
+    "jesus",
+    "owner",
+    "steel",
+    "2:55",
+    "2:50",
+    "soap",
+    "biker",
+    "bikes",
+    "not possible",
+    "amtrak",
+    "email",
+    "black and red",
+    "cd",
+    "ripe",
+    "north face",
+    "ball",
+    "dusk",
+    "bald",
+    "overalls",
+    "bananas",
+    "7:55",
+    "fire hydrant",
+    "conference room",
+    "residential",
+    "to see",
+    "onion",
+    "transport",
+    "fedex",
+    "seeds",
+    "bud light",
+    "delivery",
+    "construction",
+    "smooth",
+    "volvo",
+    "black and yellow",
+    "mushroom",
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+    "rural",
+    "buoy",
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+    "wreath",
+    "blanket",
+    "women's",
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+    "lizard",
+    "above",
+    "smoke",
+    "indians",
+    "boats",
+    "green",
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+    "on tray",
+    "lying down",
+    "in vase",
+    "sliding",
+    "pooping",
+    "train tracks",
+    "goalie",
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+    "lemonade",
+    "cow",
+    "plates",
+    "rail",
+    "rain",
+    "condiments",
+    "hills",
+    "hilly",
+    "ski pole",
+    "grizzly",
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+    "toothpick",
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+    "haircut",
+    "cold",
+    "birds",
+    "window",
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+    "fires",
+    "space",
+    "size",
+    "sheep",
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+    "10:08",
+    "10:05",
+    "10:00",
+    "fork",
+    "angel",
+    "breakfast",
+    "sunset",
+    "cat food",
+    "ground",
+    "remotes",
+    "red and yellow",
+    "concert",
+    "across street",
+    "forehand",
+    "mattress",
+    "pigtails",
+    "white and orange",
+    "3:45",
+    "heels",
+    "fruit",
+    "burrito",
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+    "desktop",
+    "9:35",
+    "red and silver",
+    "9:30",
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+    "fell",
+    "heater",
+    "smiley face",
+    "bucket",
+    "antenna",
+    "storage",
+    "polar",
+    "at table",
+    "pointing",
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+    "bathtub",
+    "colored",
+    "ahead",
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+    "broke",
+    "cruise ship",
+    "hitting ball",
+    "food",
+    "trailer",
+    "white and gray",
+    "dirt",
+    "pug",
+    "base",
+    "bnsf",
+    "coleslaw",
+    "dog food",
+    "eaten",
+    "copyright",
+    "trees",
+    "on horse",
+    "gloves",
+    "switzerland",
+    "corgi",
+    "badminton",
+    "single engine",
+    "used",
+    "grind",
+    "fly kite",
+    "qatar",
+    "golden retriever",
+    "run",
+    "rug",
+    "cigarette",
+    "72",
+    "himself",
+    "no sign",
+    "russian",
+    "adults",
+    "cotton",
+    "1 way",
+    "spectators",
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+    "go",
+    "asparagus",
+    "cupcakes",
+    "right 1",
+    "cloud",
+    "playing frisbee",
+    "mitsubishi",
+    "donut",
+    "halloween",
+    "bathing",
+    "high chair",
+    "herself",
+    "photograph",
+    "bed",
+    "defense",
+    "carrots",
+    "asics",
+    "sprinkles",
+    "visor",
+    "black and gray",
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+    "120",
+    "neither",
+    "flickr",
+    "tank top",
+    "reflection",
+    "i",
+    "german shepherd",
+    "taller",
+    "candy",
+    "controllers",
+    "mushrooms",
+    "mississippi",
+    "suzuki",
+    "backhand",
+    "hp",
+    "carriage",
+    "on elephant",
+    "balcony",
+    "nintendo",
+    "blue and pink",
+    "snowflakes",
+    "ups",
+    "clothes",
+    "new",
+    "net",
+    "never",
+    "remote control",
+    "cardboard",
+    "keyboard",
+    "dawn",
+    "enclosure",
