feat: test upload - Trendyol DinoV2 Product Similarity and Retrieval Embedding Model

🧪 Test Upload Details:
- Personal account testing before company publication
- Architecture: ConvNeXt-Base + ArcFace loss
- Embedding dimension: 256
- Task: Product similarity and retrieval

📁 Repository Contents:
- Model weights in safetensors format
- Complete model card with usage examples
- Apache 2.0 license
- Demo notebook for inference

🔒 Security: Scanned and validated
📋 RFC Compliance: Ready for company publication

Test upload by: Personal Account

README.md

@@ -88,28 +88,6 @@ The model uses a specific preprocessing pipeline that's crucial for good perform
 6. **ToTensor**: Convert to PyTorch tensor
 7. **Normalize**: ImageNet normalization (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
 
-### Using with AutoModel and AutoImageProcessor
-
-```python
-from transformers import AutoModel, AutoImageProcessor
-
-# Load from Hugging Face Hub
-model = AutoModel.from_pretrained("Trendyol/trendyol-dino-v2-ecommerce-256d")
-processor = AutoImageProcessor.from_pretrained("Trendyol/trendyol-dino-v2-ecommerce-256d")
-
-# Full inference pipeline
-import torch
-from PIL import Image
-
-image = Image.open('your_image.jpg')
-inputs = processor(images=image, return_tensors="pt")
-
-with torch.no_grad():
-    outputs = model(**inputs)
-    embeddings = outputs.last_hidden_state  # Shape: [1, 256]
-
-print("Embedding shape:", embeddings.shape)
-```
 
 ## Installation