enginex-mlu370-any2any/transformers/docs/source/en/model_doc/yolos.md

This model was released on 2021-06-01 and added to Hugging Face Transformers on 2022-05-02.

YOLOS

YOLOS uses a Vision Transformer (ViT) for object detection with minimal modifications and region priors. It can achieve performance comparable to specialized object detection models and frameworks with knowledge about 2D spatial structures.

You can find all the original YOLOS checkpoints under the HUST Vision Lab organization.

YOLOS architecture. Taken from the original paper.

Tip

This model wasa contributed by nielsr. Click on the YOLOS models in the right sidebar for more examples of how to apply YOLOS to different object detection tasks.

The example below demonstrates how to detect objects with [Pipeline] or the [AutoModel] class.

import torch
from transformers import pipeline

detector = pipeline(
    task="object-detection",
    model="hustvl/yolos-base",
    dtype=torch.float16,
    device=0
)
detector("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png")

import torch
from PIL import Image
import requests
from transformers import AutoImageProcessor, AutoModelForObjectDetection, infer_device

device = infer_device()

processor = AutoImageProcessor.from_pretrained("hustvl/yolos-base")
model = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-base", dtype=torch.float16, attn_implementation="sdpa").to(device)

url = "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
inputs = processor(images=image, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model(**inputs)
logits = outputs.logits.softmax(-1)
scores, labels = logits[..., :-1].max(-1)
boxes = outputs.pred_boxes

threshold = 0.3
keep = scores[0] > threshold

filtered_scores = scores[0][keep]
filtered_labels = labels[0][keep]
filtered_boxes  = boxes[0][keep]

width, height = image.size
pixel_boxes = filtered_boxes * torch.tensor([width, height, width, height], device=boxes.device)

for score, label, box in zip(filtered_scores, filtered_labels, pixel_boxes):
    x0, y0, x1, y1 = box.tolist()
    print(f"Label {model.config.id2label[label.item()]}: {score:.2f} at [{x0:.0f}, {y0:.0f}, {x1:.0f}, {y1:.0f}]")

Notes

• Use [YolosImageProcessor] for preparing images (and optional targets) for the model. Contrary to DETR, YOLOS doesn't require a pixel_mask.

Resources

• Refer to these notebooks for inference and fine-tuning with [YolosForObjectDetection] on a custom dataset.

YolosConfig

autodoc YolosConfig

YolosImageProcessor

autodoc YolosImageProcessor - preprocess

YolosImageProcessorFast

autodoc YolosImageProcessorFast - preprocess - pad - post_process_object_detection

YolosFeatureExtractor

autodoc YolosFeatureExtractor - call - pad - post_process_object_detection

YolosModel

autodoc YolosModel - forward

YolosForObjectDetection

autodoc YolosForObjectDetection - forward