adetailer
Maintainer: Bingsu
425
🔗
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
The adetailer model is a set of object detection models developed by Bingsu, a Hugging Face creator. The models are trained on various datasets, including face, hand, person, and deepfashion2 datasets, and can detect and segment these objects with high accuracy. The model offers several pre-trained variants, each specialized for a specific task, such as detecting 2D/realistic faces, hands, and persons with bounding boxes and segmentation masks.
The adetailer model is closely related to the YOLOv8 detection model and leverages the YOLO (You Only Look Once) framework. It provides a versatile solution for tasks involving the detection and segmentation of faces, hands, and persons in images.
Model inputs and outputs
Inputs
- Image data (either a file path, URL, or a PIL Image object)
Outputs
- Bounding boxes around detected objects (faces, hands, persons)
- Class labels for the detected objects
- Segmentation masks for the detected objects (in addition to bounding boxes)
Capabilities
The adetailer model is capable of detecting and segmenting faces, hands, and persons in images with high accuracy. It outperforms many existing object detection models in terms of mAP (mean Average Precision) on the specified datasets, as shown in the provided performance metrics.
The model's ability to provide both bounding boxes and segmentation masks for the detected objects makes it a powerful tool for applications that require precise object localization and segmentation, such as image editing, augmented reality, and computer vision tasks.
What can I use it for?
The adetailer model can be used in a variety of applications that involve the detection and segmentation of faces, hands, and persons in images. Some potential use cases include:
- Image editing and manipulation: The model's segmentation capabilities can be used to enable advanced image editing techniques, such as background removal, object swapping, and face/body editing.
- Augmented reality: The bounding box and segmentation outputs can be used to overlay virtual elements on top of real-world objects, enabling more realistic and immersive AR experiences.
- Computer vision and image analysis: The model's object detection and segmentation capabilities can be leveraged in various computer vision tasks, such as person tracking, gesture recognition, and clothing/fashion analysis.
- Facial analysis and recognition: The face detection and segmentation features can be used in facial analysis applications, such as emotion recognition, age estimation, and facial landmark detection.
Things to try
One interesting aspect of the adetailer model is its ability to handle a diverse range of object types, from realistic faces and hands to anime-style persons and clothing. This versatility allows you to experiment with different input images and see how the model performs across various visual styles and domains.
For example, you could try feeding the model images of anime characters, cartoon figures, or stylized illustrations to see how it handles the detection and segmentation of these more abstract object representations. Observing the model's performance on these challenging inputs can provide valuable insights into its generalization capabilities and potential areas for improvement.
Additionally, you could explore the model's segmentation outputs in more detail, examining the quality and accuracy of the provided masks for different object types. This information can be useful in determining the model's suitability for applications that require precise object isolation, such as image compositing or virtual try-on scenarios.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
❗
YOLOv8
59
YOLOv8 is a state-of-the-art (SOTA) object detection model developed by Ultralytics. It builds upon the success of previous YOLO versions, introducing new features and improvements to boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of computer vision tasks, including object detection, instance segmentation, image classification, and pose estimation. The model has been fine-tuned on diverse datasets and has demonstrated impressive capabilities across various domains. For example, the stockmarket-pattern-detection-yolov8 model is specifically tailored for detecting stock market patterns in live trading video data, while the stockmarket-future-prediction model focuses on predicting future stock market trends. Additionally, the yolos-tiny and yolos-small models demonstrate the versatility of the YOLOS architecture, which utilizes Vision Transformers (ViT) for object detection. Model inputs and outputs YOLOv8 is a versatile model that can accept a variety of input formats, including images, videos, and real-time video streams. The model's primary output is the detection of objects within the input, including their bounding boxes, class labels, and confidence scores. Inputs Images**: