π StyleFinder β AI-Powered Fashion Visual Search
StyleFinder is a deep learning-based image retrieval system fine-tuned on the DeepFashion In-shop Clothes dataset using CLIP. It enables users to upload an image and retrieve visually similar fashion items using both zero-shot and fine-tuned CLIP variants.
π§ Supported Models
Model | Stage | Description |
---|---|---|
ViT-B/16 | Stage 3 v4 | Best fine-tuned transformer-based model |
RN50 | Stage 3 v3 | Best fine-tuned CNN-based model |
ViT-B/16 | Zero-shot | Official OpenAI pretrained CLIP |
RN50 | Zero-shot | Official OpenAI pretrained CLIP |
π Evaluation Results
Metric | ViT-B/16 (v4) | RN50 (v3) |
---|---|---|
Rank-1 | 46.24% | 53.95% |
mAP | 0.3481 | 0.4265 |
πΌοΈ Precomputed Gallery Features
Gallery embeddings are stored as .pt
files for fast cosine similarity search.
File Name | Description |
---|---|
vitb16_stage3_v4_gallery.pt |
Fine-tuned ViT-B/16 gallery |
rn50_stage3_v3_gallery.pt |
Fine-tuned RN50 gallery |
vitb16_zeroshot_gallery.pt |
Official CLIP ViT-B/16 gallery |
rn50_zeroshot_gallery.pt |
Official CLIP RN50 gallery |
These are stored in the gallery_features/
directory and can be loaded with load_gallery_features()
.
βοΈ How to Use
πΉ Load a Model
from model_loader import load_model
model, preprocess = load_model(arch="vitb16", stage="stage3") # or rn50 / zeroshot
Space using babupallam/stylefinder 1
Evaluation results
- Rank-1 Accuracy (RN50) on DeepFashion In-shop Clothes Retrievalself-reported53.950
- mAP (RN50) on DeepFashion In-shop Clothes Retrievalself-reported0.426
- Rank-1 Accuracy (ViT-B/16) on DeepFashion In-shop Clothes Retrievalself-reported46.240
- mAP (ViT-B/16) on DeepFashion In-shop Clothes Retrievalself-reported0.348