Add files using upload-large-folder tool

.gitattributes

@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+metadata.json filter=lfs diff=lfs merge=lfs -text

OpenHumnoidActuatedFaceData.py

@@ -0,0 +1,119 @@
+import json, random, math, os
+from pathlib import Path
+from datasets import (
+    BuilderConfig, DatasetInfo, DownloadManager, GeneratorBasedBuilder,
+    SplitGenerator, Split, Features, Image, Value
+)
+from huggingface_hub import hf_hub_url
+
+_REPO_ID = "infosys/OpenHumnoidActuatedFaceData"
+_IMAGES_PER_SHARD = 10_000            # how many files you put in each tar
+_TAR_TPL = "images-{start:05d}-{end:05d}.tar"  # file-name pattern
+
+
+class ImageSubsetConfig(BuilderConfig):
+    def __init__(self, name, sample_size=None, **kw):
+        super().__init__(name=name, version="1.0.2",
+                         description=kw.get("description", ""))
+        self.sample_size = sample_size
+
+
+class MyImageDataset(GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        ImageSubsetConfig("full",  sample_size=None,
+                          description="Entire dataset (≈100 GB)"),
+        ImageSubsetConfig("small", sample_size=20_000,
+                          description="20 K random images"),
+    ]
+    DEFAULT_CONFIG_NAME = "small"
+
+    # ------------------------------------------------------------------ #
+    # 1. Schema                                                          #
+    # ------------------------------------------------------------------ #
+    def _info(self):
+        return DatasetInfo(
+            description="Humanoid face images + 16 servo angles.",
+            features=Features(
+                {
+                    "image": Image(),              # PIL.Image is fine
+                    "actuated_angle":
+                        {str(i): Value("int32") for i in range(16)},
+                }
+            ),
+        )
+
+    # ------------------------------------------------------------------ #
+    # 2. Download                                                         #
+    # ------------------------------------------------------------------ #
+    def _split_generators(self, dl_manager: DownloadManager):
+        # ---- 2-a: load metadata -------------------------------------- #
+        meta_url = hf_hub_url(_REPO_ID, "metadata.json", repo_type="dataset")
+        meta_path = dl_manager.download(meta_url)
+        with open(meta_path, encoding="utf-8") as f:
+            metadata = json.load(f)
+
+        all_names = sorted(metadata)
+        selected = (                           # sampling logic
+            sorted(random.sample(all_names, self.config.sample_size))
+            if self.config.sample_size else all_names
+        )
+        selected_set = set(selected)
+
+        # ---- 2-b: figure out which shards we need -------------------- #
+        max_idx = len(all_names) - 1
+        n_shards = math.floor(max_idx / _IMAGES_PER_SHARD) + 1
+        shard_files = [
+            _TAR_TPL.format(start=s*_IMAGES_PER_SHARD,
+                            end=min((s+1)*_IMAGES_PER_SHARD-1, max_idx))
+            for s in range(n_shards)
+        ]
+
+        # ---- 2-c: download (and extract) each tar -------------------- #
+        tar_urls   = [hf_hub_url(_REPO_ID, f, repo_type="dataset")
+                      for f in shard_files]
+        local_tars = dl_manager.download(tar_urls)          # .tar paths
+
+        #   we’ll stream from the tar, so no extract() needed
+
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={
+                    "tar_paths": local_tars,
+                    "metadata": metadata,
+                    "want": selected_set,
+                },
+            )
+        ]
+
+    # ------------------------------------------------------------------ #
+    # 3. Generate                                                         #
+    # ------------------------------------------------------------------ #
+    def _generate_examples(self, tar_paths, metadata, want):
+        """Stream over each tar and yield only requested files."""
+        idx = 0
+        for tar_path in tar_paths:
+            # iterate without extraction
+            for inner_path, fobj in \
+                    self._iter_archive_fast(tar_path):      # helper below
+                fname = Path(inner_path).name               # strip tar prefix
+                if fname not in want:
+                    continue
+
+                angles = metadata[fname]
+                yield idx, {
+                    "image": {"bytes": fobj.read(), "path": fname},
+                    "actuated_angle":
+                        {str(i): int(angles.get(str(i), 0)) for i in range(16)}
+                }
+                idx += 1
+
+    # Small wrapper so we don’t import datasets.utils.file_utils directly
+    @staticmethod
+    def _iter_archive_fast(tar_path):
+        import tarfile
+        with tarfile.open(tar_path) as tar:
+            for member in tar:
+                if member.isfile():
+                    f = tar.extractfile(member)
+                    yield member.name, f