+    "surprise",
+    "behind clouds",
+    "hood",
+    "girls",
+    "blue jay",
+    "no left turn",
+    "left side",
+    "playstation",
+    "laying down",
+    "tablet",
+    "protection",
+    "orange and blue",
+    "tank",
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+    "50 feet",
+    "sunflower",
+    "dining room",
+    "daytime",
+    "white and yellow",
+    "yellow and white",
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+    "classic",
+    "ship",
+    "digital",
+    "red sox",
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+    "string",
+    "magnet",
+    "transportation",
+    "easton",
+    "wallet",
+    "pink",
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+    "to catch frisbee",
+    "pine",
+    "tile",
+    "biplane",
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+    "stability",
+    "marshmallows",
+    "smoothie",
+    "texas",
+    "tropicana",
+    "green and brown",
+    "monster",
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+    "tractor",
+    "coconut",
+    "white and green",
+    "shirt and tie",
+    "napkin",
+    "tennis player",
+    "black",
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+    "behind",
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+    "bmw",
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+    "krispy kreme",
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+    "blankets",
+    "toilet brush",
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+    "around neck",
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+    "4:50",
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+    "airplanes",
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+    "hundreds",
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+    "black and blue",
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+    "in bowl",
+    "croissant",
+    "plaster",
+    "mercedes benz",
+    "ceiling",
+    "serve",
+    "western",
+    "burger",
+    "french fries",
+    "donkey",
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+    "talking",
+    "sunglasses",
+    "liquor",
+    "they aren't",
+    "towels",
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+    "on wall",
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+    "model",
+    "pacific",
+    "eiffel tower",
+    "parmesan",
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+    "woman",
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+    "warning",
+    "lemon",
+    "train station",
+    "pottery",
+    "nasa",
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+    "air france",
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+    "clydesdale",
+    "green and yellow",
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+    "decoration",
+    "plunger",
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+    "cigarettes",
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+    "on pole",
+    "basketball",
+    "bus driver",
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+    "circles",
+    "anniversary",
+    "chandelier",
+    "toast",
+    "on bike",
+    "on desk",
+    "2:10",
+    "2:15",
+    "steak",
+    "steam",
+    "3:00",
+    "no hat",
+    "on couch",
+    "relaxing",
+    "ski lift",
+    "sticker",
+    "goggles",
+    "photographer",
+    "mayo",
+    "xbox",
+    "orchid",
+    "wireless",
+    "snowy",
+    "braves",
+    "7:10",
+    "ladybug",
+    "umpire",
+    "holding",
+    "thumbs up",
+    "tugboat",
+    "stick",
+    "microwave",
+    "brown",
+    "steeple",
+    "sidewalk",
+    "htc",
+    "planter",
+    "playing wii",
+    "market",
+    "angels",
+    "club",
+    "grassy",
+    "posing",
+    "baseball field",
+    "curly",
+    "h",
+    "parking lot",
+    "marina",
+    "flying",
+    "tusks",
+    "thai",
+    "flowers",
+    "birthday cake",
+    "professional",
+    "tennis racquet",
+    "lilies",
+    "leather",
+    "on suitcase",
