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The model processes input images through a CNN feature extractor, applies spatial self-attention mechanisms to identify signature bounding boxes, and outputs normalized coordinates (x, y, width, height) for each detected signature. Fine-tuned on tech4humans/signature-detection dataset with conditional cross-attention to improve localization precision for variable document layouts and signature styles.","intents":["I need to automatically locate signature fields in scanned documents for automated document processing workflows","I want to extract signature regions from multi-page PDFs for verification or archival purposes","I need to identify where users should sign in document templates before sending them for signature"],"best_for":["document processing automation teams building invoice/contract workflows","fintech companies implementing digital signature capture pipelines","compliance teams automating document validation and signature verification"],"limitations":["Trained on specific signature-detection dataset — may have reduced accuracy on non-standard document layouts or handwritten signatures with unusual characteristics","ResNet-50 backbone limits real-time inference on edge devices — typical latency 200-500ms per image on CPU","No multi-language document support — optimized for Latin script signatures","Requires minimum image resolution of ~480x640 pixels for reliable detection; lower resolutions degrade accuracy"],"requires":["PyTorch 1.9+","transformers library 4.25+","Python 3.8+","GPU recommended for batch processing (CUDA 11.0+ or Metal for Apple Silicon)"],"input_types":["image (PNG, JPEG, BMP, TIFF)","tensor (torch.Tensor with shape [batch, 3, height, width])"],"output_types":["structured data (bounding boxes as [x_min, y_min, x_max, y_max] or [x_center, y_center, width, height])","confidence scores (float 0.0-1.0 per detection)","tensor (torch.Tensor with detection logits and bbox coordinates)"],"categories":["image-visual","object-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-tech4humans--conditional-detr-50-signature-detector__cap_1","uri":"capability://image.visual.batch.document.signature.detection.with.confidence.filtering","name":"batch document signature detection with confidence filtering","description":"Processes multiple document images in parallel batches through the Conditional DETR model with configurable confidence thresholds and non-maximum suppression (NMS) to filter overlapping detections. Implements batching logic that automatically pads variable-sized images to uniform dimensions, applies post-processing to remove low-confidence predictions, and returns deduplicated signature bounding boxes per document. Supports streaming inference for large document collections without loading entire batch into memory.","intents":["I need to process 1000+ scanned documents and extract all signature locations in a single batch job","I want to filter out false positive signature detections below a confidence threshold to reduce manual review overhead","I need to handle documents of different sizes and aspect ratios in a single inference pipeline"],"best_for":["document processing platforms handling high-volume batch jobs (100+ documents)","enterprise document management systems requiring automated signature field extraction","data annotation teams validating signature detection accuracy across document collections"],"limitations":["Batch processing requires uniform padding which adds ~5-15% computational overhead for mixed-size documents","NMS post-processing is CPU-bound — becomes bottleneck with >500 detections per batch","Memory usage scales linearly with batch size — typical batch of 32 images requires 8-12GB VRAM on GPU","No built-in handling for rotated or skewed documents — requires preprocessing with document deskewing"],"requires":["PyTorch 1.9+","transformers 4.25+","torchvision for image preprocessing utilities","GPU with minimum 6GB VRAM for batch_size=32 (CPU inference possible but 10-20x slower)"],"input_types":["image batch (list of PIL Images or numpy arrays)","tensor batch (torch.Tensor with shape [batch_size, 3, height, width])","file paths (list of strings pointing to image files)"],"output_types":["structured data (list of detection dictionaries per image with boxes, scores, labels)","tensor (torch.Tensor with shape [num_detections, 4] for coordinates and [num_detections] for scores)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-tech4humans--conditional-detr-50-signature-detector__cap_2","uri":"capability://image.visual.signature.region.extraction.and.cropping","name":"signature region extraction and cropping","description":"Extracts detected signature regions from source documents by converting bounding box coordinates to pixel-space crops and returning isolated signature images. Implements coordinate transformation from normalized model output to image pixel coordinates, applies optional padding/margin expansion around detected regions, and handles edge cases (signatures near image boundaries, overlapping detections). Supports multiple output formats (PIL Image, numpy array, base64-encoded) for downstream signature verification or storage.","intents":["I need to crop out signature images from documents for separate signature verification or comparison","I want to save extracted signatures as individual files for archival or audit purposes","I need to pass cropped signature regions to a