Upload README.md with huggingface_hub

README.md

@@ -1,33 +1,232 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: flow_rate
-    dtype: float32
-  - name: nozzle_tip_x
-    dtype: int32
-  - name: nozzle_tip_y
-    dtype: int32
-  splits:
-  - name: train
-    num_bytes: 34131230.33
-    num_examples: 3405
-  - name: validation
-    num_bytes: 3230593.0
-    num_examples: 330
-  - name: test
-    num_bytes: 3108124.0
-    num_examples: 309
-  download_size: 40509287
-  dataset_size: 40469947.33
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
+license: mit
+task_categories:
+- image-classification
+- visual-question-answering
+- computer-vision
+- image-to-text
+tags:
+- 3d-printing
+- manufacturing
+- quality-control
+- vision-language
+- flow-rate-estimation
+size_categories:
+- 1K<n<10K
+pretty_name: TL-Caxton - 3D Printing Quality Assessment Dataset
 ---
+
+# TL-Caxton: 3D Printing Nozzle Images Dataset
+
+### Dataset Summary
+
+- **Task**: Vision-based flow rate estimation and extrusion quality assessment
+- **Domain**: Additive Manufacturing / 3D Printing
+- **Data Type**: RGB images with numerical annotations
+- **Total Samples**: 4,048 images
+  - Training: 3,407 samples
+  - Validation: 331 samples
+  - Test: 310 samples
+
+### Supported Tasks
+
+1. **Flow Rate Regression**: Predict the flow rate percentage from camera images of the printing process
+2. **Extrusion Quality Classification**: Classify prints as under-extruded (<90%), good extrusion (90-110%), or over-extruded (>110%)
+3. **Vision-Language Modeling**: Generate natural language descriptions of print quality from images
+4. **Visual Question Answering**: Answer questions about print parameters and quality from images
+
+## Dataset Structure
+
+### Data Fields
+
+Each sample contains:
+
+- **`img_path`** (string): Filename of the camera image
+- **`flow_rate`** (float): Flow rate percentage value (ranging from ~39% to ~265%)
+- **`nozzle_tip_x`** (int): X-coordinate of nozzle tip position in pixels
+- **`nozzle_tip_y`** (int): Y-coordinate of nozzle tip position in pixels
+
+### Data Splits
+
+| Split | Samples | Percentage |
+|-------|---------|------------|
+| Train | 3,407   | 84.2%      |
+| Validation | 331 | 8.2%       |
+| Test  | 310     | 7.6%       |
+| **Total** | **4,048** | **100%** |
+
+### Data Characteristics
+
+- **Flow Rate Range**: 39% to 265%
+  - Under-extrusion: < 90%
+  - Good extrusion: 90-110%
+  - Over-extrusion: > 110%
+- **Image Format**: JPEG
+- **Camera**: Single fixed camera (camera1)
+- **Printer**: CCR20PRO
+- **Print Types**: Various geometries including cylinders, cones, cubes, polygons, hashtag patterns, and more
+
+## Dataset Creation
+
+### Image Acquisition
+
+Images were captured during 3D printing processes using a fixed camera setup. The dataset includes prints of various geometric shapes with different layer heights (1.0mm and 5.0mm) and flow rate settings.
+
+### Annotations
+
+Each image is annotated with:
+- Ground truth flow rate values used during printing
+- Nozzle tip coordinates for potential attention mechanisms or spatial reasoning tasks
+
+### Qualitative Descriptions
+
+The dataset includes JSON template files for generating natural language descriptions:
+
+- **`general_statements.json`**: General observations about the 3D printing nozzle and process
+- **`qual_good_extrusion.json`**: Descriptions of good extrusion quality (flow rate 90-110%)
+- **`qual_under_extrusion.json`**: Descriptions of under-extrusion issues (flow rate < 90%)
+- **`qual_over_extrusion.json`**: Descriptions of over-extrusion issues (flow rate > 110%)
+- **`quant_templates.json`**: Templates for stating quantitative flow rate values
+
+These templates enable synthetic generation of diverse natural language annotations for vision-language training.
