{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"florence-2","slug":"florence-2","name":"Florence-2","type":"model","url":"https://huggingface.co/microsoft/Florence-2-large","page_url":"https://unfragile.ai/florence-2","categories":["image-generation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"florence-2__cap_0","uri":"capability://image.visual.unified.sequence.to.sequence.vision.task.execution","name":"unified sequence-to-sequence vision task execution","description":"Florence-2 uses a single encoder-decoder transformer architecture trained on diverse vision tasks (captioning, detection, grounding, segmentation, OCR) to handle multiple vision problems without task-specific model switching. The model processes images through a visual encoder and generates structured text outputs via a language decoder, treating all vision tasks as sequence-to-sequence problems with task-specific prompt tokens that condition the decoder behavior.","intents":["I need one model that can handle image captioning, object detection, and OCR without managing multiple specialized models","I want to reduce inference latency by avoiding model switching overhead in multi-task vision pipelines","I need a foundation model that generalizes across vision tasks with a consistent interface"],"best_for":["teams building multi-task vision systems who want unified model management","developers prototyping vision applications with limited GPU memory","researchers studying transfer learning across diverse vision tasks"],"limitations":["Single model may have lower peak performance on individual tasks compared to specialized models optimized for one task","Inference speed depends on output sequence length; longer structured outputs (e.g., dense object lists) increase latency","Requires careful prompt engineering with task-specific tokens to achieve optimal performance per task"],"requires":["PyTorch 1.13+ or TensorFlow 2.10+","GPU with minimum 8GB VRAM for large variant (16GB+ recommended)","Hugging Face transformers library 4.30+","PIL/Pillow for image preprocessing"],"input_types":["image (PNG, JPEG, WebP, BMP)","text prompts with task-specific tokens","image + text pairs for grounding tasks"],"output_types":["text (captions, OCR text)","structured JSON (bounding boxes, segmentation masks)","coordinate-based outputs (grounding, detection)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"florence-2__cap_1","uri":"capability://image.visual.dense.object.detection.with.bounding.box.generation","name":"dense object detection with bounding box generation","description":"Florence-2 detects objects in images by generating bounding box coordinates in a structured text format through the decoder. The model encodes the image, uses a detection-specific prompt token, and outputs coordinates as normalized values (0-1000 scale) for each detected object with associated class labels, enabling end-to-end detection without post-processing NMS or anchor boxes.","intents":["I need to detect multiple objects in an image and get their coordinates in a single forward pass","I want object detection without managing anchor boxes, NMS thresholds, or task-specific hyperparameters","I need detection results in a structured format I can directly parse and use in downstream applications"],"best_for":["developers building inventory management or visual search systems","teams needing detection without YOLO/Faster R-CNN infrastructure complexity","applications requiring detection + other vision tasks in one model"],"limitations":["Detection accuracy on small objects (<5% image area) is lower than specialized detectors due to encoder compression","Coordinate precision is limited to 1000-scale normalization; sub-pixel accuracy requires post-processing","Performance degrades with >50 objects per image due to sequence length constraints in decoder"],"requires":["PyTorch 1.13+ or TensorFlow 2.10+","GPU with 8GB+ VRAM","transformers library 4.30+","image preprocessing (resize to 768x768 or 1024x1024)"],"input_types":["image (PNG, JPEG, WebP)","optional text prompt for class filtering"],"output_types":["structured text with coordinates: '<OD>object1<loc_0><loc_1><loc_2><loc_3>...'","parsed JSON with bounding boxes and class labels"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"florence-2__cap_10","uri":"capability://image.visual.efficient.inference.through.encoder.decoder.caching","name":"efficient inference through encoder-decoder caching","description":"Florence-2 optimizes inference latency through key-value caching in the decoder, where previously computed attention states are reused for subsequent token generation. The visual encoder output is computed once per image and cached, while the decoder generates output tokens sequentially with cached attention, reducing redundant computation and enabling faster inference for variable-length outputs.","intents":["I need to reduce inference latency for real-time vision applications","I want to optimize inference cost in high-throughput production