images-00000-09999.tar

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3d7f5642e3769a7d485ee29f8deb1beb151a210d9c24841b6c2bf5def6775b1
+size 11352606720

images-10000-19999.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a895b631f00a4be479af0383b6c8c0fe598f71c23f180a0967516f14fd24ac62
+size 11351879680

images-100000-109999.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bb62cd347217bca0e145264703e4197d0a00f058e849f8a34a27d1861fb28ec0
+size 11352842240

images-110000-119999.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6aa79ab6b0293547e844b6da9a2193f5156ede13d613e9b2db62f48b6b992a1e
+size 11352760320

images-120000-129999.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:07efe60887fdce8d7c342cda8de258f876990e516e360f31be128cbd8c449dcd
+size 11352176640

images-130000-135235.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8898d1b36ffbf73187f1b878ba4b5d0f3a713b5f66c8e09900f8ea70b1e9feff
+size 5943767040

images-20000-29999.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:49817f52cef3dc6fa9917219821a2f62153d58787c8cecd68a85c069042c505a
+size 11352186880

images-30000-39999.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5909376fdb83eaf8ffc71e4f0a497898418851de08de2664267d48574a084f1a
+size 11353016320

images-40000-49999.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5ec275238c987874e930d7604d4046112f4c20977931fd8455d4a5a6967f77d4
+size 11353968640

images-50000-59999.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c498ea28a5b4a3ae4a6ae3ffb6c7f4c8713021bcc0a4fda3b3d295b5176cd43f
+size 11351685120

images-60000-69999.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ed71bf466faa58836a03531894ae829f57f1c22bf7cf7baa8d495be8f30aa1ad
+size 11352279040

images-70000-79999.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:11fa2294642abdf0a090260444a523739a67398614bf0ff8c13a249d0a59c838
+size 11352637440

images-80000-89999.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7c54663b1b0bedb2d1eda60ec79d87944d4d1597a4a02cda2f813c6c54de0bbd
+size 11351603200

images-90000-99999.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f5ee435c3d1050577ae2f3c975cb13ecc61c67fc23e0d29f4ce0725851927e11
+size 11351418880

metadata.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a70c8509508bab891f7e476dfa8219496b313669046cb995871443b07d12000
+size 49865473

tar_maker.py

@@ -0,0 +1,23 @@
+#!/usr/bin/env python3
+import os, tarfile, math, json
+from pathlib import Path
+
+IMAGE_DIR   = Path("images")          # all PNGs live here now
+META_JSON   = Path("metadata.json")
+PER_SHARD   = 10_000                  # ≤ 10 000 files per tar
+OUT_DIR     = Path(".")               # tars are written next to script
+
+# 1. load metadata so we keep ordering consistent
+with META_JSON.open() as f:
+    meta = json.load(f)
+all_names = sorted(meta)
+
+n_shards = math.floor((len(all_names)-1) / PER_SHARD) + 1
+for s in range(n_shards):
+    start = s * PER_SHARD
+    end   = min((s+1)*PER_SHARD - 1, len(all_names) - 1)
+    shard_name = OUT_DIR / f"images-{start:05d}-{end:05d}.tar"
+    print(f" → building {shard_name}")
+    with tarfile.open(shard_name, "w") as tar:
+        for fname in all_names[start:end+1]:
+            tar.add(IMAGE_DIR / fname, arcname=fname)