hustvl/yolos-small
The YOLOS model, a Vision Transformer (ViT) trained using the DETR loss, achieves 42 AP on COCO validation 2017, similar to DETR and more complex frameworks like Faster R-CNN, despite its small size. The model uses a bipartite matching loss to compare predicted classes and bounding boxes to ground truth annotations. Fine-tuned on COCO 2017 object detection dataset consisting of 118k/5k annotated images for training/validation, it achieved 36.1 AP on validation set.
The YOLOS model, a Vision Transformer (ViT) trained using the DETR loss, achieves 42 AP on COCO validation 2017, similar to DETR and more complex frameworks like Faster R-CNN, despite its small size. The model uses a bipartite matching loss to compare predicted classes and bounding boxes to ground truth annotations. Fine-tuned on COCO 2017 object detection dataset consisting of 118k/5k annotated images for training/validation, it achieved 36.1 AP on validation set.
HTTP/cURL API
You can use cURL or any other http client to run inferences:
curl -X POST \
-H "Authorization: bearer $DEEPINFRA_TOKEN" \
-F image=@my_image.jpg \
'https://api.deepinfra.com/v1/inference/hustvl/yolos-small'
which will give you back something similar to:
{
"results": [
{
"score": 0.9939407110214233,
"label": "remote",
"box": {
"xmin": 46,
"ymin": 72,
"xmax": 181,
"ymax": 119
}
},
{
"score": 0.983637809753418,
"label": "cat",
"box": {
"xmin": 12,
"ymin": 54,
"xmax": 319,
"ymax": 470
}
}
],
"request_id": null,
"inference_status": {
"status": "unknown",
"runtime_ms": 0,
"cost": 0.0,
"tokens_generated": 0,
"tokens_input": 0
}
}
Input fields
webhookfile
The webhook to call when inference is done, by default you will get the output in the response of your inference request