+
+## Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load the full dataset
+dataset = load_dataset("cemag/tl-caxton")
+
+# Access individual splits
+train_data = dataset['train']
+val_data = dataset['validation']
+test_data = dataset['test']
+
+# Example: Access a sample
+sample = train_data[0]
+print(f"Flow rate: {sample['flow_rate']}%")
+print(f"Nozzle position: ({sample['nozzle_tip_x']}, {sample['nozzle_tip_y']})")
+```
+
+### Using with PyTorch
+
+```python
+from torch.utils.data import DataLoader
+from PIL import Image
+import os
+
+class CIPHERDataset:
+    def __init__(self, dataset, image_dir, transform=None):
+        self.dataset = dataset
+        self.image_dir = image_dir
+        self.transform = transform
+    
+    def __len__(self):
+        return len(self.dataset)
+    
+    def __getitem__(self, idx):
+        sample = self.dataset[idx]
+        img_path = os.path.join(self.image_dir, sample['img_path'])
+        image = Image.open(img_path).convert('RGB')
+        
+        if self.transform:
+            image = self.transform(image)
+        
+        return {
+            'image': image,
+            'flow_rate': sample['flow_rate'],
+            'nozzle_tip': (sample['nozzle_tip_x'], sample['nozzle_tip_y'])
+        }
+
+# Create dataset and dataloader
+train_dataset = CIPHERDataset(train_data, 'images/', transform=your_transform)
+train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
+```
+
+### Vision-Language Training
+
+```python
+from data_utils import synthesize_answer, format_data
+
+# Generate a natural language description for a sample
+sample = train_data[0]
+description = synthesize_answer(sample, general=True, quant=True, qual=True)
+print(description)
+
+# Example output:
+# "This is the nozzle of a 3D printer. The observed flow rate is approximately 
+#  100%. Good extrusion occurs when a 3D printer delivers the exact amount of 
+#  filament needed, resulting in strong, accurate, and visually appealing prints."
+```
+
+## Applications
+
+This dataset is suitable for:
+
+1. **Automated Quality Control**: Develop models to automatically detect printing defects
+2. **Flow Rate Calibration**: Train systems to recommend optimal flow rate settings
+3. **Vision-Language Models**: Fine-tune multimodal models for manufacturing domain
+4. **Predictive Maintenance**: Identify early signs of printing issues
+5. **Education**: Teaching resource for 3D printing optimization and machine learning
+
+## Dataset Statistics
+
+### Flow Rate Distribution
+
+The dataset contains a diverse range of flow rates to capture various extrusion conditions:
+- Minimum: ~39%
+- Maximum: ~265%
+- Coverage includes under-extrusion, optimal, and over-extrusion scenarios
+
+### Geometric Diversity
+
+Print geometries include:
+- Cylinders and cones
+- Test cubes
+- Polygons (30x70, 40x70)
+- Hashtag patterns
+- Gears and complex geometries
+- Tetris shapes
+- Custom cuts and patterns
+
+### Layer Heights
+
+- 1.0mm layer height
+- 5.0mm layer height
+
+## Limitations
+
+- Single camera angle (camera1 only)
+- Limited to one printer model (CCR20PRO)
+- No temporal sequences (single frames only)
+- Nozzle position annotations are 2D pixel coordinates, not 3D world coordinates
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@dataset{tl_caxton,
+  title={tl-Caxton: 3D Printing Quality Assessment Dataset},
+  author={cemag},
+  year={2025},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/datasets/cemag/tl-caxton}}
+}
+```
+
+## License
+
+This dataset is released under the MIT License.
+
+## Contact
+
+For questions or issues regarding this dataset, please open an issue on the dataset repository.
+
+## Acknowledgments
+
+This dataset was created to advance automated quality control in additive manufacturing through computer vision and machine learning techniques.
+