AMLC project page

Author: Loki-Silvres

_projects/8_project.md

@@ -1,81 +1,76 @@
 ---
-layout: page
-title: project 8
-description: an other project with a background image and giscus comments
-img: assets/img/9.jpg
-importance: 2
-category: work
-giscus_comments: true
+layout: page  
+title: Multimodal Price Regressor
+description: Smart product price prediction - Amazon ML Challenge 2025
+img: assets/img/AMLC2025-Final-drawio.png  
+importance: 1
+category: ml_research
 ---
 
-Every project has a beautiful feature showcase page.
-It's easy to include images in a flexible 3-column grid format.
-Make your photos 1/3, 2/3, or full width.
+### <a href="[https://github.com/RudrakshSJoshi/amlc-multimodal-mlp]">[Code]</a> <a href="https://www.kaggle.com/datasets/manav2805/amazon-ml-challenge-25/data">[Data]</a>
 
-To give your project a background in the portfolio page, just add the img tag to the front matter like so:
+As part of team **SPAM_LLMs**, we achieved **6th place** overall in the **Amazon ML Challenge 2025**, a national competition focused on smart product pricing. Our solution secured the **3rd position on the public leaderboard and 5th on the private leaderboard**.
 
-    ---
-    layout: page
-    title: project
-    description: a project with a background image
-    img: /assets/img/12.jpg
-    ---
+This project showcases a **multimodal learning** approach to predict product prices from text descriptions and images. By using large, pre-trained embedding models without fine-tuning, our architecture effectively processed complex, real-world e-commerce data to achieve high accuracy.
+
+---
 
 <div class="row">
-    <div class="col-sm mt-3 mt-md-0">
-        {% include figure.liquid loading="eager" path="assets/img/1.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
-    </div>
-    <div class="col-sm mt-3 mt-md-0">
-        {% include figure.liquid loading="eager" path="assets/img/3.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
-    </div>
-    <div class="col-sm mt-3 mt-md-0">
-        {% include figure.liquid loading="eager" path="assets/img/5.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
-    </div>
-</div>
-<div class="caption">
-    Caption photos easily. On the left, a road goes through a tunnel. Middle, leaves artistically fall in a hipster photoshoot. Right, in another hipster photoshoot, a lumberjack grasps a handful of pine needles.
-</div>
-<div class="row">
-    <div class="col-sm mt-3 mt-md-0">
-        {% include figure.liquid loading="eager" path="assets/img/5.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
-    </div>
+   <div class="col-sm mt-3 mt-md-0">
+       {% include figure.liquid path="assets/img/AMLC2025-Final-drawio.png" title="Solution Architecture" class="img-fluid rounded z-depth-1" %}
+   </div>
 </div>
 <div class="caption">
-    This image can also have a caption. It's like magic.
+   Our solution combined text and image modalities using separate networks before a final regression head.
 </div>
 
-You can also put regular text between your rows of images.
-Say you wanted to write a little bit about your project before you posted the rest of the images.
-You describe how you toiled, sweated, _bled_ for your project, and then... you reveal its glory in the next row of images.
-
-<div class="row justify-content-sm-center">
-    <div class="col-sm-8 mt-3 mt-md-0">
-        {% include figure.liquid path="assets/img/6.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
-    </div>
-    <div class="col-sm-4 mt-3 mt-md-0">
-        {% include figure.liquid path="assets/img/11.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
-    </div>
-</div>
-<div class="caption">
-    You can also have artistically styled 2/3 + 1/3 images, like these.
-</div>
+---
 
