Feature Extraction
timm
Safetensors
trendyol_arcface
computer-vision
image-feature-extraction
arcface
product-similarity
e-commerce
image-embeddings
convnext
Instructions to use Trendyol/e-commerce-product-image-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use Trendyol/e-commerce-product-image-encoder with timm:
import timm model = timm.create_model("hf_hub:Trendyol/e-commerce-product-image-encoder", pretrained=True) - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "trendyol_arcface", | |
| "architectures": ["TYArcFaceModel"], | |
| "backbone_name": "convnext_base_in22ft1k", | |
| "backbone_features": 1024, | |
| "embedding_dim": 512, | |
| "hidden_size": 49, | |
| "use_arcface_loss": true, | |
| "arcface_scale": 128, | |
| "arcface_margin": 0.25, | |
| "dropout": 0.5, | |
| "input_size": 224, | |
| "normalization": { | |
| "mean": [0.485, 0.456, 0.406], | |
| "std": [0.229, 0.224, 0.225] | |
| }, | |
| "task_type": "feature-extraction", | |
| "training_info": { | |
| "epoch": 5, | |
| "global_step": 108, | |
| "pytorch_lightning_version": "1.8.1" | |
| } | |
| } | |