Update README.md

README.md

@@ -1,36 +1,55 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+language:
+- en
+pipeline_tag: keypoint-detection
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6579e0eaa9e58aec614e9d97/ZuIwMdomy2_6aJ_JTE1Yd.png)
 
+<!-- Provide a quick summary of what the model is/does. -->
 
+ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation and ViTPose+: Vision Transformer Foundation Model for Generic Body Pose Estimation. It obtains 81.1 AP on MS COCO Keypoint test-dev set.
 
 ## Model Details
 
+Although no specific domain knowledge is considered in the design, plain vision transformers have shown excellent performance in visual recognition tasks. However, little effort has been made to reveal the potential of such simple structures for
+pose estimation tasks. In this paper, we show the surprisingly good capabilities of plain vision transformers for pose estimation from various aspects, namely simplicity in model structure, scalability in model size, flexibility in training paradigm,
+and transferability of knowledge between models, through a simple baseline model called ViTPose. Specifically, ViTPose employs plain and non-hierarchical vision
+transformers as backbones to extract features for a given person instance and a
+lightweight decoder for pose estimation. It can be scaled up from 100M to 1B
+parameters by taking the advantages of the scalable model capacity and high
+parallelism of transformers, setting a new Pareto front between throughput and performance. Besides, ViTPose is very flexible regarding the attention type, input resolution, pre-training and finetuning strategy, as well as dealing with multiple pose
+tasks. We also empirically demonstrate that the knowledge of large ViTPose models
+can be easily transferred to small ones via a simple knowledge token. Experimental
+results show that our basic ViTPose model outperforms representative methods
+on the challenging MS COCO Keypoint Detection benchmark, while the largest
+model sets a new state-of-the-art, i.e., 80.9 AP on the MS COCO test-dev set. The
+code and models are available at https://github.com/ViTAE-Transformer/ViTPose
+
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
 
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
+- **Developed by:** Sangbum Choi and Niels Rogge
+- **Funded by [optional]:** ARC FL-170100117 and IH-180100002.
 - **Shared by [optional]:** [More Information Needed]
 - **Model type:** [More Information Needed]
 - **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
+- **License:** apache-2.0
 - **Finetuned from model [optional]:** [More Information Needed]
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
+- **Repository:** https://github.com/ViTAE-Transformer/ViTPose
+- **Paper [optional]:** https://arxiv.org/pdf/2204.12484
 - **Demo [optional]:** [More Information Needed]
 
 ## Uses
@@ -59,7 +78,15 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
+In this paper, we propose a simple yet effective vision transformer baseline for pose estimation,
+i.e., ViTPose. Despite no elaborate designs in structure, ViTPose obtains SOTA performance
+on the MS COCO dataset. However, the potential of ViTPose is not fully explored with more
+advanced technologies, such as complex decoders or FPN structures, which may further improve the
+performance. Besides, although the ViTPose demonstrates exciting properties such as simplicity,
+scalability, flexibility, and transferability, more research efforts could be made, e.g., exploring the
+prompt-based tuning to demonstrate the flexibility of ViTPose further. In addition, we believe
+ViTPose can also be applied to other pose estimation datasets, e.g., animal pose estimation [47, 9, 45]
+and face keypoint detection [21, 6]. We leave them as the future work.
 
 ### Recommendations
 
@@ -71,8 +98,75 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+```
+import numpy as np
+import requests
+import torch
+from PIL import Image
+
+from transformers import (
+    RTDetrForObjectDetection,
+    RTDetrImageProcessor,
+    VitPoseConfig,
+    VitPoseForPoseEstimation,
+    VitPoseImageProcessor,
+)
+
+
+url = "http://images.cocodataset.org/val2017/000000000139.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+# Stage 1. Run Object Detector (User can replace this object_detector part)
+person_image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_r50vd_coco_o365")
+person_model = RTDetrForObjectDetection.from_pretrained("PekingU/rtdetr_r50vd_coco_o365")
+inputs = person_image_processor(images=image, return_tensors="pt")
+
+with torch.no_grad():
+    outputs = person_model(**inputs)
+
+results = person_image_processor.post_process_object_detection(
+    outputs, target_sizes=torch.tensor([(image.height, image.width)]), threshold=0.3
+)
+
+def pascal_voc_to_coco(bboxes: np.ndarray) -> np.ndarray:
+    """
+    Converts bounding boxes from the Pascal VOC format to the COCO format.
+
+    In other words, converts from (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format
+    to (top_left_x, top_left_y, width, height).
 
+    Args:
+        bboxes (`np.ndarray` of shape `(batch_size, 4)):
+            Bounding boxes in Pascal VOC format.
+
+    Returns:
+        `np.ndarray` of shape `(batch_size, 4) in COCO format.
+    """
+    bboxes[:, 2] = bboxes[:, 2] - bboxes[:, 0]
+    bboxes[:, 3] = bboxes[:, 3] - bboxes[:, 1]
+
+    return bboxes
+
+# Human label refers 0 index in COCO dataset
+boxes = results[0]["boxes"][results[0]["labels"] == 0]
+boxes = [pascal_voc_to_coco(boxes.cpu().numpy())]
+
+# Stage 2. Run ViTPose
+config = VitPoseConfig()
+image_processor = VitPoseImageProcessor.from_pretrained("nielsr/vitpose-base-simple")
+model = VitPoseForPoseEstimation.from_pretrained("nielsr/vitpose-base-simple")
+
+pixel_values = image_processor(image, boxes=boxes, return_tensors="pt").pixel_values
+
+with torch.no_grad():
+    outputs = model(pixel_values)
+
+pose_results = image_processor.post_process_pose_estimation(outputs, boxes=boxes)[0]
+
+for pose_result in pose_results:
+    print(pose_result)
+```
+    
 ## Training Details
 
 ### Training Data
@@ -92,13 +186,14 @@ Use the code below to get started with the model.
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- **Training regime:** ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6579e0eaa9e58aec614e9d97/Gj6gGcIGO3J5HD2MAB_4C.png)
+<!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
 #### Speeds, Sizes, Times [optional]
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-[More Information Needed]
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6579e0eaa9e58aec614e9d97/rsCmn48SAvhi8xwJhX8h5.png)
 
 ## Evaluation
 
@@ -126,7 +221,7 @@ Use the code below to get started with the model.
 
 ### Results
 
-[More Information Needed]
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6579e0eaa9e58aec614e9d97/FcHVFdUmCuT2m0wzB8QSS.png)
 
 #### Summary
 
@@ -162,7 +257,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 #### Hardware
 
-[More Information Needed]
+The models are trained on 8 A100 GPUs based on the mmpose codebase [11]
 
 #### Software
 
@@ -174,7 +269,15 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 **BibTeX:**
 
-[More Information Needed]
+@misc{xu2022vitposesimplevisiontransformer,
+      title={ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation}, 
+      author={Yufei Xu and Jing Zhang and Qiming Zhang and Dacheng Tao},
+      year={2022},
+      eprint={2204.12484},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2204.12484}, 
+}
 
 **APA:**