+    "blue and red",
+    "3",
+    "skyscraper",
+    "dairy queen",
+    "wheels",
+    "learning",
+    "olives",
+    "cycling",
+    "foggy",
+    "stickers",
+    "on fridge",
+    "apple and banana",
+    "in car",
+    "forks",
+    "business",
+    "riding bike",
+    "cats",
+    "orange and white",
+    "building",
+    "vines",
+    "messy",
+    "carpet",
+    "blueberries",
+    "fence",
+    "casual",
+    "trunk",
+    "brushing his teeth",
+    "smartphone",
+    "egg",
+    "peanuts",
+    "fancy",
+    "kayaking",
+    "fountain",
+    "daisy",
+    "washington dc",
+    "grocery",
+    "top right",
+    "bride",
+    "concentration",
+    "above stove",
+    "lid",
+    "clear",
+    "clean",
+    "chain link",
+    "very deep",
+    "circle",
+    "not here",
+    "10:40",
+    "x",
+    "throwing",
+    "union station",
+    "both",
+    "8:05",
+    "8:00",
+    "hallway",
+    "cleaning",
+    "on pizza",
+    "yankees",
+    "toyota",
+    "20 feet",
+    "parade",
+    "eating",
+    "30 mph",
+    "raspberry",
+    "giraffe and zebra",
+    "above door",
+    "skull",
+    "wine glass",
+    "facebook",
+    "on building",
+    "button",
+    "cell",
+    "poles",
+    "raspberries",
+    "hexagon",
+    "san francisco",
+    "singing",
+    "white and black",
+    "salad",
+    "ride",
+    "shower curtain",
+    "control",
+    "in his hand",
+    "broom",
+    "spray paint",
+    "front",
+    "globe",
+    "partly cloudy",
+    "mt airy",
+    "florida",
+    "privacy",
+    "bears",
+    "new york",
+    "beard",
+    "recessed",
+    "microphone",
+    "kickstand",
+    "scrambled",
+    "indoor",
+    "soldiers",
+    "dodge",
+    "michigan",
+    "hardwood",
+    "computer",
+    "on bus",
+    "high heels",
+    "chalk",
+    "10 inches",
+    "balloon",
+    "fireman",
+    "on left",
+    "savannah",
+    "shed",
+    "monday",
+    "kettle",
+    "telling time",
+    "rodeo",
+    "on shelf",
+    "museum",
+    "brick",
+    "penne",
+    "2 feet",
+    "quilt",
+    "japanese",
+    "cactus",
+    "tow truck",
+    "asia",
+    "lights",
+    "pancakes",
+    "on train",
+    "landscape",
+    "army",
+    "very fast",
+    "sony ericsson",
+    "honey",
+    "4 feet",
+    "briefcase",
+    "cadillac",
+    "man on right",
+    "above sink",
+    "deck",
+    "side of road",
+    "turning",
+    "spinach",
+    "boating",
+    "horse racing",
+    "in field",
+    "chest",
+    "nighttime",
+    "12",
+    "bat",
+    "bar",
+    "17",
+    "bag",
+    "bad",
+    "brazil",
+    "sail",
+    "shaved",
+    "black and silver",
+    "olympics",
+    "balance",
+    "mexico",
+    "sushi",
+    "seattle",
+    "vegetable",
+    "left",
+    "background",
+    "meow",
+    "tinkerbell",
+    "donut shop",
+    "floating",
+    "jones",
+    "milk",
+    "dim",
+    "limes",
+    "bikers",
+    "dip",
+    "french",
+    "france",
+    "phillies",
+    "downtown",
+    "fly",
+    "tokyo",
+    "soup",
+    "drawer",
+    "red and blue",
+    "ski poles",
+    "african",
+    "us open",
+    "main",
+    "girl",
+    "living",
+    "palm trees",
+    "california",
+    "paper towels",
+    "pepperoni",
+    "no clock",
+    "normal",
+    "track",
+    "in hand",
+    "fanta",
+    "yield",
+    "dog bed",
+    "12:05",
+    "12:00",
+    "shopping",
+    "cutting cake",
+    "bags",
+    "pan",
+    "exhaust",
+    "seagulls",
+    "running",
+    "railroad crossing",
+    "bottle",
+    "beagle",
+    "1.00",
+    "mom",
+    "laughing",
+    "road",
+    "1:25",
+    "1:20",
+    "close up",
+    "tripod",
+    "outfield",
+    "children",
+    "earbuds",
+    "ribbon",
+    "movement",
+    "tattoos",
+    "stretching",
+    "buses",
+    "diamond",
+    "green and black",
+    "cast iron",
+    "rye",
+    "csx",
+    "5:30",
+    "someone",
+    "house",
+    "flower",
+    "cups",
+    "sweet potato",
+    "mountain",
+    "4:15",
+    "refrigerator",
