Update model card readme.md (#1)

- Update model card readme.md (44b4e9310df6faeac24dbc50079de7a9addd63e8)


Co-authored-by: Abhay Puri <[email protected]>

README.md

@@ -1,199 +1,151 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+language:
+- en
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# Model Card for StarVector
 
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/65c27c201b5b51dd4814fcd2/ULL7FkrMHA38I8olD7nEh.png)
 
+StarVector is a foundation model for generating Scalable Vector Graphics (SVG) code from images and text. It utilizes a Vision-Language Modeling architecture to understand both visual and textual inputs, enabling high-quality vectorization and text-guided SVG creation.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+This is the model card for the StarVector model, a 🤗 transformers model. StarVector is a foundation model for generating Scalable Vector Graphics (SVG) code from images and text. It utilizes a Vision-Language Modeling architecture to understand both visual and textual inputs, enabling high-quality vectorization and text-guided SVG creation.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **Developed by:** ServiceNow Research, Mila - Quebec AI Institute, ETS, Montreal.
+- **Shared by :** Juan A Rodriguez, Abhay Puri, Shubham Agarwal, Issam H. Laradji, Sai Rajeswar, Pau Rodriguez, David Vazquez, Christopher Pal, Marco Pedersoli.
+- **Model type:** Vision-Language Model for SVG Generation.
+- **Language(s) (NLP):** English.
+- **License:** Apache 2.0
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+### Model Architecture
 
-### Model Sources [optional]
+The StarVector architecture integrates an image encoder and a Large Language Model (LLM) Adapter to generate SVG code from both image and text inputs. Images are first converted into embeddings using a Vision Transformer (ViT), after which the LLM Adapter maps these embeddings into the LLM's embedding space to create visual tokens. Text prompts are handled through the LLM’s tokenizer and embedder. This unified multimodal approach ensures precise and contextually rich SVG output.
 
-<!-- Provide the basic links for the model. -->
+<figure>
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/65c27c201b5b51dd4814fcd2/IVGxASfNr8wfu-agH9Nqj.png" alt="Figure 2: StarVector Architecture">
+  <figcaption>Figure 2: a) StarVector Architecture: StarVector projects images into embeddings via an image encoder, then maps these embeddings to the LLM hidden space using an LLM Adapter, generating Visual Tokens. Text conditioning is achieved with the LLM's tokenizer and embedder. The model learns to map token sequences (visual or textual) to SVG code. The symbol ⊕ denotes mutually exclusive operations (image-to- SVG or text-to-SVG), while ‖ indicates sequence concatenation. Figure 2: b)Vision Model and Adapter: The image encoder employs a Vision Transformer (ViT) to process image patches sequentially. The LLM Adapter non-linearly projects embeddings into visual tokens for LLM integration.</figcaption>
+</figure>
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
-## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+### Model Sources
 
-### Direct Use
+- **Repository:** [https://github.com/joanrod/star-vector](https://github.com/joanrod/star-vector)
+- **Paper:** [https://arxiv.org/abs/2312.11556](https://arxiv.org/abs/2312.11556)
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+## Uses
 
-[More Information Needed]
+### Direct Use
 
-### Downstream Use [optional]
+Image-to-SVG generation, Text-to-SVG generation.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Downstream Use
 
-[More Information Needed]
+Creation of icons, logotypes, technical diagrams, and other vector graphics.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+Generating realistic photographic images or complex 3D graphics.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+Potential biases may exist in the model due to the composition of the training data (SVG-Stack). The model's ability to perfectly vectorize all types of images and interpret all textual instructions may have limitations. Users should be aware of these potential issues, especially in critical applications.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Further investigation into the model's behavior across different types of inputs is recommended.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+```Python
+from PIL import Image
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor
+from starvector.data.util import process_and_rasterize_svg
+import torch
+ 
+model_name = "starvector/starvector-1b-im2svg"
+ 
+starvector = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, trust_remote_code=True)
+processor = starvector.model.processor
+tokenizer = starvector.model.svg_transformer.tokenizer
+ 
+starvector.cuda()
+starvector.eval()
+ 
+image_pil = Image.open('assets/examples/sample-18.png')
+ 
+image = processor(image_pil, return_tensors="pt")['pixel_values'].cuda()
+if not image.shape[0] == 1:
+    image = image.squeeze(0)
+batch = {"image": image}
+ 
+raw_svg = starvector.generate_im2svg(batch, max_length=4000)[0]
+svg, raster_image = process_and_rasterize_svg(raw_svg)
+```
 
 ## Training Details
-
 ### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
+SVG-Stack: A dataset of over 2 million SVG samples.
 
 ### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+The model utilizes a Vision-Language Modeling architecture. Images are projected into embeddings via an image encoder, then mapped to the LLM hidden space using an LLM Adapter, generating Visual Tokens. Text conditioning is achieved with the LLM's tokenizer and embedder. The model learns to map token sequences (visual or textual) to SVG code.
 
 ## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
+### Testing Data & Factors
 #### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+SVG-Bench
 
 #### Factors
+SVG-Stack, SVG-Fonts, SVG-Icons, SVG-Emoji, SVG-Diagrams.
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
+## Models
 
-**APA:**
+StarVector models achieve state-of-the-art performance on SVG generation tasks
 
-[More Information Needed]
+We provide [Hugging Face 🤗 model checkpoints](https://huggingface.co/collections/starvector/starvector-models-6783b22c7bd4b43d13cb5289) for image2SVG vectorization, for 💫 StarVector-8B and 💫 StarVector-1B. These are the results on SVG-Bench, using the DinoScore metric.
 
-## Glossary [optional]
+| Method             | SVG-Stack | SVG-Fonts | SVG-Icons | SVG-Emoji | SVG-Diagrams |
+|--------------------|-----------|-----------|-----------|-----------|--------------|
+| AutoTrace          | 0.942     | 0.954     | 0.946     | 0.975     | 0.874        |
+| Potrace            | 0.898     | 0.967     | 0.972     | 0.882     | 0.875        |
+| VTracer            | 0.954     | 0.964     | 0.940     | 0.981     | 0.882        |
+| Im2Vec             | 0.692     | 0.733     | 0.754     | 0.732     | -            |
+| LIVE               | 0.934     | 0.956     | 0.959     | 0.969     | 0.870        |
+| DiffVG             | 0.810     | 0.821     | 0.952     | 0.814     | 0.822        |
+| GPT-4-V            | 0.852     | 0.842     | 0.848     | 0.850     | -            |
+| 💫 **StarVector-1B** | 0.926     | 0.978     | 0.975     | 0.929     | 0.943        |
+| 💫 **StarVector-8B** | 0.966     | 0.982     | 0.984     | 0.981     | 0.959        |
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+**Note:** StarVector models will not work for natural images or illustrations, as they have not been trained on those images. They excel in vectorizing icons, logotypes, technical diagrams, graphs, and charts.
 
-[More Information Needed]
+As shown in the table above, StarVector-8B achieves the highest performance across all benchmark datasets, demonstrating its effectiveness in generating high-quality SVG code from images. The model's ability to understand and reproduce complex vector graphics makes it particularly valuable for applications requiring precise vectorization of icons, logos, and technical diagrams.
 
-## More Information [optional]
+## Summary
+StarVector represents a significant advancement in the field of vector graphics generation. By combining the power of vision-language models with a comprehensive training dataset, we've created a system that can accurately translate images into high-quality SVG code. The model's performance on SVG-Bench demonstrates its effectiveness across a wide range of vector graphics tasks.
 
-[More Information Needed]
+We believe that StarVector will enable new applications in design, illustration, and technical documentation, making vector graphics more accessible and easier to create. We invite the research community to build upon our work and explore new directions in this exciting field.
 
-## Model Card Authors [optional]
+For more details, please refer to our [paper](https://arxiv.org/abs/2312.11556) and explore our [code](https://github.com/joanrod/star-vector) repository.
 
-[More Information Needed]
+## BibTeX entry and citation info
 
-## Model Card Contact
 
-[More Information Needed]
+```
+@misc{rodriguez2024starvector,
+      title={StarVector: Generating Scalable Vector Graphics Code from Images and Text},
+      author={Juan A. Rodriguez and Abhay Puri and Shubham Agarwal and Issam H. Laradji and Pau Rodriguez and Sai Rajeswar and David Vazquez and Christopher Pal and Marco Pedersoli},
+      year={2024},
+      eprint={2312.11556},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2312.11556},
+}
+```