yolos-tiny
Maintainer: hustvl
199
🏷️
| 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-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.
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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