yolos-fashionpedia
Maintainer: valentinafeve
71
🏋️
| 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 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-fashionpediamodel 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.
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
🏷️
yolos-tiny
199
The yolos-tiny model is a lightweight object detection model based on the YOLOS architecture. It was fine-tuned on the COCO 2017 object detection dataset, which contains 118k annotated images. The yolos-tiny model is a Vision Transformer (ViT) trained using the DETR loss, which is a simple yet effective approach for object detection. Despite its simplicity, the base-sized YOLOS model can achieve 42 AP on the COCO validation set, on par with more complex frameworks like Faster R-CNN. The YOLOS model uses a "bipartite matching loss" to train the object detection heads. It compares the predicted classes and bounding boxes of each of the 100 object queries to the ground truth annotations, using the Hungarian matching algorithm to create an optimal one-to-one mapping. It then optimizes the model parameters using standard cross-entropy loss for the classes and a combination of L1 and generalized IoU loss for the bounding boxes. Compared to similar models like DETR and YOLO-world, the yolos-tiny model stands out for its small size and strong performance on the COCO dataset. Model inputs and outputs Inputs Images**: The model takes in individual images as input, which are expected to be processed and resized to a fixed size. Outputs Object Logits**: The model outputs class logits for each of the 100 object queries. Bounding Boxes**: The model outputs bounding box coordinates for each of the 100 object queries. Capabilities The yolos-tiny model can be used for real-time object detection in images. It is able to detect a wide variety of objects from the COCO dataset, including common household items, animals, and vehicles. The model's compact size makes it suitable for deployment on edge devices and mobile applications. What can I use it for? You can use the yolos-tiny model for a variety of object detection tasks, such as: Surveillance and security**: Detect and track objects of interest in real-time video feeds. Autonomous vehicles**: Identify and localize objects like pedestrians, cars, and traffic signals to enable safe navigation. Robotics and automation**: Integrate the model into robotic systems to enable interaction with and manipulation of objects in the environment. Retail and inventory management**: Monitor product stocks and detect misplaced items in stores and warehouses. See the model hub to explore other available YOLOS models that may fit your specific use case. Things to try One interesting aspect of the YOLOS architecture is its use of object queries to detect objects in the image. This approach is different from traditional object detection frameworks that rely on pre-defined anchor boxes or region proposals. By directly predicting the class and bounding box for each object query, the YOLOS model can potentially be more efficient and flexible in handling a variable number of objects in an image. You could experiment with the model's performance on different types of images, such as scenes with a large number of objects or images with significant occlusion or clutter. Evaluating the model's robustness and adaptability to diverse real-world scenarios would help understand its strengths and limitations. Additionally, you could investigate ways to further optimize the yolos-tiny model for deployment on resource-constrained devices, such as by exploring model quantization or distillation techniques.
Updated Invalid Date
↗️
yolos-small
51
The yolos-small model is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, the yolos-small model is able to achieve 42 AP on COCO validation 2017, which is similar to the performance of the DETR model and more complex frameworks like Faster R-CNN. The model is trained using a "bipartite matching loss" that compares the predicted classes and bounding boxes of 100 object queries to the ground truth annotations. This allows the model to detect objects in an image effectively. The yolos-small model is part of a family of YOLOS models, including the yolos-tiny model which has a smaller size. The yolos-fashionpedia model is a fine-tuned version of YOLOS for fashion object detection, trained on the Fashionpedia dataset. Another related model is the DETR model with a ResNet-101 backbone, which achieves a higher AP of 43.5 on COCO validation. Model inputs and outputs Inputs Images**: The model takes in RGB images as input. Outputs Object detection**: The model outputs the predicted bounding boxes and class labels for objects detected in the input image. Logits**: The model also outputs the class logits for the detected objects. Capabilities The yolos-small model is capable of detecting a wide range of common objects in images, including household items, animals, and people. It can locate the position of these objects with bounding boxes and classify them into 80 COCO categories. This makes it a versatile model for various computer vision tasks, such as object detection and image analysis. What can I use it for? You can use the yolos-small model for object detection in your computer vision applications. For example, you could build an app that can automatically identify and localize objects in images, which could be useful for tasks like inventory management, security monitoring, or even self-driving car development. Things to try One interesting thing to try with the yolos-small model is to explore its performance on different types of images, beyond the standard COCO dataset. You could try fine-tuning the model on a more specialized dataset, such as the Fashionpedia dataset used for the yolos-fashionpedia model, to see if it can improve its detection accuracy for fashion-related objects. Additionally, you could experiment with different inference techniques, such as adjusting the confidence threshold or using non-maximum suppression, to see how they impact the model's precision and recall. This could help you optimize the model's performance for your specific use case.
Updated Invalid Date
❗
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
🔗
adetailer
425
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.
Updated Invalid Date