systems","I need to understand how encoder-decoder caching improves performance"],"best_for":["teams building real-time vision APIs","developers optimizing inference cost in cloud environments","applications requiring low-latency vision processing"],"limitations":["Caching adds memory overhead; GPU memory usage increases with batch size and output sequence length","Cache invalidation is required when processing new images; no cross-image cache reuse","Caching benefits are most significant for long output sequences (>50 tokens); minimal improvement for short outputs"],"requires":["PyTorch 1.13+ or TensorFlow 2.10+","GPU with sufficient memory for cache storage","transformers library 4.30+ with caching support"],"input_types":["image (PNG, JPEG, WebP)"],"output_types":["faster inference with reduced latency"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"florence-2__cap_2","uri":"capability://image.visual.image.to.text.captioning.with.task.conditioned.generation","name":"image-to-text captioning with task-conditioned generation","description":"Florence-2 generates natural language descriptions of images using a caption-specific prompt token that conditions the decoder to produce fluent, contextually appropriate text. The visual encoder extracts image features, and the decoder generates captions token-by-token using standard language modeling, with beam search or greedy decoding available for output quality control.","intents":["I need to generate descriptive captions for images at scale without managing separate captioning models","I want captions that can be controlled for length and detail level through prompt engineering","I need to caption images as part of a multi-task vision pipeline"],"best_for":["content creators building image metadata systems","accessibility teams generating alt-text for web applications","developers integrating captioning into multi-modal search systems"],"limitations":["Generated captions may hallucinate objects or details not present in the image, especially for complex scenes","Caption length is difficult to control precisely; longer captions may exceed token budgets in downstream applications","Performance on domain-specific images (medical, scientific) is lower than general web images due to training data distribution"],"requires":["PyTorch 1.13+ or TensorFlow 2.10+","GPU with 6GB+ VRAM","transformers library 4.30+","image preprocessing (resize to 768x768 minimum)"],"input_types":["image (PNG, JPEG, WebP, BMP)","optional style/length prompt tokens"],"output_types":["text (natural language caption)","variable length (typically 10-50 tokens)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"florence-2__cap_3","uri":"capability://image.visual.visual.grounding.with.region.to.text.localization","name":"visual grounding with region-to-text localization","description":"Florence-2 grounds text phrases to image regions by generating bounding box coordinates for objects matching natural language descriptions. The model takes an image and text query (e.g., 'the red car'), encodes both through the visual and text encoders, and outputs normalized coordinates for matching regions, enabling phrase-to-region mapping without separate grounding models.","intents":["I need to find where in an image a specific object or phrase is located based on text description","I want to ground multiple phrases to different regions in a single image","I need visual grounding integrated with other vision tasks in one model"],"best_for":["developers building interactive image annotation tools","teams creating visual question answering systems","applications requiring text-to-region mapping for image understanding"],"limitations":["Grounding accuracy decreases for ambiguous phrases or when multiple objects match the description","Performance is limited to phrases seen during training; novel or highly specific descriptions may fail","Coordinate precision is limited to 1000-scale normalization, requiring post-processing for pixel-level accuracy"],"requires":["PyTorch 1.13+ or TensorFlow 2.10+","GPU with 8GB+ VRAM","transformers library 4.30+","text tokenizer compatible with Florence-2 (included in model)"],"input_types":["image (PNG, JPEG, WebP)","text phrase or description (natural language)"],"output_types":["bounding box coordinates (normalized 0-1000 scale)","structured text with phrase and coordinates"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"florence-2__cap_4","uri":"capability://image.visual.semantic.segmentation.mask.generation","name":"semantic segmentation mask generation","description":"Florence-2 generates semantic segmentation masks by outputting pixel-level class labels in a structured text format, where the decoder produces a sequence of coordinates and class IDs that can be reconstructed into full segmentation masks. The model uses a segmentation-specific prompt token and encodes spatial information through coordinate sequences rather than dense feature maps.","intents":["I need pixel-level segmentation of objects