-The code is simple.
-Just wrap your images with `<div class="col-sm">` and place them inside `<div class="row">` (read more about the <a href="https://getbootstrap.com/docs/4.4/layout/grid/">Bootstrap Grid</a> system).
-To make images responsive, add `img-fluid` class to each; for rounded corners and shadows use `rounded` and `z-depth-1` classes.
-Here's the code for the last row of images above:
-
-{% raw %}
-
-```html
-<div class="row justify-content-sm-center">
-  <div class="col-sm-8 mt-3 mt-md-0">
-    {% include figure.liquid path="assets/img/6.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
-  </div>
-  <div class="col-sm-4 mt-3 mt-md-0">
-    {% include figure.liquid path="assets/img/11.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
-  </div>
-</div>
-```
+## Competition Highlights  
+
+### **Key Achievements**  
+- **Top National Ranking**: Finished 3rd (Public LB) and 5th (Private LB), placing us among the top 6 teams in a highly competitive national challenge.  
+- **Advanced Multimodal Architecture**: Designed and implemented a novel system that processes text and image data in parallel to predict prices.  
+- **Strategic Model Selection**: Demonstrated that large, frozen pre-trained models (**Qwen-3-4B**, **Siglip2**, **DinoV3**) can outperform fine-tuned smaller models, providing a crucial performance edge.  
+- **Effective Data Preprocessing**: Developed a comprehensive pipeline for cleaning and normalizing noisy catalog data, which was critical for model performance.  
+
+---
+
+## Methodology  
+
+### 1. **Business Problem**  
+In e-commerce, setting the optimal price for products is vital for success. The challenge was to create a machine learning model that analyzes product details from text and images to accurately predict its price, tackling complexities like brand value, specifications, and quantity.
+
+### 2. **Data Handling & Preprocessing**  
+- **Transformation**: Applied a **log1p transformation** to the right-skewed price data to normalize its distribution.  
+- **Text Cleaning**: Processed the `catalog_content` by stripping it into descriptions, bullet points, and quantity. We also standardized inconsistent units (e.g., "g," "gm" to "grams") and converted numbers to words to improve embedding quality.  
+- **Feature Selection**: To handle large text fields, we prioritized using the product description if available, otherwise defaulting to the top five bullet points.  
+
+### 3. **Multimodal Architecture**  
+- **Embedding Generation**: Utilized powerful, pre-trained models to generate high-quality embeddings for each modality:
+    - **Text**: Qwen-3-4B
+    - **Text + Image**: Siglip2 Giant
+    - **Image**: DinoV3
+- **Modality-Specific Networks**: Fed the embeddings from each modality into separate, dedicated neural networks. This allowed the model to learn modality-specific representations before fusing the information.
+- **Final Regressor**: Concatenated the outputs from the modality-specific networks and passed them to a final regression head to predict the log-transformed price.
+
+### 4. **Training and Evaluation**
+- **Loss Function**: Employed a **Log-based Mean Squared Error (MSE) Loss**, which is well-suited for log-transformed targets.
+- **Evaluation Metric**: The competition used the **Symmetric Mean Absolute Percentage Error (SMAPE)**, where a lower score is better.
+
+---
+
+## Insights & Conclusion  
+
+- **Power of Large Models**: Our success showed that using larger, state-of-the-art pre-trained models, even without fine-tuning, can provide superior feature representations compared to fine-tuning smaller, distilled models.
+- **Multimodality is Key**: Integrating image data provided a significant signal that purely text-based models might miss, confirming the value of a multimodal approach for complex real-world problems.  
+- **Architecture Matters**: Using modality-specific networks to process embeddings before concatenation was a key architectural decision that improved training stability and overall performance.
+
+---
 
-{% endraw %}
+### **Team Members**
+This was a collaborative effort by **SPAM_LLMs** (IIT ISM Dhanbad):
+- Manav Jain [[LinkedIn]](https://www.linkedin.com/in/manav-jain-05a711255/)
+- Karaka Prasanth Naidu [[LinkedIn]](https://www.linkedin.com/in/prasanth-naidu-karaka-a7162019b/)
+- Alok Raj [[LinkedIn]](https://www.linkedin.com/in/loki-silvres/)
+- Rudraksh Sachin Joshi [[LinkedIn]](https://www.linkedin.com/in/rudraksh-sachin-joshi-75554b202/)