Upload folder using huggingface_hub

.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
+xview2_train_tier3_sharegpt.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,317 @@
----
-license: cc-by-4.0
----
+---
+license: cc-by-nc-sa-4.0
+task_categories:
+  - visual-question-answering
+  - image-classification
+  - image-segmentation
+language:
+  - en
+  - zh
+  - ja
+tags:
+  - disaster-recognition
+  - satellite-imagery
+  - remote-sensing
+  - vision-language
+  - multi-modal
+  - xview2
+size_categories:
+  - 10K<n<100K
+pretty_name: xView2 Multi-Language Disaster Recognition Dataset
+---
+
+# xView2 Multi-Language Disaster Recognition Dataset
+
+This dataset is derived from the xBD (xView2) Building Damage Assessment Dataset and has been reformatted for Vision-Language Model (VLM) training with multi-language support.
+
+## 📊 Dataset Overview
+
+This dataset contains satellite imagery paired with multi-language conversational annotations for disaster recognition tasks. It supports three languages: **English**, **Chinese (中文)**, and **Japanese (日本語)**.
+
+### Dataset Splits
+
+- **Training Set (tier3)**: 9,168 image pairs → 55,008 conversations
+- **Test Set**: 933 image pairs → 5,598 conversations
+- **Total**: 10,101 image pairs → 60,606 multi-language conversations
+
+Each image pair consists of:
+- Pre-disaster satellite image
+- Post-disaster satellite image
+- Corresponding segmentation masks
+- Building damage labels
+- Metadata (capture date, sun position, sensor info)
+
+## 🗂️ Dataset Structure
+
+### Downloadable Files (Available on HuggingFace)
+
+The dataset is provided as compressed archives to facilitate downloading:
+
+```
+xview2/
+├── xview2_train.tar.gz             # Training split (8.04 GB compressed)
+├── xview2_tier3.tar.gz             # Additional training data (17.79 GB compressed)
+├── xview2_test.tar.gz              # Test split (2.67 GB compressed)
+├── xview2_train_tier3.json         # Training metadata
+├── xview2_test.json                # Test metadata
+├── xview2_train_tier3_sharegpt.json # Training conversations (ShareGPT format)
+├── xview2_test_sharegpt.json       # Test conversations (ShareGPT format)
+├── verify_dataset.py               # Dataset integrity verification script
+├── README.md                       # This file
+└── samples/images/                 # Sample images for preview
+    ├── guatemala-volcano_00000000_pre_disaster.png
+    ├── guatemala-volcano_00000000_post_disaster.png
+    ├── hurricane-florence_00000004_post_disaster.png
+    └── santa-rosa-wildfire_00000000_post_disaster.png
+```
+
+### After Extraction
+
+Once you extract the compressed archives, the structure will be:
+
+```
+xview2/
+├── train/                          # Training split (extracted from xview2_train.tar.gz)
+│   ├── images/                     # Satellite images (pre/post disaster)
+│   ├── masks/                      # Segmentation masks
+│   ├── color_masks/                # Visualization masks
+│   └── labels/                     # Building annotations (JSON)
+├── tier3/                          # Additional training data (extracted from xview2_tier3.tar.gz)
+│   ├── images/
+│   ├── masks/
+│   ├── color_masks/
+│   └── labels/
+├── test/                           # Test split (extracted from xview2_test.tar.gz)
+│   ├── images/
+│   ├── masks/
+│   ├── color_masks/
+│   └── labels/
+└── ... (metadata and conversation files)
+```
+
+## 🌍 Disaster Types
+
+The dataset covers 6 types of natural disasters:
+
+| Type | English | 中文 | 日本語 | Examples |
+|------|---------|------|--------|----------|
+| volcano | Volcano | 火山 | 火山 | Guatemala volcano |
+| flooding | Flooding | 洪水 | 洪水 | Hurricane Florence, Hurricane Harvey |
+| wind | Wind damage | 风灾 | 風災 | Hurricane Matthew, Hurricane Michael |