+    "hugging",
+    "baby's breath",
+    "meeting",
+    "solid",
+    "keys",
+    "leopard",
+    "beanie",
+    "flags",
+    "kites",
+    "bin",
+    "big",
+    "bib",
+    "google",
+    "scale",
+    "british airways",
+    "in background",
+    "1 inch",
+    "graduation",
+    "daisies",
+    "downhill",
+    "calico",
+    "pasture",
+    "desert",
+    "vehicles",
+    "2000",
+    "2007",
+    "2008",
+    "2009",
+    "chicago",
+    "sink",
+    "alaska",
+    "lime",
+    "tunnel",
+    "db",
+    "lines",
+    "squatting",
+    "dad",
+    "toshiba",
+    "cutting",
+    "day",
+    "sweatshirt",
+    "windsurfing",
+    "lacoste",
+    "no train",
+    "curved",
+    "sidecar",
+    "playing video games",
+    "kite",
+    "photography",
+    "bear",
+    "striped",
+    "green and white",
+    "ski resort",
+    "vent",
+    "plaid",
+    "plain",
+    "domestic",
+    "on ground",
+    "lake",
+    "sofa",
+    "soft",
+    "alive",
+    "chains",
+    "hose",
+    "ivory",
+    "brand",
+    "supreme",
+    "pie",
+    "pig",
+    "birthday party",
+    "no 1",
+    "sleep",
+    "feeding",
+    "paris",
+    "7:45",
+    "fireplace",
+    "parrot",
+    "bank of america",
+    "star wars",
+    "decorative",
+    "woodpecker",
+    "in suitcase",
+    "bunk",
+    "gate",
+    "over",
+    "writing",
+    "hanger",
+    "driving",
+    "green and orange",
+    "arizona",
+    "blue and green",
+    "free",
+    "scissors",
+    "ram",
+    "raw",
+    "metal",
+    "queen",
+    "north america",
+    "under armour",
+    "rowing",
+    "licking",
+    "toothbrush",
+    "jelly",
+    "players",
+    "bathroom",
+    "catch",
+    "washington monument",
+    "hearts",
+    "laptop",
+    "catching frisbee",
+    "cnn",
+    "tie",
+    "younger",
+    "serious",
+    "protest",
+    "hit ball",
+    "drinks",
+    "stands",
+    "on man",
+    "water",
+    "baseball",
+    "name tag",
+    "middle 1",
+    "conductor",
+    "ferry",
+    "wiimote",
+    "jetblue",
+    "on his head",
+    "floral",
+    "classroom",
+    "branches",
+    "swans",
+    "whirlpool",
+    "persian",
+    "sunrise",
+    "factory",
+    "fishing boat",
+    "motion",
+    "pelican",
+    "plastic",
+    "all of them",
+    "white",
+    "wide",
+    "multiple",
+    "seafood",
+    "wood",
+    "wool",
+    "lighter",
+    "parking",
+    "stop light",
+    "spoons",
+    "in sink",
+    "abstract",
+    "telephone",
+    "10:30",
+    "bowl",
+    "macaroni",
+    "shih tzu",
+    "hot dogs",
+    "kneeling",
+    "fridge",
+    "table",
+    "8:55",
+    "8:50",
+    "cabinet",
+    "verizon",
+    "5",
+    "trash",
+    "lace",
+    "siblings",
+    "far",
+    "fan",
+    "sony",
+    "9:25",
+    "9:20",
+    "foreground",
+    "rectangles",
+    "siamese",
+    "cucumbers",
+    "urinals",
+    "sandwich",
+    "water bottle",
+    "pirates",
+    "dugout",
+    "weather vane",
+    "syrup",
+    "double",
+    "cleaner",
+    "sandwiches",
+    "windows",
+    "hit",
+    "bars",
+    "art",
+    "dump",
+    "unsure",
+    "bark",
+    "arm",
+    "barn",
+    "paisley",
+    "fire extinguisher",
+    "c",
+    "suspenders",
+    "orioles",
+    "1950s",
+    "cumulus",
+    "moving",
+    "no grass",
+    "marker",
+    "prom",
+    "20 ft",
+    "behind bench",
+    "upright",
+    "crane",
+    "notes",
+    "waiting",
+    "mitt",
+    "metro",
+    "spices",
+    "apple",
+    "motor",
+    "porch",
+    "rabbit",
+    "women",
+    "tag",
+    "tam",
+    "tan",
+    "onions",
+    "shopping cart",
+    "flour",
+    "practice",
+    "maple leaf",
+    "tray",
+    "parasailing",
+    "6 feet",
+    "powdered",
+    "seaweed",
+    "chicken",
+    "thousands",
+    "words",
+    "closet",
+    "virgin atlantic",
+    "closed",
+    "pants",
+    "canopy",
+    "traffic light",
+    "vanilla",
+    "wild",
+    "on street",
+    "octagon",
+    "star alliance",
+    "english",
