Update README.md

README.md

@@ -3,20 +3,18 @@ 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
+# 3D Printing Nozzle Images Dataset
 
 ### Dataset Summary
 
@@ -55,29 +53,6 @@ Each sample contains:
 | 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:
@@ -176,15 +151,20 @@ If you use this dataset in your research, please cite:
 }
 ```
 
+```bibtex
+@article{MargadjiPattinson2025HybridReasoning,
+  title   = {Hybrid Reasoning for Perception, Explanation, and Autonomous Action in Manufacturing},
+  author  = {Margadji, Christos and Pattinson, Sebastian W.},
+  year    = {2025},
+  note    = {arXiv:2506.08462},
+  url     = {https://arxiv.org/abs/2506.08462}
+}
+```
+
 ## 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.
-
+For questions or issues regarding this dataset, please open an issue on the dataset repository or email at cm2161@cam.ac.uk