Delete README.md

README.md

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----
-license: apache-2.0
-tags:
-- object-detection
-- vision
-datasets:
-- coco
-widget:
-- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg
-  example_title: Savanna
-- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
-  example_title: Football Match
-- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg
-  example_title: Airport
----
-
-# YOLOS (tiny-sized) model
-
-YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [this repository](https://github.com/hustvl/YOLOS). 
-
-Disclaimer: The team releasing YOLOS did not write a model card for this model so this model card has been written by the Hugging Face team.
-
-## Model description
-
-YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN).
-
-The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.
-
-## Intended uses & limitations
-
-You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=hustvl/yolos) to look for all available YOLOS models.
-
-### How to use
-
-Here is how to use this model:
-
-```python
-from transformers import YolosImageProcessor, YolosForObjectDetection
-from PIL import Image
-import torch
-import requests
-
-url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-image = Image.open(requests.get(url, stream=True).raw)
-
-model = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny')
-image_processor = YolosImageProcessor.from_pretrained("hustvl/yolos-tiny")
-
-inputs = image_processor(images=image, return_tensors="pt")
-outputs = model(**inputs)
-
-# model predicts bounding boxes and corresponding COCO classes
-logits = outputs.logits
-bboxes = outputs.pred_boxes
-
-
-# print results
-target_sizes = torch.tensor([image.size[::-1]])
-results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0]
-for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
-    box = [round(i, 2) for i in box.tolist()]
-    print(
-        f"Detected {model.config.id2label[label.item()]} with confidence "
-        f"{round(score.item(), 3)} at location {box}"
-    )
-```
-
-Currently, both the feature extractor and model support PyTorch. 
-
-## Training data
-
-The YOLOS model was pre-trained on [ImageNet-1k](https://huggingface.co/datasets/imagenet2012) and fine-tuned on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively. 
-
-### Training
-
-The model was pre-trained for 300 epochs on ImageNet-1k and fine-tuned for 300 epochs on COCO.
-
-## Evaluation results
-
-This model achieves an AP (average precision) of **28.7** on COCO 2017 validation. For more details regarding evaluation results, we refer to the original paper.
-
-### BibTeX entry and citation info
-
-```bibtex
-@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}
-}
-```