+    "cakes",
+    "happiness",
+    "sky",
+    "ski",
+    "leaf",
+    "brush",
+    "sweatband",
+    "polar bears",
+    "daffodil",
+    "boot",
+    "illinois",
+    "book",
+    "junk"
+]

annotations/vqa/v2_OpenEnded_mscoco_test2015_questions.json

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+oid sha256:69169f086e0fa9f878a7425aabe6573e999af79029726b64bbf144482000960a
+size 41800665

annotations/vqa/v2_mscoco_train2014_annotations.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:770d2d3980e7838d6186082e0ddeddd804014c390ae45a592e996d6baf841d92
+size 355516514

annotations/vqa/v2_mscoco_val2014_annotations.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0564650e712b34c246e4ad142894692bf2e45dee8e9cc6ebb2cedbe85731c4bf
+size 171762489

convert_zip_to_hf.py

@@ -0,0 +1,357 @@
+#!/usr/bin/env python3
+"""
+Convert the original VL-Adapter `vlt5_dataset` zip into a HuggingFace-Hub-friendly
+layout, streaming straight out of the zip so the archive is never fully extracted.
+
+Original layout (inside VLadapter.zip)        HF repo layout produced here
+---------------------------------------       -----------------------------------------
+vlt5_dataset/<D>/clip_features/                features/<D>/data_clip_RN101_att/
+    data_clip_RN101_att/<img_id>.h5                part-XXXXX.parquet   (id, features)
+vlt5_dataset/nlvr/images/<img_id>.png          images/nlvr/part-XXXXX.parquet  (id, image)
+vlt5_dataset/<everything else>                 annotations/<same relative path>
+
+Each `.h5` holds exactly one group named after the image id with a single
+(49, 2048) float16 dataset `features`, so the payload is stored losslessly in
+parquet as a nested fixed-size list and rebuilt by `restore_datasets.py`.
+
+Disk use is bounded: one shard (~300 MB) plus at most one annotation file is on
+disk at a time when --upload is used.
+"""
+import argparse
+import gc
+import io
+import json
+import os
+import sys
+import time
+import zipfile
+from pathlib import Path
+
+import h5py
+import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+ROOT = "vlt5_dataset/"
+FEAT_SHAPE = (49, 2048)
+FEAT_NUMEL = FEAT_SHAPE[0] * FEAT_SHAPE[1]
+
+
+# --------------------------------------------------------------------------- #
+# zip inventory
+# --------------------------------------------------------------------------- #
+def classify(zf):
+    """Split the archive into feature / nlvr-image / plain-file groups.
+
+    Entries keep central-directory order, which for this archive equals physical
+    order, so reading them back is a sequential scan of the file.
+    """
+    feats, images, others = {}, [], []
+    for info in zf.infolist():
+        name = info.filename
+        if name.endswith("/"):
+            continue
+        if not name.startswith(ROOT):
+            raise SystemExit(f"unexpected entry outside {ROOT}: {name}")
+        rel = name[len(ROOT):]
+        if "/clip_features/" in rel and rel.endswith(".h5"):
+            dataset, _, variant, _ = rel.split("/")
+            feats.setdefault((dataset, variant), []).append(info)
+        elif rel.startswith("nlvr/images/"):
+            images.append(info)
+        else:
+            others.append(info)
+    return feats, images, others
+
+
+# --------------------------------------------------------------------------- #
+# parquet writers
+# --------------------------------------------------------------------------- #
+def features_table(ids, buf, count):
+    """(49, 2048) float16 arrays -> fixed_size_list<fixed_size_list<half>[2048]>[49]."""
+    flat = pa.array(buf[: count * FEAT_NUMEL], type=pa.float16())
+    inner = pa.FixedSizeListArray.from_arrays(flat, FEAT_SHAPE[1])
+    outer = pa.FixedSizeListArray.from_arrays(inner, FEAT_SHAPE[0])
+    return pa.table({"id": pa.array(ids, type=pa.string()), "features": outer})
+
+
+def images_table(ids, blobs):
+    """PNG bytes in the struct layout the HF `datasets` Image feature expects."""