signature verification model for authenticity checking"],"best_for":["signature verification pipelines that require isolated signature images as input","document archival systems extracting signature metadata for compliance records","signature comparison systems comparing extracted signatures against reference samples"],"limitations":["Cropping quality depends on detection accuracy — poor bounding boxes result in partial or over-cropped signatures","No automatic signature rotation correction — skewed signatures in crops may reduce downstream verification accuracy","Padding/margin expansion is fixed — no adaptive sizing based on signature dimensions","Loses document context information — extracted signatures cannot be re-associated with source documents without external tracking"],"requires":["PyTorch 1.9+","Pillow (PIL) 8.0+ for image manipulation","numpy for array operations","transformers 4.25+"],"input_types":["image (PIL Image or numpy array)","bounding boxes (list of [x_min, y_min, x_max, y_max] or [x_center, y_center, width, height])","detection output from signature-region localization capability"],"output_types":["image (PIL Image objects)","tensor (numpy arrays or torch.Tensor)","file (PNG/JPEG files saved to disk)","encoded (base64 strings for transmission)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-tech4humans--conditional-detr-50-signature-detector__cap_3","uri":"capability://image.visual.document.aware.signature.detection.with.layout.context","name":"document-aware signature detection with layout context","description":"Leverages Conditional DETR's spatial attention mechanisms to detect signatures while maintaining awareness of document layout structure (margins, text regions, form fields). The model's conditional cross-attention conditions detection queries on spatial features extracted from the full document image, enabling it to distinguish signatures from other similar-looking elements (initials, handwritten notes) based on positional context. Outputs signature detections with implicit layout-aware confidence scores that reflect document structure conformance.","intents":["I need to distinguish actual signature fields from handwritten notes or initials in documents based on their position","I want to detect signatures in structured forms where signature location is predictable but variable across document types","I need to reduce false positives from signature-like elements (underlines, marks) by using document layout context"],"best_for":["document processing systems handling diverse form types with consistent signature placement patterns","compliance systems requiring high-confidence signature detection to minimize manual review","form automation platforms that need to distinguish signature fields from other document elements"],"limitations":["Requires reasonably well-structured documents — performs poorly on unstructured handwritten documents or napkin notes","Layout context only helps if signatures appear in expected regions — detects signatures anywhere in document if trained data shows that pattern","No explicit document type classification — cannot adapt detection strategy based on document category (invoice vs contract)","Conditional attention adds ~50-100ms latency compared to standard DETR due to additional cross-attention computations"],"requires":["PyTorch 1.9+","transformers 4.25+","Well-formatted input images (not heavily skewed or rotated)"],"input_types":["image (full document image, PNG/JPEG/TIFF)","tensor (torch.Tensor with shape [1, 3, height, width] for single document)"],"output_types":["structured data (bounding boxes with layout-aware confidence scores)","tensor (detection logits reflecting spatial context)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-tech4humans--conditional-detr-50-signature-detector__cap_4","uri":"capability://code.generation.editing.fine.tuning.and.transfer.learning.for.custom.signature.detection","name":"fine-tuning and transfer learning for custom signature detection","description":"Provides a pre-trained Conditional DETR-ResNet-50 checkpoint that can be fine-tuned on custom signature detection datasets using standard PyTorch training loops. Supports transfer learning by freezing early ResNet-50 layers and training only the DETR decoder and detection head, enabling rapid adaptation to domain-specific signature styles (handwritten vs printed, different ink colors, document types). Includes safetensors model serialization for efficient checkpoint loading and sharing.","intents":["I need to adapt the signature detector to my company's specific document types and signature styles","I want to fine-tune the model on a smaller labeled dataset of our internal documents","I need to deploy a custom signature detector without training from scratch"],"best_for":["enterprises with proprietary document formats requiring custom signature detection","research teams experimenting with signature detection on specialized datasets","teams with limited labeled data (100-1000 examples) who can leverage transfer learning"],"limitations":["Fine-tuning requires labeled bounding box annotations — no weak supervision or semi-supervised learning support","Overfitting risk with small datasets (<500 examples) — requires careful regularization and data augmentation","Training infrastructure required — no built-in distributed training or multi-GPU support (requires manual setup with PyTorch DistributedDataParallel)","Safetensors format is read-only for