The model can process single images or batches of images. Videos**: The model can process video frames in real-time, enabling applications such as live object detection and tracking. Real-time video streams**: The model can integrate with live video feeds, enabling immediate object detection and analysis. Outputs Bounding boxes**: The model predicts the location of detected objects within the input using bounding box coordinates. Class labels**: The model classifies the detected objects and provides the corresponding class labels. Confidence scores**: The model outputs a confidence score for each detection, indicating the model's certainty about the prediction. Capabilities YOLOv8 is a versatile model that can be applied to a wide range of computer vision tasks. Its key capabilities include: Object detection**: The model can identify and locate multiple objects within an image or video frame, providing bounding box coordinates, class labels, and confidence scores. Instance segmentation**: In addition to object detection, YOLOv8 can also perform instance segmentation, which involves precisely outlining the boundaries of each detected object. Image classification**: The model can classify entire images into predefined categories, such as different types of animals or scenes. Pose estimation**: YOLOv8 can detect and estimate the poses of people or other subjects within an image or video, identifying the key joints and limbs. What can I use it for? YOLOv8 is a powerful tool that can be leveraged in a variety of real-world applications. Some potential use cases include: Retail and e-commerce**: The model can be used for automated product detection and inventory management in retail environments, as well as for recommendation systems based on customer browsing and purchasing behavior. Autonomous vehicles**: YOLOv8 can be integrated into self-driving car systems, enabling real-time object detection and collision avoidance. Surveillance and security**: The model can be used for intelligent video analytics, such as people counting, suspicious activity detection, and license plate recognition. Healthcare**: YOLOv8 can be applied to medical imaging tasks, such as identifying tumors or other abnormalities in X-rays or CT scans. Agriculture**: The model can be used for precision farming applications, such as detecting weeds, pests, or diseased crops in aerial or ground-based imagery. Things to try One interesting aspect of YOLOv8 is its ability to adapt to a wide range of domains and tasks beyond the traditional object detection use case. For example, the stockmarket-pattern-detection-yolov8 and stockmarket-future-prediction models demonstrate how the core YOLOv8 architecture can be fine-tuned to tackle specialized problems in the financial domain. Another area to explore is the use of different YOLOv8 model sizes, such as the yolos-tiny and yolos-small variants. These smaller models may be more suitable for deployment on resource-constrained devices or in real-time applications that require low latency. Ultimately, the versatility and performance of YOLOv8 make it an attractive choice for a wide range of computer vision projects, from edge computing to large-scale enterprise deployments.
Updated Invalid Date
🧪
holodayo-xl-2.1
57
holodayo-xl-2.1 is the latest version of the Yodayo Holodayo XL series, following the previous iteration, Holodayo XL 1.0. This open-source model is built upon Animagine XL V3, a specialized SDXL model designed for generating high-quality anime-style artwork. Holodayo XL 2.1 has undergone additional fine-tuning and optimization to focus specifically on generating images that accurately represent the visual style and aesthetics of the Virtual Youtuber franchise. Model inputs and outputs holodayo-xl-2.1 is a diffusion-based text-to-image generative model. It can take textual prompts as input and generate corresponding anime-style images as output. Inputs Textual prompts describing the desired image Outputs High-quality anime-style images that match the provided textual prompt Capabilities holodayo-xl-2.1 excels at generating anime-style artwork that captures the visual style and aesthetics of Virtual Youtubers. The model has been fine-tuned to address issues present in earlier versions, such as poor hand and anatomy rendering, and an overexposed art style. What can I use it for? holodayo-xl-2.1 can be used to create high-quality anime-style images for a variety of applications, such as virtual YouTuber avatars, anime-inspired artwork, and character designs. The model's focus on the Virtual Youtuber aesthetic makes it particularly useful for creators and businesses within that community. Things to try Experiment with different textual prompts to see the range of anime-style images holodayo-xl-2.1 can generate. Try prompts that incorporate specific Virtual Youtuber elements, such as character traits, themes, or settings, to see how the model handles those requests.