+| earthquake | Earthquake | 地震 | 地震 | Mexico earthquake |
+| tsunami | Tsunami | 海啸 | 津波 | Palu tsunami |
+| fire | Fire | 火灾 | 火災 | Santa Rosa wildfire, SoCal fire |
+
+## 🖼️ Sample Images
+
+The `samples/images/` directory contains example images for preview:
+
+- **Guatemala Volcano (Pre-disaster)**: `guatemala-volcano_00000000_pre_disaster.png`
+- **Guatemala Volcano (Post-disaster)**: `guatemala-volcano_00000000_post_disaster.png`
+- **Hurricane Florence (Post-disaster)**: `hurricane-florence_00000004_post_disaster.png`
+- **Santa Rosa Wildfire (Post-disaster)**: `santa-rosa-wildfire_00000000_post_disaster.png`
+
+## 💬 Conversation Format
+
+The dataset uses ShareGPT format with two-turn conversations:
+
+### Real Example: Guatemala Volcano (Post-disaster - Chinese)
+
+**Image**: `train/images/guatemala-volcano_00000000_post_disaster.png` (see sample above)
+
+```json
+{
+  "id": "xview2_train_000000_post_zh",
+  "images": ["data/xview2/train/images/guatemala-volcano_00000000_post_disaster.png"],
+  "messages": [
+    {
+      "from": "human",
+      "value": "<image>\n这张图片中发生了灾害吗？"
+    },
+    {
+      "from": "gpt",
+      "value": "是的，这张图片中发生了灾害。这是灾后图像。"
+    },
+    {
+      "from": "human",
+      "value": "这张图片中发生了什么类型的灾害？"
+    },
+    {
+      "from": "gpt",
+      "value": "这张图片中发生了火山灾害。"
+    }
+  ]
+}
+```
+
+### Real Example: Guatemala Volcano (Pre-disaster - English)
+
+**Image**: `train/images/guatemala-volcano_00000000_pre_disaster.png` (see sample above)
+
+```json
+{
+  "id": "xview2_train_000000_pre_en",
+  "images": ["data/xview2/train/images/guatemala-volcano_00000000_pre_disaster.png"],
+  "messages": [
+    {
+      "from": "human",
+      "value": "<image>\nHas a disaster occurred in this image?"
+    },
+    {
+      "from": "gpt",
+      "value": "No, no disaster has occurred in this image. This is a pre-disaster image."
+    },
+    {
+      "from": "human",
+      "value": "What type of disaster occurred in this image?"
+    },
+    {
+      "from": "gpt",
+      "value": "No disaster has occurred. This image was taken before the disaster."
+    }
+  ]
+}
+```
+
+### Real Example: Guatemala Volcano (Post-disaster - Japanese)
+
+```json
+{
+  "id": "xview2_train_000000_post_ja",
+  "images": ["data/xview2/train/images/guatemala-volcano_00000000_post_disaster.png"],
+  "messages": [
+    {
+      "from": "human",
+      "value": "<image>\nこの画像では災害が発生していますか？"
+    },
+    {
+      "from": "gpt",
+      "value": "はい、この画像では災害が発生しています。これは災害後の画像です。"
+    },
+    {
+      "from": "human",
+      "value": "この画像ではどのような種類の災害が発生しましたか？"
+    },
+    {
+      "from": "gpt",
+      "value": "この画像では火山災害が発生しました。"
+    }
+  ]
+}
+```
+
+## 📚 Original Dataset Citation
+
+This dataset is based on the **xBD (xView2) Dataset**:
+
+```bibtex
+@InProceedings{Gupta_2019_CVPR_Workshops,
+  author = {Gupta, Ritwik and Goodman, Bryce and Patel, Nirav and Hosfelt, Ricky and Sajeev, Sandra and Heim, Eric and Doshi, Jigar and Lucas, Keane and Choset, Howie and Gaston, Matthew},
+  title = {Creating xBD: A Dataset for Assessing Building Damage from Satellite Imagery},
+  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
+  month = {June},
+  year = {2019},
+  pages = {10-17}
+}
+```
+
+**Paper Abstract**: xBD is a large-scale dataset for the advancement of change detection and building damage assessment for humanitarian assistance and disaster recovery research. The dataset provides pre- and post-event multi-band satellite imagery from a variety of disaster events with building polygons, classification labels for damage types, ordinal labels of damage level, and corresponding satellite metadata. xBD contains ~700,000 building annotations across over 5,000 km² of imagery from 15 countries.