+    image = pa.StructArray.from_arrays(
+        [pa.array(blobs, type=pa.binary()), pa.array(ids, type=pa.string())],
+        names=["bytes", "path"],
+    )
+    return pa.table({"id": pa.array(ids, type=pa.string()), "image": image})
+
+
+def write_shard(table, path, compression, level, meta, row_group_size=128):
+    """Write one shard.
+
+    Small row groups matter here: a reader that wants a single row must fetch a
+    whole row group, so 4096-row groups meant pulling ~282 MB to read one image.
+    At 128 rows that drops to ~8.6 MB, and for this data it also compresses
+    slightly *better* (-2.2%), so there is no size penalty to pay for it.
+    """
+    path.parent.mkdir(parents=True, exist_ok=True)
+    schema = table.schema.with_metadata({k: str(v) for k, v in meta.items()})
+    pq.write_table(
+        table.replace_schema_metadata(schema.metadata),
+        path,
+        compression=compression,
+        compression_level=level if compression == "zstd" else None,
+        row_group_size=row_group_size,
+    )
+    return path.stat().st_size
+
+
+# --------------------------------------------------------------------------- #
+# upload / state
+# --------------------------------------------------------------------------- #
+class Sink:
+    """Writes shards locally and, with --upload, pushes then deletes each one."""
+
+    def __init__(self, out_dir, repo_id, upload, state_path, token=None, private=False,
+                 max_stall=86400):
+        self.out_dir = Path(out_dir)
+        self.repo_id = repo_id
+        self.upload = upload
+        self.max_stall = max_stall
+        self.state_path = Path(state_path)
+        self.done = set()
+        if self.state_path.exists():
+            self.done = set(json.loads(self.state_path.read_text())["done"])
+        self.api = None
+        if upload:
+            from huggingface_hub import HfApi
+
+            self.api = HfApi(token=token)
+            self.api.create_repo(repo_id, repo_type="dataset", private=private, exist_ok=True)
+            who = self.api.whoami()["name"]
+            print(f"[hub] authenticated as {who}; target dataset {repo_id} "
+                  f"({'private' if private else 'public'})", flush=True)
+
+    def has(self, rel):
+        return rel in self.done
+
+    def mark(self, rel):
+        self.done.add(rel)
+        self.state_path.parent.mkdir(parents=True, exist_ok=True)
+        tmp = self.state_path.with_suffix(".tmp")
+        tmp.write_text(json.dumps({"done": sorted(self.done)}))
+        tmp.replace(self.state_path)
+
+    def push(self, local_path, rel):
+        """Upload one file, waiting out network outages rather than dying on them.
+
+        A laptop that sleeps overnight looks exactly like a long DNS failure, so
+        retry indefinitely with a capped backoff and only give up after
+        --max-stall seconds of *continuous* failure.
+        """
+        if self.upload:
+            delay, attempt, first_failure = 5, 0, None
+            while True:
+                attempt += 1
+                try:
+                    self.api.upload_file(
+                        path_or_fileobj=str(local_path),
+                        path_in_repo=rel,
+                        repo_id=self.repo_id,
+                        repo_type="dataset",
+                        commit_message=f"Add {rel}",
+                    )
+                    if first_failure is not None:
+                        print(f"    recovered after {time.time()-first_failure:.0f}s "
+                              f"and {attempt-1} failed attempts", flush=True)
+                    break
+                except KeyboardInterrupt:
+                    raise
+                except Exception as exc:  # noqa: BLE001 - retry any transport error
+                    if first_failure is None:
+                        first_failure = time.time()
+                    stalled = time.time() - first_failure
+                    if stalled > self.max_stall:
+                        raise SystemExit(
+                            f"giving up on {rel} after {stalled/3600:.1f}h of failures; "
+                            f"rerun the same command to resume")
+                    print(f"    upload of {rel} failed (attempt {attempt}, "
