Update README.md

README.md

@@ -4,35 +4,168 @@ tags:
 - spec-vision
 - vision-language-model
 - transformers
-license: apache-2.0
+license: mit
 ---
 
-# SpecVision Model
+# Model Summary
 
-This is the SpecVision model, a vision-language model based on the transformers architecture.
+Spec-Vision-V1 is a lightweight, state-of-the-art open multimodal model built on datasets that include synthetic data and filtered publicly available sources, with a focus on high-quality, reasoning-dense data in both text and vision. The model belongs to the SpecVision family and supports a 128K context length (in tokens). It has undergone a rigorous enhancement process, incorporating supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures.
 
-## Model Description
+# 🚀 Model Overview  
 
-SpecVision is designed for vision-language tasks, combining visual and textual understanding capabilities.
+**Spec-Vision-V1** is built for **deep integration of visual and textual data**, enabling it to understand and process images in combination with natural language. The model has been trained on a diverse dataset containing images with associated captions, descriptions, and contextual information.  
 
-## Usage
+### ✨ Key Features
+
+- **🖼️ Multimodal Processing**: Seamlessly combines image and text inputs.
+- **⚡ Transformer-Based Architecture**: High efficiency in vision-language understanding.
+- **📝 Optimized for VQA & Captioning**: Excels in answering visual questions and generating descriptions.
+- **📥 Pre-trained Model**: Available for inference and fine-tuning.
+
+---
+
+## 📌 Installation
+
+To use Spec-Vision-V1, install the required dependencies:
+
+```bash
+pip install transformers torch torchvision pillow
+```
+
+---
+
+## 🔥 Usage
+
+### Load the Model
 
 ```python
-from transformers import AutoConfig, AutoModelForCausalLM, AutoProcessor
+from transformers import AutoModelForCausalLM, AutoProcessor
+from PIL import Image
+import torch
 
 # Load the model and processor
-model = AutoModelForCausalLM.from_pretrained("Spec-4B-Vision-V1")
-processor = AutoProcessor.from_pretrained("Spec-4B-Vision-V1")
+model_name = "Spec-Vision-V1"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+processor = AutoProcessor.from_pretrained(model_name)
+
+# Load an example image
+image = Image.open("example.jpg")
+
+# Input text prompt
+text = "Describe the image in detail."
 
 # Process inputs
 inputs = processor(images=image, text=text, return_tensors="pt")
-outputs = model(**inputs)
+
+# Generate output
+with torch.no_grad():
+    outputs = model(**inputs)
+
+# Print the generated text
+print(outputs)
 ```
 