+
+## 🔗 Data Source
+
+- **Original Dataset**: [https://xview2.org/dataset](https://xview2.org/dataset)
+- **License**: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
+
+## 📋 License
+
+This derivative dataset follows the original license:
+
+**CC BY-NC-SA 4.0** - You are free to:
+- **Share** — copy and redistribute the material in any medium or format
+- **Adapt** — remix, transform, and build upon the material
+
+Under the following terms:
+- **Attribution** — You must give appropriate credit, provide a link to the license, and indicate if changes were made
+- **NonCommercial** — You may not use the material for commercial purposes
+- **ShareAlike** — If you remix, transform, or build upon the material, you must distribute your contributions under the same license
+
+## 🎯 Use Cases
+
+This dataset is suitable for:
+
+1. **Vision-Language Model Training**: Multi-modal models that understand disaster imagery
+2. **Multi-language AI Systems**: Models that can communicate about disasters in multiple languages
+3. **Disaster Assessment**: Automated systems for rapid disaster type identification
+4. **Change Detection**: Pre/post disaster image comparison
+5. **Humanitarian AI**: Applications for disaster response and recovery
+
+## 📦 How to Use
+
+### Step 1: Download and Extract
+
+```bash
+# Download from HuggingFace, then extract
+tar -xzf xview2_train.tar.gz
+tar -xzf xview2_tier3.tar.gz
+tar -xzf xview2_test.tar.gz
+```
+
+### Step 2: Verify Dataset Integrity
+
+```bash
+python verify_dataset.py
+```
+
+**Expected Output**: 
+```
+Verifying dataset integrity... 
+✅ Dataset is ready
+```
+
+For detailed verification report:
+```bash
+python verify_dataset.py --verbose
+```
+
+### Step 3: Load and Use
+
+```python
+import json
+from PIL import Image
+
+# Load conversations
+with open('xview2_train_tier3_sharegpt.json', 'r', encoding='utf-8') as f:
+    conversations = json.load(f)
+
+# Get first conversation
+conv = conversations[0]
+
+# Load image
+image = Image.open(conv['images'][0])
+
+# Access conversation
+print(conv['messages'][0]['value'])  # Question 1
+print(conv['messages'][1]['value'])  # Answer 1
+```
+
+## 📚 Citation
+
+Please cite the original xBD dataset:
+
+```bibtex
+@InProceedings{Gupta_2019_CVPR_Workshops,
+  author = {Gupta, Ritwik and Goodman, Bryce and Patel, Nirav and Hosfelt, Ricky and Sajeev, Sandra and Heim, Eric and Doshi, Jigar and Lucas, Keane and Choset, Howie and Gaston, Matthew},
+  title = {Creating xBD: A Dataset for Assessing Building Damage from Satellite Imagery},
+  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
+  month = {June},
+  year = {2019},
+  pages = {10-17}
+}
+```
+
+## 📋 License
+
+**CC BY-NC-SA 4.0** - [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)
+
+Original dataset: [https://xview2.org/dataset](https://xview2.org/dataset)
+

UPLOAD_INSTRUCTIONS.md

@@ -0,0 +1,89 @@
+# HuggingFace Upload Instructions
+
+## Step 1: Install HuggingFace CLI
+
+```bash
+cd /home/ohya-bob/Documents/DisasterSynth
+source .venv/bin/activate
+uv pip install huggingface-hub[cli]
+```
+
+## Step 2: Login to HuggingFace
+
+```bash
+huggingface-cli login
+```
+
+Enter your HuggingFace token when prompted.
+
+## Step 3: Create Dataset Repository
+
+Go to https://huggingface.co/new-dataset and create a new dataset repository.