+                          f"stalled {stalled/60:.1f}min): {exc}; retry in {delay}s", flush=True)
+                    time.sleep(delay)
+                    delay = min(delay * 2, 300)
+            local_path.unlink()
+        self.mark(rel)
+
+
+# --------------------------------------------------------------------------- #
+# conversion passes
+# --------------------------------------------------------------------------- #
+def convert_features(zf, group, infos, sink, args, manifest):
+    dataset, variant = group
+    prefix = f"features/{dataset}/{variant}"
+    total = len(infos)
+    shards = (total + args.shard_rows - 1) // args.shard_rows
+    print(f"[features] {dataset}/{variant}: {total} files -> {shards} shards", flush=True)
+
+    buf = np.empty(args.shard_rows * FEAT_NUMEL, dtype=np.float16)
+    for shard_idx in range(shards):
+        rel = f"{prefix}/part-{shard_idx:05d}.parquet"
+        chunk = infos[shard_idx * args.shard_rows : (shard_idx + 1) * args.shard_rows]
+        manifest.extend(
+            {"path": f"{dataset}/clip_features/{variant}/{i.filename.rsplit('/', 1)[1]}",
+             "size": i.file_size, "crc": i.CRC, "shard": rel}
+            for i in chunk
+        )
+        if sink.has(rel):
+            print(f"  skip {rel} (done)", flush=True)
+            continue
+
+        ids = []
+        t0 = time.time()
+        for row, info in enumerate(chunk):
+            raw = zf.read(info)
+            stem = info.filename.rsplit("/", 1)[1][:-3]
+            with h5py.File(io.BytesIO(raw), "r") as h5:
+                keys = list(h5.keys())
+                # restore_datasets.py names the rebuilt file after the group key,
+                # so the two must agree or the tree would come back renamed.
+                if keys != [stem]:
+                    raise SystemExit(f"{info.filename}: expected single group {stem!r}, got {keys}")
+                arr = h5[stem]["features"]
+                if arr.shape != FEAT_SHAPE or arr.dtype != np.float16:
+                    raise SystemExit(f"unexpected array {arr.shape}/{arr.dtype} in {info.filename}")
+                arr.read_direct(buf[row * FEAT_NUMEL : (row + 1) * FEAT_NUMEL].reshape(FEAT_SHAPE))
+            ids.append(stem)
+
+        table = features_table(ids, buf, len(chunk))
+        path = Path(args.out) / rel
+        size = write_shard(table, path, args.compression, args.level,
+                           {"source": f"{ROOT}{dataset}/clip_features/{variant}",
+                            "shape": str(FEAT_SHAPE), "dtype": "float16"},
+                           row_group_size=args.row_group_size)
+        del table
+        gc.collect()
+        print(f"  {rel}: {len(chunk)} rows, {size/1e6:.1f} MB, {time.time()-t0:.1f}s", flush=True)
+        sink.push(path, rel)
+
+
+def convert_images(zf, infos, sink, args, manifest):
+    print(f"[images] nlvr: {len(infos)} png", flush=True)
+    shard_idx, nbytes = 0, 0
+
+    def flush(shard_idx, batch, blobs):
+        rel = f"images/nlvr/part-{shard_idx:05d}.parquet"
+        if sink.has(rel):
+            print(f"  skip {rel} (done)", flush=True)
+            return
+        table = images_table(batch, blobs)
+        path = Path(args.out) / rel
+        size = write_shard(table, path, "zstd", 1, {"source": f"{ROOT}nlvr/images"},
+                           row_group_size=args.row_group_size)
+        print(f"  {rel}: {len(batch)} images, {size/1e6:.1f} MB", flush=True)
+        sink.push(path, rel)
+
+    pending = []
+    for info in infos:
+        name = info.filename.rsplit("/", 1)[1]
+        rel_shard = f"images/nlvr/part-{shard_idx:05d}.parquet"
+        manifest.append({"path": f"nlvr/images/{name}", "size": info.file_size,
+                         "crc": info.CRC, "shard": rel_shard})
+        pending.append(info)
+        nbytes += info.file_size
+        if nbytes >= args.image_shard_bytes:
+            if not sink.has(rel_shard):
+                batch = [i.filename.rsplit("/", 1)[1][:-4] for i in pending]
+                blobs = [zf.read(i) for i in pending]
+                flush(shard_idx, batch, blobs)
+                del blobs
+                gc.collect()
+            else:
