Dfine X Obj Detection

--- language: - vi license: apache-2.0 library_name: d-fine pipeline_tag: object-detection tags: - d-fine - object-detection - document-layout - fine-tuned - hgnetv2 --- <div align="center"> # 🔍 D-FINE X — Object Detection for Document Layout **D-FINE HGNetv2-X fine-tuned for document layout analysis** [](https://github.com/Peterande/D-FINE/blob/master/LICENSE) []() []() []() </div> --- ## Overview Mô hình **D-FINE HGNetv2-X** được fine-tune cho bài toán nhận diện bố cục tài liệu (Document Layout Analysis). Mô hình sử dụng kiến trúc D-FINE tiên tiến với cơ chế Fine-grained Distribution Refinement (FDR) để tinh chỉnh bounding box, kết hợp backbone HGNetv2-B5 mạnh mẽ. | Property | Value | |---|---| | Base model | D-FINE (HGNetv2-X) | | Task | Object Detection | | Domain | Document Layout | | Input resolution | 640 × 640 | | Training | 80 epochs (72 Stage 1 + 8 Stage 2) | --- ## Classes | ID | Label | Description | |:---:|---|---| | 0 | `Object-detection` | Generic detectable region | | 1 | `Figure` | Diagrams, illustrations, charts | | 2 | `Icon` | Small symbolic graphics | | 3 | `Table` | Tabular data structures | --- ## Benchmark ### ✅ Best validation metrics (Stage 2) | Metric | Value | |---|---:| | mAP @ \[0.50 : 0.95\] | **0.8375** | | mAP @ 0.50 | **0.9182** | | mAP @ 0.75 | **0.8966** | | AP (Medium) | **0.8703** | | AP (Large) | **0.8384** | | AR @ 100 | **0.9348** | --- ## Usage ### Option 1 — HuggingFace Transformers (recommended) ```bash pip install transformers torch torchvision # Clone D-FINE source code (bắt buộc) git clone https://github.com/Peterande/D-FINE.git cd D-FINE pip install -r requirements.txt ``` ```python from transformers import AutoModel, AutoImageProcessor from PIL import Image processor = AutoImageProcessor.from_pretrained( "ducnhan0804/dfine-x-obj-detection", trust_remote_code=True ) model = AutoModel.from_pretrained( "ducnhan0804/dfine-x-obj-detection", trust_remote_code=True ) image = Image.open("path/to/your/document.png") inputs = processor.preprocess(image) outputs = model(inputs["images"], threshold=0.4) results = processor.post_process_obje...

Source context: 3 downloads · 0 likes · Pipeline object-detection · Library d-fine · Repo ducnhan0804/dfine-x-obj-detection