+
+Example name: `your-username/xview2-disaster-vlm`
+
+## Step 4: Upload Dataset
+
+```bash
+cd /home/ohya-bob/Documents/DisasterSynth/data/xview2/zipped
+
+# Upload all files to your dataset repository
+huggingface-cli upload your-username/xview2-disaster-vlm . . --repo-type dataset
+```
+
+**Note**: Replace `your-username/xview2-disaster-vlm` with your actual repository name.
+
+## Alternative: Upload Large Files Individually
+
+If the upload times out, upload large files separately:
+
+```bash
+cd /home/ohya-bob/Documents/DisasterSynth/data/xview2/zipped
+
+# Upload compressed archives
+huggingface-cli upload your-username/xview2-disaster-vlm xview2_train.tar.gz xview2_train.tar.gz --repo-type dataset
+huggingface-cli upload your-username/xview2-disaster-vlm xview2_tier3.tar.gz xview2_tier3.tar.gz --repo-type dataset
+huggingface-cli upload your-username/xview2-disaster-vlm xview2_test.tar.gz xview2_test.tar.gz --repo-type dataset
+
+# Upload JSON files
+huggingface-cli upload your-username/xview2-disaster-vlm xview2_train_tier3_sharegpt.json xview2_train_tier3_sharegpt.json --repo-type dataset
+huggingface-cli upload your-username/xview2-disaster-vlm xview2_test_sharegpt.json xview2_test_sharegpt.json --repo-type dataset
+huggingface-cli upload your-username/xview2-disaster-vlm xview2_train_tier3.json xview2_train_tier3.json --repo-type dataset
+huggingface-cli upload your-username/xview2-disaster-vlm xview2_test.json xview2_test.json --repo-type dataset
+
+# Upload documentation and scripts
+huggingface-cli upload your-username/xview2-disaster-vlm README.md README.md --repo-type dataset
+huggingface-cli upload your-username/xview2-disaster-vlm verify_dataset.py verify_dataset.py --repo-type dataset
+
+# Upload samples folder
+huggingface-cli upload your-username/xview2-disaster-vlm samples samples --repo-type dataset
+```
+
+## Step 5: Set Repository Metadata
+
+After upload, edit your dataset card on HuggingFace:
+
+- **License**: CC BY-NC-SA 4.0
+- **Tags**: `computer-vision`, `disaster-detection`, `multi-language`, `satellite-imagery`, `xview2`
+- **Languages**: English, Chinese, Japanese
+
+## Files to Upload (Total: ~29.2 GB)
+
+- [x] xview2_train.tar.gz (8.04 GB)
+- [x] xview2_tier3.tar.gz (17.79 GB)
+- [x] xview2_test.tar.gz (2.67 GB)
+- [x] xview2_train_tier3_sharegpt.json (~1.26 GB)
+- [x] xview2_test_sharegpt.json (~14 MB)
+- [x] xview2_train_tier3.json (~195 MB)
+- [x] xview2_test.json (~20 MB)
+- [x] README.md
+- [x] verify_dataset.py
+- [x] samples/images/ (4 images)
+
+## Troubleshooting
+
+If upload fails:
+- Try uploading smaller files first
+- Use `--resume` flag to resume interrupted uploads
+- Split very large files if needed
+

verify_dataset.py

@@ -0,0 +1,260 @@
+#!/usr/bin/env python3
+"""
+Verify xView2 dataset integrity
+Checks that all files referenced in JSON metadata exist
+"""
+
+import json
+from pathlib import Path
+from tqdm import tqdm
+from collections import defaultdict
+from typing import Dict, List, Tuple
+
+
+def verify_dataset_split(
+    json_file: Path,
+    base_dir: Path,
+    split_name: str,
+    verbose: bool = False
+) -> Tuple[bool, Dict]:
+    """
+    Verify a single dataset split
+    
+    Args:
+        json_file: Path to JSON metadata file
+        base_dir: Base directory containing the dataset
+        split_name: Name of the split (train/test)
+        verbose: Print detailed statistics
+    
+    Returns:
+        Tuple of (all_valid, statistics)
+    """
+    if not json_file.exists():
+        if verbose:
+            print(f"❌ JSON file not found: {json_file}")
+        return False, {}
+    
+    # Load JSON
+    with open(json_file, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+    
+    # Statistics
+    stats = {
+        'total_entries': len(data),
+        'missing_files': [],
+        'disaster_types': defaultdict(int),
+        'valid_entries': 0,
+        'invalid_entries': 0
+    }
+    
+    # Check each entry
+    all_valid = True
+    
+    # Use tqdm only if verbose
