detr-resnet-101
Maintainer: facebook
86
🏋️
| 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 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.
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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