-## Training and Evaluation
+---
+
+## 📊 Model Specifications
+
+| Attribute        | Description                                  |
+|-----------------|----------------------------------------------|
+| **Model Name**  | Spec-Vision-V1                               |
+| **Architecture** | Transformer-based Vision-Language Model    |
+| **Pretrained**  | ✅ Yes                                      |
+| **Dataset**     | Trained on diverse image-text pairs        |
+| **Framework**   | PyTorch & Hugging Face Transformers        |
+
+---
+
+## 🎯 Applications
+
+| Task                     | Description                                                   |
+|--------------------------|--------------------------------------------------------------|
+| **🖼️ Image Captioning**    | Generates detailed descriptions for input images.         |
+| **🧐 Visual Question Answering** | Answers questions about images.                  |
+| **🔎 Image-Text Matching**  | Determines the relevance of an image to a given text.  |
+| **🌍 Scene Understanding**  | Extracts insights from complex visual data.              |
+
+---
+
+## BLINK Benchmark
+
+A benchmark with 14 visual tasks that humans can solve very quickly but are still hard for current multimodal LLMs.
+
+| Benchmark                | Spec-Vision-V1 | LlaVA-Interleave-Qwen-7B | InternVL-2-4B | InternVL-2-8B | Gemini-1.5-Flash | GPT-4o-mini | Claude-3.5-Sonnet | Gemini-1.5-Pro | GPT-4o |
+|--------------------------|--------------|--------------------------|---------------|---------------|------------------|-------------|-------------------|----------------|--------|
+| Art Style               | 87.2         | 62.4                     | 55.6          | 52.1          | 64.1             | 70.1        | 59.8              | 70.9           | 73.3   |
+| Counting                | 54.2         | 56.7                     | 54.2          | 66.7          | 51.7             | 55.0        | 59.2              | 65.0           | 65.0   |
+| Forensic Detection      | 92.4         | 31.1                     | 40.9          | 34.1          | 54.5             | 38.6        | 67.4              | 60.6           | 75.8   |
+| Functional Correspondence | 29.2       | 34.6                     | 24.6          | 24.6          | 33.1             | 26.9        | 33.8              | 31.5           | 43.8   |
+| IQ Test                 | 25.3         | 26.7                     | 26.0          | 30.7          | 25.3             | 29.3        | 26.0              | 34.0           | 19.3   |
+| Jigsaw                  | 68.0         | 86.0                     | 55.3          | 52.7          | 71.3             | 72.7        | 57.3              | 68.0           | 67.3   |
+| Multi-View Reasoning    | 54.1         | 44.4                     | 48.9          | 42.9          | 48.9             | 48.1        | 55.6              | 49.6           | 46.6   |
+| Object Localization     | 49.2         | 54.9                     | 53.3          | 54.1          | 44.3             | 57.4        | 62.3              | 65.6           | 68.0   |
+| Relative Depth          | 69.4         | 77.4                     | 63.7          | 67.7          | 57.3             | 58.1        | 71.8              | 76.6           | 71.0   |
+| Relative Reflectance    | 37.3         | 34.3                     | 32.8          | 38.8          | 32.8             | 27.6        | 36.6              | 38.8           | 40.3   |
+| Semantic Correspondence | 36.7         | 31.7                     | 31.7          | 22.3          | 32.4             | 31.7        | 45.3              | 48.9           | 54.0   |
+| Spatial Relation       | 65.7         | 75.5                     | 78.3          | 78.3          | 55.9             | 81.1        | 60.1              | 79.0           | 84.6   |
+| Visual Correspondence  | 53.5         | 40.7                     | 34.9          | 33.1          | 29.7             | 52.9        | 72.1              | 81.4           | 86.0   |
+| Visual Similarity      | 83.0         | 91.9                     | 48.1          | 45.2          | 47.4             | 77.8        | 84.4              | 81.5           | 88.1   |
+| **Overall**            | **57.0**     | **53.1**                 | **45.9**      | **45.4**      | **45.8**         | **51.9**    | **56.5**          | **61.0**       | **63.2** |
+
+---
+
+## Video-MME Benchmark
+
+A benchmark that comprehensively assesses the capabilities of multimodal LLMs in processing video data, covering a wide range of visual domains, temporal durations, and data modalities.
+
+| Benchmark               | Spec-Vision-V1 | LlaVA-Interleave-Qwen-7B | InternVL-2-4B | InternVL-2-8B | Gemini-1.5-Flash | GPT-4o-mini | Claude-3.5-Sonnet | Gemini-1.5-Pro | GPT-4o |
+|-------------------------|--------------|--------------------------|---------------|---------------|------------------|-------------|-------------------|----------------|--------|
+| Short (<2min)          | 60.8         | 62.3                     | 60.7          | 61.7          | 72.2             | 70.1        | 66.3              | 73.3           | 77.7   |
+| Medium (4-15min)       | 47.7         | 47.1                     | 46.4          | 49.6          | 62.7             | 59.6        | 54.7              | 61.2           | 68.0   |
+| Long (30-60min)        | 43.8         | 41.2                     | 42.6          | 46.6          | 52.1             | 53.9        | 46.6              | 53.2           | 59.6   |
+| **Overall**            | **50.8**     | **50.2**                 | **49.9**      | **52.6**      | **62.3**         | **61.2**    | **55.9**          | **62.6**       | **68.4** |
+
+
+---
+
+## 🏗️ Model Training Details
+
+| Parameter             | Value                          |
+|----------------------|--------------------------------|
+| **Batch Size**      | 16                             |
+| **Optimizer**       | AdamW                          |
+| **Learning Rate**   | 5e-5                           |
+| **Training Steps**  | 100k                           |
+| **Loss Function**   | CrossEntropyLoss               |
+| **Framework**       | PyTorch & Transformers         |
+
+---
+
+## 📜 License
+
+**Spec-Vision-V1** is released under the **MIT**.
+
+---
+
+## 📖 Citation
+
+If you use **Spec-Vision-V1** in your research or application, please cite:
+
+```bibtex
+@article{SpecVision2025,
+  title={Spec-Vision-V1: A Vision-Language Transformer Model},
+  author={SVECTOR},
+  year={2025},
+  journal={SVECTOR Research}
+}
+```
+
+---
 
-[Add your training and evaluation details here]
+## 📬 Contact
 
-## Limitations and Biases
+For support or inquiries, reach out to **SVECTOR**:
 
-[Add any known limitations and biases here]
+- **🌐 Website**: [svector.co.in](https://www.svector.co.in)
+- **📧 Email**: [Research@svector.co.in](Research@svector.co.in)
+- **✨ GitHub**: [SVECTOR GitHub](https://github.com/SVECTOR-CORPORATION)