+    iterator = tqdm(data, desc=f"Checking {split_name}", unit="entry", disable=not verbose) if verbose else data
+    
+    for idx, entry in enumerate(iterator):
+        entry_valid = True
+        
+        # Count disaster types
+        disaster_type = entry.get('disaster_type', 'unknown')
+        stats['disaster_types'][disaster_type] += 1
+        
+        # Check all required fields
+        required_fields = [
+            'pre_disaster_image',
+            'post_disaster_image',
+            'pre_disaster_mask',
+            'post_disaster_mask',
+            'disaster',
+            'disaster_type'
+        ]
+        
+        for field in required_fields:
+            if field not in entry:
+                stats['missing_files'].append({
+                    'entry_idx': idx,
+                    'field': field,
+                    'reason': 'Field missing from JSON'
+                })
+                entry_valid = False
+                continue
+            
+            # Check if file exists (for image/mask paths)
+            if field.endswith('_image') or field.endswith('_mask') or field.endswith('_colormask'):
+                file_path = base_dir / entry[field]
+                if not file_path.exists():
+                    stats['missing_files'].append({
+                        'entry_idx': idx,
+                        'field': field,
+                        'path': str(file_path),
+                        'reason': 'File not found'
+                    })
+                    entry_valid = False
+        
+        if entry_valid:
+            stats['valid_entries'] += 1
+        else:
+            stats['invalid_entries'] += 1
+            all_valid = False
+    
+    # Print errors if any
+    if not all_valid and verbose:
+        print(f"\n✗ Invalid entries: {stats['invalid_entries']}")
+        print(f"✗ Missing files: {len(stats['missing_files'])}")
+        if stats['missing_files']:
+            print(f"\nFirst 5 missing files:")
+            for missing in stats['missing_files'][:5]:
+                print(f"  - Entry {missing['entry_idx']}: {missing['field']} - {missing['reason']}")
+                if 'path' in missing:
+                    print(f"    Path: {missing['path']}")
+    
+    return all_valid, stats
+
+
+def verify_sharegpt_format(
+    sharegpt_file: Path,
+    split_name: str,
+    verbose: bool = False
+) -> Tuple[bool, Dict]:
+    """
+    Verify ShareGPT format file
+    
+    Args:
+        sharegpt_file: Path to ShareGPT JSON file
+        split_name: Name of the split
+        verbose: Print detailed statistics
+    
+    Returns:
+        Tuple of (all_valid, statistics)
+    """
+    if not sharegpt_file.exists():
+        if verbose:
+            print(f"❌ ShareGPT file not found: {sharegpt_file}")
+        return False, {}
+    
+    # Load JSON
+    with open(sharegpt_file, 'r', encoding='utf-8') as f:
+        conversations = json.load(f)
+    
+    stats = {
+        'total_conversations': len(conversations),
+        'valid_conversations': 0,
+        'invalid_conversations': 0,
+        'languages': defaultdict(int),
+        'image_types': defaultdict(int),
+        'issues': []
+    }
+    
+    all_valid = True
+    
+    # Use tqdm only if verbose
+    iterator = tqdm(conversations, desc=f"Checking ShareGPT {split_name}", unit="conv", disable=not verbose) if verbose else conversations
+    
+    for idx, conv in enumerate(iterator):
+        conv_valid = True
+        
+        # Check required fields
+        if 'id' not in conv:
+            stats['issues'].append(f"Entry {idx}: Missing 'id' field")
+            conv_valid = False
+        else:
+            # Extract language and type from ID
+            parts = conv['id'].split('_')
+            if len(parts) >= 4:
+                img_type = parts[-2]  # pre or post
+                lang = parts[-1]      # en, zh, ja
+                stats['languages'][lang] += 1
+                stats['image_types'][img_type] += 1
+        
+        if 'images' not in conv or not conv['images']:
