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README.md

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+---
+license: cc-by-4.0
+tags:
+- adaptive
+- architecture
+- jsonl
+- leave-one-out
+- mixup-cutmix
+- pointcloud-text
+- self-training
+- standard
+- stratified
+---
+
+# dataset_055326048_architecture_pointcloud_text.py
+
+## Dataset Summary
+
+A **architecture** dataset with **pointcloud text** modality, stored in **jsonl** format.
+
+## Preprocessing & Augmentation
+
+- **Preprocessing**: standard
+- **Augmentation**: mixup cutmix
+
+## Splits & Sampling
+
+- **Split strategy**: leave one out
+- **Sampling**: stratified
+
+## Quality & Labeling
+
+- **Quality filtering**: adaptive
+- **Labeling**: self training
+
+## Files
+
+- `dataset_055326048_architecture_pointcloud_text.py` — main artifact of this repository
+
+## License
+
+See the license field above.

dataset_055326048_architecture_pointcloud_text.py

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+import json, os, hashlib
+from pathlib import Path
+
+# architecture dataset processor
+# modality: pointcloud_text, preprocessing: standard
+
+
+# --- real data source: architecture ---
+TV_DATASET = 'SUN397'
+HF_CANDIDATES = []
+IMAGE_FIELD = 'image'
+TEXT_FIELD = None
+LABEL_FIELD = 'label'
+PROMPT_TEMPLATE = 'a photo of {label} architecture'
+DATASET_URL = 'https://vision.princeton.edu/projects/2010/SUN/'
+
+def fetch_real_samples(max_samples=5000, cache_dir='./_cache'):
+    # 本地没有数据时自动下载真实公开数据集: torchvision -> HuggingFace -> 手动说明
+    out = []
+    if TV_DATASET is not None:
+        try:
+            import torchvision
+            ctor = getattr(torchvision.datasets, TV_DATASET)
+            try:
+                ds = ctor(root=cache_dir, split='train', download=True)
+            except TypeError:
+                try:
+                    ds = ctor(root=cache_dir, train=True, download=True)
+                except TypeError:
+                    ds = ctor(root=cache_dir, download=True)
+            classes = getattr(ds, 'classes', None)
+            os.makedirs(os.path.join(cache_dir, 'tv'), exist_ok=True)
+            for i, item in enumerate(ds):
+                if len(out) >= max_samples:
+                    break
+                img, label = item[0], item[1]
+                name = classes[label] if classes else str(label)
+                p = os.path.join(cache_dir, 'tv', str(i) + '.png')
+                try:
+                    img.save(p)
+                except Exception:
+                    continue
+                out.append({'image': p, 'text': PROMPT_TEMPLATE.format(label=name)})
+            if out:
+                return out
+        except Exception as e:
+            print('torchvision load failed:', e)
+    for repo in HF_CANDIDATES:
+        try:
+            from datasets import load_dataset
+            try:
+                ds = load_dataset(repo, split='train', streaming=True)
+            except Exception:
+                ds = load_dataset(repo, split='train')
+            img_dir = os.path.join(cache_dir, 'hf_images')
+            os.makedirs(img_dir, exist_ok=True)
+            for i, ex in enumerate(ds):
+                if len(out) >= max_samples:
+                    break
+                txt = None
+                if TEXT_FIELD is not None and TEXT_FIELD in ex:
+                    v = ex[TEXT_FIELD]
+                    txt = v if isinstance(v, str) else ' '.join(map(str, v if isinstance(v, (list, tuple)) else [v]))
+                if txt is None and LABEL_FIELD in ex:
+                    txt = PROMPT_TEMPLATE.format(label=ex[LABEL_FIELD])
+                if txt is None:
+                    continue
+                if IMAGE_FIELD not in ex or ex[IMAGE_FIELD] is None:
+                    continue
+                p = os.path.join(img_dir, str(i) + '.jpg')
+                try:
+                    ex[IMAGE_FIELD].convert('RGB').save(p)
+                except Exception:
+                    continue
+                out.append({'image': p, 'text': txt})
+            if out:
+                return out
+        except Exception as e:
+            print('HF load failed for', repo, ':', e)
+    print('Automatic download failed. Please get the data manually from:')
+    print('   ' + DATASET_URL)
+    return out
+
+def build_dataset(src, dst, sz=224):
+    samples = []
+    for f in Path(src).glob('*.jsonl'):
+        with open(f) as fp:
+            for line in fp:
+                if line.strip():
+                    samples.append(json.loads(line))
+
+    if not samples:
+        samples = fetch_real_samples()
+
+    # dedup
+    seen = set()
+    unique = []
+    for s in samples:
+        fp = s.get('image', s.get('audio', ''))
+        if fp and os.path.exists(fp):
+            h = hashlib.md5(open(fp, 'rb').read()).hexdigest()
+            if h not in seen:
+                seen.add(h)
+                unique.append(s)
+        else:
+            unique.append(s)
+
+    os.makedirs(dst, exist_ok=True)
+    out = []
+    for s in unique:
+        item = {}
+        if 'image' in s:
+            from PIL import Image
+            img = Image.open(s['image']).convert('RGB').resize((sz, sz))
+            p = os.path.join(dst, os.path.basename(s['image']))
+            img.save(p, 'JPEG', quality=95)
+            item['image'] = p
+        item['text'] = s.get('architecture', s.get('text', ''))
+        item['domain'] = 'architecture'
+        out.append(item)
+
+    with open(os.path.join(dst, 'dataset.jsonl'), 'w') as f:
+        for d in out:
+            f.write(json.dumps(d, ensure_ascii=False) + '\n')
+    return out
+
+if __name__ == '__main__':
+    import sys
+    result = build_dataset(sys.argv[1], sys.argv[2])
+    print(f'Processed {len(result)} samples')