Updated Invalid Date
🚀
yolov8s
44
The yolov8s model, developed by the Ultralytics team, is a powerful object detection model that can recognize a wide range of objects, from common household items to animals and vehicles. It is part of the YOLOv8 family of models, which are known for their impressive accuracy and real-time performance. The yolov8s model is a smaller and more efficient variant of the YOLOv8 series, making it well-suited for deployments on resource-constrained devices. The YOLOv8 models, including yolov8s, build upon the success of previous YOLO versions and introduce new features and improvements to boost performance and flexibility. These models are designed to be fast, accurate, and easy to use, making them excellent choices for a wide range of object detection, instance segmentation, image classification, and pose estimation tasks. Model inputs and outputs Inputs Images**: The yolov8s model accepts image data as input, which can be provided in various formats, such as local image files or URLs. Outputs Detected objects**: The model's primary output is a set of detected objects within the input image, including their bounding boxes, class labels, and confidence scores. Visualization**: The model can also provide a visual representation of the detected objects, with bounding boxes and labels overlaid on the original image. Capabilities The yolov8s model is capable of detecting a diverse set of 80 object classes, including common everyday items, animals, vehicles, and more. It can accurately identify and localize these objects in real-time, making it a valuable tool for applications such as surveillance, autonomous vehicles, and smart home assistants. What can I use it for? The yolov8s model can be used in a variety of applications that require object detection capabilities. Some potential use cases include: Surveillance and security: The model can be integrated into surveillance systems to detect and track objects of interest, such as people, vehicles, or suspicious activities. Autonomous vehicles: The model can be used in self-driving cars or drones to detect and avoid obstacles, pedestrians, and other vehicles on the road. Retail and e-commerce: The model can be used to detect and count products on store shelves or in warehouses, enabling better inventory management and optimization. Smart home automation: The model can be used to detect and identify household objects, enabling smart home devices to provide more personalized and intelligent functionality. Things to try One interesting thing to try with the yolov8s model is to explore its performance on domain-specific datasets or custom datasets. By fine-tuning the model on specialized data, users can potentially improve its accuracy and reliability for their particular use case. Another idea is to experiment with the model's inference speed and resource requirements. By adjusting the model's parameters or using techniques like model quantization or distillation, users can optimize the model's performance for deployment on edge devices or resource-constrained environments. Overall, the yolov8s model offers a powerful and versatile object detection solution that can be tailored to a wide range of applications and environments.
Updated Invalid Date
🏋️
yolos-fashionpedia
71
The yolos-fashionpedia model is a fine-tuned object detection model for fashion. It was developed by Valentina Feve and is based on the YOLOS architecture. The model was trained on the Fashionpedia dataset, which contains over 50,000 annotated fashion product images across 80 different categories. Similar models include yolos-tiny, a smaller YOLOS model fine-tuned on COCO, and adetailer, a suite of YOLOv8 detection models for various visual tasks like face, hand, and clothing detection. Model Inputs and Outputs Inputs Image data: The yolos-fashionpedia model takes in image data as input, and is designed to detect and classify fashion products in those images. Outputs Object detection: The model outputs bounding boxes around detected fashion items, along with their predicted class labels from the 80 categories in the Fashionpedia dataset. These include items like shirts, pants, dresses, accessories, and fine-grained details like collars, sleeves, and patterns. Capabilities The yolos-fashionpedia model excels at accurately detecting and categorizing a wide range of fashion products within images. This can be particularly useful for applications like e-commerce, virtual try-on, and visual search, where precise product identification is crucial. What Can I Use It For? The yolos-fashionpedia model can be leveraged in a variety of fashion-related applications: E-commerce product tagging**: Automatically tag and categorize product images on e-commerce platforms to improve search, recommendation, and visual browsing experiences. Virtual try-on**: Integrate the model into virtual fitting room technologies to accurately detect garment types and sizes. Visual search**: Enable fashion-focused visual search engines by allowing users to query using images of products they're interested in. Fashion analytics**: Analyze fashion trends, inventory, and consumer preferences by processing large datasets of fashion images. Things to Try One interesting aspect of the yolos-fashionpedia model is its ability to detect fine-grained fashion details like collars, sleeves, and patterns. Developers could experiment with using this capability to enable more advanced fashion-related features, such as: Generating detailed product descriptions from images Recommending complementary fashion items based on detected garment attributes Analyzing runway shows or street style to identify emerging trends By leveraging the model's detailed understanding of fashion elements, researchers and practitioners can create novel applications that go beyond basic product detection.
Updated Invalid Date