+            stats['issues'].append(f"Entry {idx}: Missing or empty 'images' field")
+            conv_valid = False
+        
+        if 'messages' not in conv or len(conv['messages']) != 4:
+            stats['issues'].append(f"Entry {idx}: Expected 4 messages, got {len(conv.get('messages', []))}")
+            conv_valid = False
+        else:
+            # Check message structure
+            messages = conv['messages']
+            expected_pattern = ['human', 'gpt', 'human', 'gpt']
+            actual_pattern = [m.get('from', '') for m in messages]
+            
+            if actual_pattern != expected_pattern:
+                stats['issues'].append(f"Entry {idx}: Unexpected message pattern {actual_pattern}")
+                conv_valid = False
+            
+            # Check first message has <image> tag
+            if messages[0].get('value', '').find('<image>') == -1:
+                stats['issues'].append(f"Entry {idx}: First message missing <image> tag")
+                conv_valid = False
+        
+        if conv_valid:
+            stats['valid_conversations'] += 1
+        else:
+            stats['invalid_conversations'] += 1
+            all_valid = False
+    
+    # Print errors if any
+    if not all_valid and verbose:
+        print(f"\n✗ Invalid conversations: {stats['invalid_conversations']}")
+        print(f"\nFirst 5 issues:")
+        for issue in stats['issues'][:5]:
+            print(f"  - {issue}")
+    
+    return all_valid, stats
+
+
+def main():
+    """Main verification function"""
+    
+    import sys
+    
+    # Check for verbose flag
+    verbose = '--verbose' in sys.argv or '-v' in sys.argv
+    
+    if not verbose:
+        print("Verifying dataset integrity...", end=" ", flush=True)
+    
+    # Define paths
+    data_root = Path("/home/ohya-bob/Documents/DisasterSynth/data/xview2")
+    
+    # Verify original metadata files
+    train_json = data_root / "xview2_train_tier3.json"
+    test_json = data_root / "xview2_test.json"
+    
+    train_valid, train_stats = verify_dataset_split(train_json, data_root, "train", verbose=verbose)
+    test_valid, test_stats = verify_dataset_split(test_json, data_root, "test", verbose=verbose)
+    
+    # Verify ShareGPT format files
+    train_sharegpt = data_root / "xview2_train_tier3_sharegpt.json"
+    test_sharegpt = data_root / "xview2_test_sharegpt.json"
+    
+    train_sharegpt_valid, train_sharegpt_stats = verify_sharegpt_format(train_sharegpt, "train", verbose=verbose)
+    test_sharegpt_valid, test_sharegpt_stats = verify_sharegpt_format(test_sharegpt, "test", verbose=verbose)
+    
+    # Overall summary
+    all_checks_passed = train_valid and test_valid and train_sharegpt_valid and test_sharegpt_valid
+    
+    if not verbose:
+        print("")  # New line after "Verifying..."
+    
+    if all_checks_passed:
+        print("✅ Dataset is ready")
+    else:
+        print("❌ Dataset verification failed")
+        print("\nIssues found:")
+        if not train_valid:
+            print("  - Training metadata has issues")
+        if not test_valid:
+            print("  - Test metadata has issues")
+        if not train_sharegpt_valid:
+            print("  - Training ShareGPT format has issues")
+        if not test_sharegpt_valid:
+            print("  - Test ShareGPT format has issues")
+        print("\nRun with --verbose flag for detailed information")
+
+
+if __name__ == "__main__":
+    main()
+

xview2_test.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:08f0665055c516fd93459096c633e404a18ab370ee97407ed550be60b4383e52
+size 2803334360

xview2_tier3.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:44d11892c206cfc7ac2e858a65a55d3d91a358919651d5872cad7ccd352c4b75
+size 18649392463

xview2_train.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0e56c650cb50c2982dd1223bad4d43900abaed60656903c3e5deff1869f2e807
+size 8431624516

xview2_train_tier3_sharegpt.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:647c385ff6182ca15d52e130b09d9c0aa90a1d20dee7953c16adb6a88079a95b
+size 36346896