Fantast/yolos-small-finetuned-for-seal

YOLOS (small-sized) model Finetuned For Seal Detection Task

YOLOS model based on hustvl/yolos-small and fine-tuned on Our Seal Image Dataset.

Model description

YOLOS is a Vision Transformer (ViT) trained using the DETR loss.

How to use

Here is how to use this model:

from transformers import YolosFeatureExtractor, YolosForObjectDetection
from PIL import Image
import requests

image = Image.open("xxxxxxxxxxxxx")

feature_extractor = YolosFeatureExtractor.from_pretrained('fantast/yolos-small-finetuned-for-seal')
model = YolosForObjectDetection.from_pretrained('fantast/yolos-small-finetuned-for-seal')

inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)

model predicts bounding boxes

logits = outputs.logits
bboxes = outputs.pred_boxes

Currently, both the feature extractor and model support PyTorch.

Training data

The YOLOS model based on hustvl/yolos-small and fine-tuned on Our Own Seal Image Dataset, a dataset consisting of 118k/5k annotated images for training/validation respectively.

BibTeX entry and citation info

@article{DBLP:journals/corr/abs-2106-00666,
  author    = {Yuxin Fang and
               Bencheng Liao and
               Xinggang Wang and
               Jiemin Fang and
               Jiyang Qi and
               Rui Wu and
               Jianwei Niu and
               Wenyu Liu},
  title     = {You Only Look at One Sequence: Rethinking Transformer in Vision through
               Object Detection},
  journal   = {CoRR},
  volume    = {abs/2106.00666},
  year      = {2021},
  url       = {https://arxiv.org/abs/2106.00666},
  eprinttype = {arXiv},
  eprint    = {2106.00666},
  timestamp = {Fri, 29 Apr 2022 19:49:16 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2106-00666.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

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license: mit