yolos-small
Maintainer: hustvl
51
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| 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 |
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Model overview
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.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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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.
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yolos-fashionpedia
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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.
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The detr-resnet-101 model is a DEtection TRansformer (DETR) model with a ResNet-101 backbone, trained end-to-end on the COCO 2017 object detection dataset. DETR is an encoder-decoder transformer that uses object queries to detect objects in an image. The model compares the predicted classes and bounding boxes of each object query to the ground truth annotations, using a bipartite matching loss to optimize the model parameters. This DETR model with a ResNet-50 backbone is a similar model that achieves slightly lower performance. The YOLOS (tiny-sized) model is another transformer-based object detection model that uses a simpler approach. Model inputs and outputs Inputs Images**: The model takes in images as input and processes them to detect objects within the image. Outputs Object detection**: The model outputs a set of detected objects, including the class label and bounding box coordinates for each detected object. Capabilities The detr-resnet-101 model is capable of detecting a wide range of objects in images, with high accuracy. It was trained on the diverse COCO dataset, which contains 80 different object categories. The model can handle complex scenes with multiple overlapping objects, and is able to localize the objects precisely using the predicted bounding boxes. What can I use it for? You can use the detr-resnet-101 model for a variety of object detection tasks, such as building smart surveillance systems, automating inventory management, or enhancing image analysis in various industries. The model's strong performance on the COCO benchmark suggests it can be a powerful tool for real-world object detection applications. You can find the model on the Hugging Face Model Hub and use it directly in your projects. Things to try One interesting aspect of the DETR model is its use of object queries to detect objects. Each object query looks for a particular object in the image, and the model learns to match these queries to the ground truth annotations during training. You could experiment with adjusting the number of object queries or the way they are initialized to see how it affects the model's performance on your specific use case. Additionally, you could try fine-tuning the model on a dataset more tailored to your application domain to further improve its accuracy.
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The detr-resnet-50 model is an End-to-End Object Detection (DETR) model with a ResNet-50 backbone. It was developed by the Facebook research team and introduced in the paper End-to-End Object Detection with Transformers. The model is trained end-to-end on the COCO 2017 object detection dataset, which contains 118k annotated images. The DETR model uses a transformer encoder-decoder architecture with a convolutional backbone to perform object detection. It takes an image as input and outputs a set of detected objects, including their class labels and bounding box coordinates. The model uses "object queries" to detect objects, where each query looks for a particular object in the image. For COCO, the number of object queries is set to 100. Similar models include the detr-resnet-50-panoptic model, which is trained for panoptic segmentation, and the detr-resnet-101 model, which uses a larger ResNet-101 backbone. Model inputs and outputs Inputs Images**: The model takes in an image as input, which is resized and normalized before being processed. Outputs Object detections**: The model outputs a set of detected objects, including their class labels and bounding box coordinates. Capabilities The detr-resnet-50 model can be used for object detection in images. It is able to identify and localize a variety of common objects, such as people, vehicles, animals, and household items. The model achieves strong performance on the COCO 2017 dataset, with an average precision (AP) of 38.8. What can I use it for? You can use the detr-resnet-50 model for a variety of computer vision applications that involve object detection, such as: Autonomous vehicles**: Detect and track objects like pedestrians, other vehicles, and obstacles to aid in navigation and collision avoidance. Surveillance and security**: Identify and localize people, vehicles, and other objects of interest in security camera footage. Retail and logistics**: Detect and count items in warehouses or on store shelves to improve inventory management. Robotics**: Enable robots to perceive and interact with objects in their environment. Things to try One interesting aspect of the DETR model is its use of "object queries" to detect objects. You could experiment with varying the number of object queries or using different types of object queries to see how it affects the model's performance and capabilities. Additionally, you could try fine-tuning the model on a specific domain or dataset to see if it can achieve even better results for your particular use case.
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