Update README.md

README.md

@@ -1,16 +1,99 @@
+---
 license: apache-2.0
 tags:
-  - object-detection
-  - vision
+- object-detection
+- vision
 datasets:
-  - coco
+- 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
+- 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}
+}
+```