File size: 5,969 Bytes
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6,050 files × ~4 MB = ~24 GB. Uploads in batches of 60 files (~240 MB/batch).
Resumable: HF Hub skips files already uploaded (content-addressed).
"""
from __future__ import annotations
import time
from pathlib import Path
from huggingface_hub import HfApi, CommitOperationAdd
TOKEN = "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"
REPO_ID = "bilalahmad176176/BrainAge-Golden-Preprocessed"
CACHE_DIR = Path("/home/MRI-DataSet/_train/cache")
BATCH_SIZE = 60
def upload_readme(api: HfApi):
readme = """---
license: cc-by-nc-4.0
task_categories:
- other
language:
- en
pretty_name: BrainAge Golden Preprocessed Cache
size_categories:
- 1K<n<10K
tags:
- neuroimaging
- mri
- brain-age
- pytorch
- preprocessed
- tensor-cache
---
# BrainAge Golden Preprocessed Cache
**6,050 preprocessed brain MRI tensors** ready for training a brain-age
prediction model. Skip the 40+ hour preprocessing step and jump straight
to model training.
## What's inside
Each `.pt` file (one per subject) contains:
| Key | Type | Shape | Description |
|-----|------|-------|-------------|
| `volume` | float16 | (128, 144, 112) | Z-normed T1w brain in MNI space, trilinear-resized |
| `tab` | float32 | (86,) | 70 regional volumes (log1p/12) + 3 sex one-hot + 13 site one-hot |
| `age` | float32 | scalar | Chronological age in years |
| `meta` | dict | — | subject_id, site, sex, age, split |
## Stats
| Metric | Value |
|--------|-------|
| Total subjects | 6,050 |
| Age range | 0 – 86 years |
| Source datasets | 12 (BCP, Calgary, ds002726, ds000248, PTBP, IXI, MPI-Leipzig, AOMIC, NKI-Rockland, ABIDE-I, ABIDE-II, ADHD-200) |
| Volume shape | 128 × 144 × 112 (D × H × W) |
| Tabular dim | 86 (70 regions + 3 sex + 13 site) |
| File size | ~4 MB each |
| Total size | ~24 GB |
## Preprocessing pipeline applied
```
Raw T1w NIfTI
→ HD-BET skull-strip (GPU)
→ N4 bias correction (ANTs)
→ Affine registration to MNI152 1mm
→ Z-score intensity normalization
→ Harvard-Oxford atlas segmentation (69 regions)
→ Volume measurement + rescaling to native space
→ Tensor packaging (.pt)
```
## Quick start
```python
from huggingface_hub import snapshot_download
import torch
# Download (~24 GB)
snapshot_download(
"bilalahmad176176/BrainAge-Golden-Preprocessed",
repo_type="dataset",
local_dir="cache/"
)
# Load one subject
data = torch.load("cache/cache/IXI002.pt", weights_only=False)
print(data["volume"].shape) # (128, 144, 112) float16
print(data["tab"].shape) # (86,) float32
print(data["age"]) # e.g. 36.2
print(data["meta"]) # {'subject_id': 'IXI002', 'site': 'DataSet-6_IXI', ...}
```
## Train a model
```bash
# Generate split
python -m pipeline_v2.data_split \\
--manifests Golden-0-to-25/manifest.csv Golden-25plus/manifest.csv \\
--out cache/split.csv
# Train
python -m pipeline_v2.train \\
--cache_dir cache/cache \\
--split_csv cache/split.csv \\
--out_ckpt brainage_sfcn.pt \\
--epochs 60 --batch 4
```
## Related
- Raw dataset: [bilalahmad176176/BrainAge-Golden-Raw](https://huggingface.co/datasets/bilalahmad176176/BrainAge-Golden-Raw)
- 3D Viewer demo: [bilalahmad176176/BrainAge-3D-Viewer](https://huggingface.co/spaces/bilalahmad176176/BrainAge-3D-Viewer)
## Citation
Please cite the original source studies listed in the raw dataset manifests.
"""
api.upload_file(
path_or_fileobj=readme.encode(),
path_in_repo="README.md",
repo_id=REPO_ID,
repo_type="dataset",
commit_message="Add dataset card",
)
print("README.md uploaded.")
def main():
api = HfApi(token=TOKEN)
print(f"User: {api.whoami()['name']}")
print(f"Repo: https://huggingface.co/datasets/{REPO_ID}")
upload_readme(api)
# Also upload the manifests + status logs for reproducibility
extras = [
("/home/MRI-DataSet/Golden-0-to-25/manifest.csv", "manifests/Golden-0-to-25_manifest.csv"),
("/home/MRI-DataSet/Golden-25plus/manifest.csv", "manifests/Golden-25plus_manifest.csv"),
("/home/MRI-DataSet/_train/logs/preprocess_status.csv", "logs/preprocess_status.csv"),
]
ops = []
for local, repo_path in extras:
if Path(local).exists():
ops.append(CommitOperationAdd(path_in_repo=repo_path, path_or_fileobj=local))
if ops:
api.create_commit(repo_id=REPO_ID, repo_type="dataset",
operations=ops, commit_message="Add manifests and preprocessing logs")
print(f"Uploaded {len(ops)} metadata files.")
# Upload .pt files in batches
pts = sorted(CACHE_DIR.glob("*.pt"))
total = len(pts)
print(f"\nUploading {total} .pt files (~24 GB)…\n")
for i in range(0, total, BATCH_SIZE):
batch = pts[i : i + BATCH_SIZE]
ops = [CommitOperationAdd(
path_in_repo=f"cache/{p.name}",
path_or_fileobj=str(p),
) for p in batch]
n = min(i + BATCH_SIZE, total)
msg = f"Add cache/{pts[i].name}…{batch[-1].name} ({i+1}–{n} of {total})"
print(f" [{n}/{total}] committing … ", end="", flush=True)
t0 = time.time()
try:
api.create_commit(
repo_id=REPO_ID, repo_type="dataset",
operations=ops, commit_message=msg,
)
print(f"done ({time.time()-t0:.0f}s)")
except Exception as e:
print(f"ERROR: {e}")
time.sleep(5)
try:
api.create_commit(
repo_id=REPO_ID, repo_type="dataset",
operations=ops, commit_message=msg + " (retry)",
)
print(" retry ok")
except Exception as e2:
print(f" retry failed: {e2}, skipping batch — rerun to resume")
print(f"\nDONE — https://huggingface.co/datasets/{REPO_ID}")
if __name__ == "__main__":
main()
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