euclid-multimodal
/

Euclid-convnext-xxlarge-120524

@@ -4,201 +4,89 @@ language:
 - en
 base_model:
 - Qwen/Qwen2.5-1.5B-Instruct
 library_name: transformers
-tags:
-- geometry
 ---
-# Model Card for Model ID
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 - en
 base_model:
 - Qwen/Qwen2.5-1.5B-Instruct
+- laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup
+pipeline_tag: question-answering
+metrics:
+- accuracy
 library_name: transformers
 ---
+# Model Card for Euclid-convnext-large
+A multimodal large language models specifically trained for strong low-level geometric perception.
 ## Model Details
 ### Model Description
+Euclid is trained on 1.6M synthetic geometry images with high-fidelity question-answer pairs using a curriculum learning approach.
+It combines a ConvNeXt visual encoder with a Qwen-2.5 language model, connected through a 2-layer MLP multimodal connector.
 ### Model Sources [optional]
+- **Repository:** https://github.com/euclid-multimodal/Euclid
+- **Paper:** [Paper Link]
+- **Demo:** [Demo Link if available]
 ## Uses
+The model is trained for precise low-level geometric perception tasks which is able to
+- Point-on-line detection
+- Point-on-circle detection
+- Angle classification
+- Length comparison
+- Geometric annotation understanding
+Please refer to our [repo](https://github.com/euclid-multimodal/Euclid) for full input format.
+### Limitations and Applications
+Our model is not designed to handle:
+- Comprehensive image understanding tasks
+- Advanced cognitive reasoning beyond geometric analysis
+However, the model demonstrates strength in low-level visual perception.
+This capability makes it potentially valuable for serving as a base model for specialized downstream fintuning including:
+- Robotic vision and automation systems
+- Medical imaging and diagnostic support
+- Industrial quality assurance and inspection
+- Geometric education and visualization tools
+### Example Usage
+Clone our Euclid [repo](https://github.com/euclid-multimodal/Euclid) first, set up the environment, then run:
+```
+pip install -U "huggingface_hub[cli]"
+huggingface-cli download --cache-dir $MODEL_PATH EuclidAI/Euclid-convnext-large
+python euclid/eval/run_euclid_geo.py --model_path $MODEL_PATH --device cuda
+```
+## Evaluation Results
+Performance on Geoperception benchmark tasks:
+| Model | POL | POC | ALC | LHC | PEP | PRA | EQL | Overall |
+|-------|-----|-----|-----|-----|-----|-----|-----|----------|
+| Random Baseline | 0.43 | 2.63 | 59.92 | 51.36 | 0.25 | 0.00 | 0.02 | 16.37 |
+| Pixtral-12B | 22.85 | 53.21 | 47.33 | 51.43 | 22.53 | 37.11 | **58.45** | 41.84 |
+| Gemini-1.5-Pro | 24.42 | **69.80** | 57.96 | 79.05 | **39.60** | **77.59** | 52.27 | 57.24 |
+| EUCLID-ConvNeXt-Large | 80.54 | 57.76 | 86.37 | 88.24 | 42.23 | 64.94 | 34.45 | 64.93 |
+| EUCLID-ConvNeXt-XXLarge | **82.98** | 61.45 | **90.56** | **90.82** | **46.96** | 70.52 | 31.94 | **67.89** |
+## Citation
+If you find Euclid useful for your research and applications, please cite using this BibTeX:
+```bibtex
+@misc{euclid,
+    title={Euclid: Supercharging Multimodal LLMs with Synthetic High-Fidelity Visual Descriptions},
+    url={https://euclid-llm.github.io/},
+    author={Zhang, Jiarui and Liu, Ollie and Yu, Tianyu and Hu, Jinyi and Neiswanger, Willie},
+    month={December},
+    year={2024}
+}