in an image without managing separate segmentation models","I want to segment multiple classes in a single forward pass","I need segmentation integrated with detection and captioning in one unified model"],"best_for":["teams building scene understanding systems","developers creating image editing or manipulation tools","applications requiring multi-task vision (detection + segmentation + captioning)"],"limitations":["Segmentation masks are generated at reduced resolution (typically 256x256 or 512x512) and require upsampling for full-resolution output","Accuracy on small objects or thin structures is lower than specialized segmentation models (Mask R-CNN, DeepLab)","Sequence-based representation limits mask complexity; highly fragmented or intricate masks may not be accurately represented"],"requires":["PyTorch 1.13+ or TensorFlow 2.10+","GPU with 12GB+ VRAM (larger than detection/captioning)","transformers library 4.30+","image preprocessing and mask reconstruction utilities"],"input_types":["image (PNG, JPEG, WebP)","optional class filter prompts"],"output_types":["structured text with coordinate sequences and class IDs","reconstructed segmentation masks (PNG, numpy array)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"florence-2__cap_5","uri":"capability://image.visual.optical.character.recognition.with.layout.preservation","name":"optical character recognition with layout preservation","description":"Florence-2 performs OCR by generating recognized text with spatial layout information, outputting character sequences along with bounding box coordinates for each text region. The model processes images through the visual encoder and generates text tokens with associated location metadata, enabling structured OCR without separate text detection and recognition stages.","intents":["I need to extract text from images while preserving spatial layout and reading order","I want OCR integrated with other vision tasks in a single model","I need structured OCR output with text and coordinates for document processing"],"best_for":["developers building document digitization systems","teams creating document understanding pipelines","applications requiring OCR + detection + captioning in one model"],"limitations":["OCR accuracy on low-resolution text (<20px height) or heavily stylized fonts is significantly lower than specialized OCR engines (Tesseract, PaddleOCR)","Handling of complex layouts (multi-column, rotated text) is limited; text order may not match visual reading order","Performance degrades with dense text regions (>500 characters per image) due to sequence length constraints"],"requires":["PyTorch 1.13+ or TensorFlow 2.10+","GPU with 8GB+ VRAM","transformers library 4.30+","image preprocessing (high-resolution input recommended, 1024x1024+)"],"input_types":["image (PNG, JPEG, WebP, BMP)","optional language or region hints"],"output_types":["structured text with coordinates: 'text<loc_x1><loc_y1><loc_x2><loc_y2>'","parsed JSON with text regions and bounding boxes"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"florence-2__cap_6","uri":"capability://image.visual.multi.task.prompt.conditioned.inference","name":"multi-task prompt-conditioned inference","description":"Florence-2 uses task-specific prompt tokens (e.g., '<OD>' for object detection, '<CAPTION>' for captioning) to condition the decoder behavior within a single model, allowing users to specify which vision task to perform through text prompts. The encoder processes the image identically for all tasks, but the decoder generates different output formats based on the prompt token, enabling task selection without model switching.","intents":["I need to switch between vision tasks (detection, captioning, OCR) without loading different models","I want to control model behavior through prompts rather than code changes or model selection","I need to build flexible vision pipelines that can adapt to different tasks dynamically"],"best_for":["developers building flexible vision APIs or microservices","teams with limited GPU memory who need multiple vision capabilities","researchers studying prompt-based task conditioning in vision models"],"limitations":["Prompt token design is model-specific; custom task tokens require retraining or fine-tuning","Task performance may be suboptimal if prompt tokens are not precisely matched to training tokens","No built-in mechanism for task-specific hyperparameter tuning (e.g., detection confidence thresholds) through prompts"],"requires":["PyTorch 1.13+ or TensorFlow 2.10+","GPU with 8GB+ VRAM","transformers library 4.30+","knowledge of Florence-2 task-specific prompt tokens"],"input_types":["image (PNG, JPEG, WebP)","task-specific prompt token (string)"],"output_types":["task-dependent: text (captioning), coordinates (detection), structured JSON (grounding)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"florence-2__cap_7","uri":"capability://image.visual.batch.inference.with.variable.image.sizes","name":"batch inference with variable image