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Converts patch-level predictions to image-space coordinates through learned regression heads that output box centers, widths, and heights, enabling sub-patch-level localization precision through continuous coordinate regression.","intents":["I need precise bounding box coordinates for detected objects in normalized format","I want to convert model predictions to pixel coordinates for visualization or downstream processing","I need to handle variable image sizes without retraining the model"],"best_for":["Developers building detection pipelines that require normalized coordinates for scale-invariant processing","Teams working with variable-resolution image streams","Applications needing sub-pixel localization accuracy"],"limitations":["Normalized coordinates require multiplication by image dimensions to convert to pixel space; floating-point precision may cause rounding errors","Bounding box regression is patch-aligned, so minimum detectable object size is constrained by patch size (16×16 pixels)","Regression loss may produce boxes slightly outside image boundaries (0-1 range); requires clipping in post-processing","No rotation or polygon-based masks; only axis-aligned rectangular boxes"],"requires":["Original image dimensions (height, width) for coordinate denormalization","Post-processing logic to clip boxes to image boundaries"],"input_types":["raw model regression outputs (torch.Tensor shape [batch, num_patches, 4])"],"output_types":["normalized bounding boxes (float [0-1] range)","pixel-space bounding boxes (integer [0-width/height] range after denormalization)","box format options: [x_min, y_min, x_max, y_max] or [center_x, center_y, width, height]"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-hustvl--yolos-small__cap_3","uri":"capability://image.visual.multi.scale.inference.through.image.resizing.and.aspect.ratio.preservation","name":"multi-scale inference through image resizing and aspect ratio preservation","description":"Accepts images of arbitrary dimensions and internally resizes them to a standard input size (typically 512×512 or 768×768) while preserving aspect ratio through letterboxing or padding. Applies the same preprocessing pipeline (normalization, augmentation) consistently across all inputs, enabling batch processing of heterogeneous image sizes without model retraining.","intents":["I need to process images of different sizes without resizing them manually","I want to maintain aspect ratio to avoid distorting objects during preprocessing","I need to batch process images with different dimensions efficiently"],"best_for":["Developers building production detection pipelines with variable-resolution inputs","Teams processing real-world image streams from multiple sources","Applications requiring minimal preprocessing overhead"],"limitations":["Letterboxing adds padding that increases computation; larger padded regions reduce effective resolution","Aspect ratio preservation may result in unused model capacity if images are very wide or tall","Preprocessing adds ~50-100ms latency per image depending on resize method and image size","Padding introduces artificial background that may confuse detection near image edges"],"requires":["torchvision.transforms or PIL for image resizing","Normalization parameters (ImageNet mean/std: [0.485, 0.456, 0.406] / [0.229, 0.224, 0.225])"],"input_types":["image (JPEG, PNG, WebP with arbitrary dimensions)","PIL Image objects","numpy arrays (H×W×3 or H×W×4)"],"output_types":["preprocessed tensor (torch.Tensor shape [1, 3, 512, 512] or [1, 3, 768, 768])","metadata (original dimensions, padding offsets for coordinate transformation)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-hustvl--yolos-small__cap_4","uri":"capability://image.visual.batch.inference.with.dynamic.batching.and.memory.efficient.processing","name":"batch inference with dynamic batching and memory-efficient processing","description":"Processes multiple images simultaneously through the transformer encoder, leveraging GPU parallelization to amortize attention computation across batch elements. Implements dynamic batching that adjusts batch size based on available GPU memory, enabling efficient processing of large image collections without out-of-memory errors or manual batch size tuning.","intents":["I need to process hundreds of images efficiently without manual batch size management","I want to maximize GPU utilization for faster throughput","I need to handle variable batch sizes without code changes"],"best_for":["Teams processing large image datasets or video streams","Developers building scalable detection services","Applications with variable throughput requirements"],"limitations":["Batch processing adds latency for small batches (1-2 images) due to GPU overhead; single-image inference may be slower than optimized CPU implementations","Memory usage scales linearly with batch size; large batches (>32) may exceed GPU VRAM on consumer hardware","Dynamic batching requires profiling to determine optimal batch size; no automatic tuning across hardware","Batch processing introduces variable latency; real-time applications may require fixed batch sizes"],"requires":["GPU with minimum 2GB VRAM for batch size 1","CUDA 11.0+ for GPU acceleration","PyTorch with CUDA support"],"input_types":["batch of images (torch.Tensor shape [batch_size, 3, height, width])","list of PIL Images or numpy arrays"],"output_types":["batch of detections (list of detection results per image)","throughput metrics (images/second)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-hustvl--yolos-small__cap_5","uri":"capability://image.visual.non.maximum.suppression.with.iou.based.duplicate.removal","name":"non-maximum suppression with iou-based duplicate removal","description":"Removes duplicate or overlapping detections using Intersection-over-Union (IoU) thresholding, keeping only the highest-confidence detection for each object. 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Automatically handles model downloading, caching, device placement, and preprocessing through a high-level API that abstracts away implementation details.","intents":["I want to use object detection with minimal code and no model management","I need to quickly prototype detection in a Jupyter notebook or script","I want automatic model caching and device placement without manual configuration"],"best_for":["Researchers and data scientists prototyping detection models","Developers building quick demos or MVPs","Teams with minimal ML infrastructure experience"],"limitations":["Pipeline API abstracts away model details, making advanced tuning difficult","Automatic device placement may not be optimal for mixed CPU/GPU setups","Model caching uses disk space; large models (>1GB) may require significant storage","Pipeline API adds ~50-100ms overhead per inference compared to direct model calls"],"requires":["transformers library 4.5.0+","PyTorch 1.9+","Internet connection for first-time model download","Disk space for model caching (~350MB for yolos-small)"],"input_types":["image file path (string)","PIL Image object","numpy array","URL to image"],"output_types":["list of detection dicts with 'box', 'score', 'label' keys","human-readable output format"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-hustvl--yolos-small__cap_8","uri":"capability://tool.use.integration.pytorch.model.export.with.safetensors.format.support.for.secure.model.distribution","name":"pytorch model export with safetensors format support for secure model distribution","description":"Stores model weights in SafeTensors format (a secure, efficient serialization format) instead of pickle, enabling safe model loading without arbitrary code execution risks. 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