inference — requires conversion back to PyTorch for modification"],"requires":["PyTorch 1.9+ with training utilities","transformers 4.25+","CUDA 11.0+ for GPU training (strongly recommended)","Labeled dataset with bounding box annotations in COCO or Pascal VOC format","8GB+ VRAM for batch_size=4 fine-tuning"],"input_types":["pre-trained checkpoint (safetensors or PyTorch .pt format)","training dataset (images + bounding box annotations in COCO JSON or VOC XML)"],"output_types":["fine-tuned checkpoint (safetensors or PyTorch format)","training metrics (loss curves, mAP scores)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-tech4humans--conditional-detr-50-signature-detector__cap_5","uri":"capability://data.processing.analysis.multi.format.document.input.handling.with.preprocessing","name":"multi-format document input handling with preprocessing","description":"Accepts document images in multiple formats (PNG, JPEG, BMP, TIFF) and automatically preprocesses them for model inference through normalization, resizing, and tensor conversion. Implements format detection, color space conversion (RGB/RGBA/grayscale to RGB), and dynamic resizing to model input dimensions while preserving aspect ratio through padding. Handles EXIF orientation metadata to correct rotated images before inference, and supports both single-image and batch processing pipelines.","intents":["I need to process documents in various formats (scanned PDFs as TIFF, digital documents as JPEG) without manual conversion","I want to handle documents with different resolutions and aspect ratios automatically","I need to correct image orientation issues (rotated scans) before signature detection"],"best_for":["document processing pipelines receiving documents from multiple sources (scanners, cameras, digital uploads)","production systems requiring robust input handling without manual preprocessing","teams processing legacy document archives in various formats"],"limitations":["Aspect ratio preservation through padding may add black borders that could affect detection near image edges","EXIF orientation correction only works for JPEG — TIFF and PNG require manual orientation handling","No automatic document deskewing — heavily rotated documents (>45 degrees) may require external preprocessing","Color space conversion assumes RGB output — may lose information from specialized color spaces (CMYK, indexed color)"],"requires":["Pillow (PIL) 8.0+ for image format handling","PyTorch 1.9+ for tensor conversion","transformers 4.25+ for preprocessing utilities","torchvision for additional image transformations"],"input_types":["image file (PNG, JPEG, BMP, TIFF)","image bytes (binary data from file upload or network stream)","PIL Image object","numpy array"],"output_types":["tensor (torch.Tensor with shape [1 or batch_size, 3, height, width])","normalized tensor (values in range [0, 1] or [-1, 1] depending on model normalization)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.9+","transformers library 4.25+","Python 3.8+","GPU recommended for batch processing (CUDA 11.0+ or Metal for Apple Silicon)","transformers 4.25+","torchvision for image preprocessing utilities","GPU with minimum 6GB VRAM for batch_size=32 (CPU inference possible but 10-20x slower)","Pillow (PIL) 8.0+ for image manipulation","numpy for array operations","Well-formatted input images (not heavily skewed or rotated)"],"failure_modes":["Trained on specific signature-detection dataset — may have reduced accuracy on non-standard document layouts or handwritten signatures with unusual characteristics","ResNet-50 backbone limits real-time inference on edge devices — typical latency 200-500ms per image on CPU","No multi-language document support — optimized for Latin script signatures","Requires minimum image resolution of ~480x640 pixels for reliable detection; lower resolutions degrade accuracy","Batch processing requires uniform padding which adds ~5-15% computational overhead for mixed-size documents","NMS post-processing is CPU-bound — becomes bottleneck with >500 detections per batch","Memory usage scales linearly with batch size — typical batch of 32 images requires 8-12GB VRAM on GPU","No built-in handling for rotated or skewed documents — requires preprocessing with document deskewing","Cropping quality depends on detection accuracy — poor bounding boxes result in partial or over-cropped signatures","No automatic signature rotation correction — skewed signatures in crops may reduce downstream verification accuracy","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.41019493439450844,"quality":0.37,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.766Z","last_scraped_at":"2026-04-22T08:08:25.099Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":36620,"model_likes":2}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=tech4humans--conditional-detr-50-signature-detector","compare_url":"https://unfragile.ai/compare?artifact=tech4humans--conditional-detr-50-signature-detector"}},"signature":"qZrezIetWH1bZUOwHEtq9JDS/MR0PExBuliSHdhLxNtN3ddBglWx6tbY+yBfipzvqJtXndF/IIwj5FdYUpJhBQ==","signedAt":"2026-06-20T04:55:49.216Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/tech4humans--conditional-detr-50-signature-detector","artifact":"https://unfragile.ai/tech4humans--conditional-detr-50-signature-detector","verify":"https://unfragile.ai/api/v1/verify?slug=tech4humans--conditional-detr-50-signature-detector","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}