+                print(f"  skip {rel_shard} (done)", flush=True)
+            pending, nbytes = [], 0
+            shard_idx += 1
+    if pending:
+        rel_shard = f"images/nlvr/part-{shard_idx:05d}.parquet"
+        if not sink.has(rel_shard):
+            batch = [i.filename.rsplit("/", 1)[1][:-4] for i in pending]
+            blobs = [zf.read(i) for i in pending]
+            flush(shard_idx, batch, blobs)
+
+
+def convert_others(zf, infos, sink, args, manifest):
+    print(f"[annotations] {len(infos)} files", flush=True)
+    for info in infos:
+        orig = info.filename[len(ROOT):]
+        rel = f"annotations/{orig}"
+        manifest.append({"path": orig, "size": info.file_size, "crc": info.CRC, "shard": rel})
+        if sink.has(rel):
+            print(f"  skip {rel} (done)", flush=True)
+            continue
+        path = Path(args.out) / rel
+        path.parent.mkdir(parents=True, exist_ok=True)
+        with zf.open(info) as src, open(path, "wb") as dst:
+            while True:
+                block = src.read(8 << 20)
+                if not block:
+                    break
+                dst.write(block)
+        print(f"  {rel}: {info.file_size/1e6:.1f} MB", flush=True)
+        sink.push(path, rel)
+
+
+# --------------------------------------------------------------------------- #
+def main():
+    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("--zip", required=True, help="path to VLadapter.zip")
+    p.add_argument("--out", required=True, help="staging directory for shards")
+    p.add_argument("--repo-id", help="HF dataset repo, e.g. user/VL-Adapter-datasets")
+    p.add_argument("--upload", action="store_true", help="upload each shard then delete it locally")
+    p.add_argument("--token", default=None)
+    p.add_argument("--private", action="store_true", help="create the dataset repo private")
+    p.add_argument("--max-stall", type=int, default=86400,
+                   help="give up only after this many seconds of continuous upload failure")
+    p.add_argument("--shard-rows", type=int, default=4096)
+    p.add_argument("--row-group-size", type=int, default=128,
+                   help="rows per parquet row group; smaller = cheaper single-row reads")
+    p.add_argument("--image-shard-bytes", type=int, default=700 * 1024 * 1024)
+    p.add_argument("--compression", default="zstd", choices=["zstd", "snappy", "none"])
+    p.add_argument("--level", type=int, default=9)
+    p.add_argument("--only", choices=["features", "images", "annotations"], action="append")
+    p.add_argument("--limit-shards", type=int, default=0, help="testing: stop after N feature shards per group")
+    p.add_argument("--state", default=None, help="resume state file (default <out>/.convert_state.json)")
+    args = p.parse_args()
+
+    if args.upload and not args.repo_id:
+        p.error("--upload requires --repo-id")
+    args.state = args.state or os.path.join(args.out, ".convert_state.json")
+    only = set(args.only or ["features", "images", "annotations"])
+
+    zf = zipfile.ZipFile(args.zip)
+    feats, images, others = classify(zf)
+    sink = Sink(args.out, args.repo_id, args.upload, args.state, args.token, args.private,
+                args.max_stall)
+    manifest = []
+
+    if "annotations" in only:
+        convert_others(zf, others, sink, args, manifest)
+    if "images" in only:
+        convert_images(zf, images, sink, args, manifest)
+    if "features" in only:
+        for group in sorted(feats):
+            infos = feats[group]
+            if args.limit_shards:
+                infos = infos[: args.limit_shards * args.shard_rows]
+            convert_features(zf, group, infos, sink, args, manifest)
+
+    man_path = Path(args.out) / "manifest.json.gz"
+    if not args.limit_shards:
+        import gzip
+
+        man_path.parent.mkdir(parents=True, exist_ok=True)
+        with gzip.open(man_path, "wt") as fh:
+            json.dump({"root": ROOT, "files": manifest}, fh)
+        print(f"[manifest] {len(manifest)} entries -> {man_path}", flush=True)
+        sink.push(man_path, "manifest.json.gz")
+    print("done.", flush=True)
+
+
+if __name__ == "__main__":
+    sys.exit(main())

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