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title: Dress Detection and Classification Pipeline | |
emoji: π | |
colorFrom: pink | |
colorTo: purple | |
sdk: gradio | |
sdk_version: 4.44.0 | |
app_file: app.py | |
pinned: false | |
license: mit | |
# Dress Detection and Classification Pipeline | |
This application performs end-to-end dress analysis using computer vision and deep learning: | |
## Features | |
1. **Human Detection**: Detects humans in uploaded images using Faster R-CNN | |
2. **Dress Segmentation**: Segments the dress area using a specialized U-Net model | |
3. **Classification**: Classifies the dress type using a ConvNeXt model | |
4. **Grad-CAM**: Shows attention areas for the classification decision with interpretability | |
## How to Use | |
1. Upload an image containing a person wearing a dress | |
2. The pipeline will automatically: | |
- Detect the person in the image | |
- Segment the dress area | |
- Classify the dress type | |
- Show what parts of the dress the model focused on for classification | |
## Technical Details | |
- **Detection Model**: Faster R-CNN with ResNet-50 backbone | |
- **Segmentation Model**: U-Net with ResNet-34 encoder | |
- **Classification Model**: ConvNeXt v2 Base | |
- **Interpretability**: Grad-CAM for attention visualization | |
## Note | |
This is an academic project demonstrating the integration of multiple computer vision models in a single pipeline. The models may require fine-tuning for optimal performance on specific datasets. | |