sizes","description":"Florence-2 supports batch processing of images with different resolutions through dynamic padding and attention masking in the encoder, allowing efficient batching without resizing all images to a common size. The model handles variable-length output sequences (e.g., different numbers of detected objects) through padding and sequence masking, enabling throughput optimization for production inference.","intents":["I need to process multiple images efficiently without resizing them to a fixed resolution","I want to maximize GPU utilization by batching images of different sizes","I need to build production inference pipelines with high throughput"],"best_for":["teams building high-throughput vision APIs","developers optimizing inference cost in cloud environments","applications processing diverse image sources (web, mobile, documents)"],"limitations":["Dynamic padding reduces GPU memory efficiency compared to fixed-size batching; memory usage scales with largest image in batch","Batch size is limited by the largest image resolution; mixing very large (4K) and small (480p) images reduces effective batch size","Attention masking adds ~5-10% latency overhead compared to fixed-size batching"],"requires":["PyTorch 1.13+ or TensorFlow 2.10+","GPU with 12GB+ VRAM for large batches","transformers library 4.30+","custom batching logic or dataloader supporting variable sizes"],"input_types":["batch of images with variable resolutions (PNG, JPEG, WebP)"],"output_types":["batch of task-specific outputs (variable length sequences)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"florence-2__cap_8","uri":"capability://image.visual.fine.tuning.on.custom.vision.tasks","name":"fine-tuning on custom vision tasks","description":"Florence-2 can be fine-tuned on custom datasets for domain-specific vision tasks by continuing training with task-specific prompt tokens and custom annotations. The model supports parameter-efficient fine-tuning through LoRA (Low-Rank Adaptation) or full fine-tuning, allowing adaptation to specialized domains (medical imaging, industrial inspection) without retraining from scratch.","intents":["I need to adapt Florence-2 to detect objects specific to my domain (e.g., defects in manufacturing)","I want to improve captioning quality for domain-specific images (e.g., medical reports)","I need to fine-tune the model efficiently with limited GPU resources"],"best_for":["teams with domain-specific vision datasets","developers building specialized vision applications","researchers studying transfer learning in multi-task vision models"],"limitations":["Fine-tuning requires carefully curated datasets with consistent annotation formats; poor data quality significantly impacts performance","LoRA fine-tuning adds inference latency (~5-10%) compared to full model inference","Catastrophic forgetting may occur if fine-tuning data is too different from pretraining distribution; careful regularization is needed"],"requires":["PyTorch 1.13+ or TensorFlow 2.10+","GPU with 16GB+ VRAM for full fine-tuning (8GB+ for LoRA)","transformers library 4.30+","custom dataset with annotations in Florence-2 format","training scripts or frameworks (Hugging Face Trainer, custom PyTorch loops)"],"input_types":["image dataset (PNG, JPEG, WebP)","annotations (bounding boxes, segmentation masks, captions, OCR text)"],"output_types":["fine-tuned model checkpoint","improved task-specific performance on custom data"],"categories":["image-visual","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"florence-2__cap_9","uri":"capability://image.visual.cross.task.knowledge.transfer.through.shared.representations","name":"cross-task knowledge transfer through shared representations","description":"Florence-2's unified architecture enables knowledge transfer across vision tasks through shared visual encoding and decoder parameters. Training on diverse tasks (detection, captioning, segmentation, OCR) simultaneously improves generalization by exposing the model to varied visual concepts and spatial reasoning patterns, resulting in better performance on each individual task compared to task-specific models trained in isolation.","intents":["I want a single model that performs well across multiple vision tasks without task-specific optimization","I need to understand how vision tasks benefit from multi-task learning","I want to leverage shared representations to improve performance on low-data tasks"],"best_for":["researchers studying multi-task learning in vision","teams building general-purpose vision systems","developers exploring knowledge transfer across vision domains"],"limitations":["Multi-task learning may reduce peak performance on individual tasks compared to specialized models; trade-off between generalization and specialization","Task interference can occur if tasks have conflicting learning signals; careful task weighting during training is required","Knowledge transfer benefits are task-dependent; 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