Update README.md

README.md

@@ -1,77 +1,174 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+pipeline_tag: keypoint-detection
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# Model Card for VitPose
 
+<img src="https://cdn-uploads.huggingface.co/production/uploads/6579e0eaa9e58aec614e9d97/ZuIwMdomy2_6aJ_JTE1Yd.png" alt="x" width="400"/>
 
+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
 
-### Model Description
+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
 
-<!-- Provide a longer summary of what this model is. -->
+### Model Description
 
 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]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
+- **Developed by:** Yufei Xu, Jing Zhang, Qiming Zhang, Dacheng Tao
+- **Funded by:** ARC FL-170100117 and IH-180100002.
+- **License:** Apache-2.0
+- **Ported to 🤗 Transformers by:** Sangbum Choi and Niels Rogge
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Original repository:** https://github.com/ViTAE-Transformer/ViTPose
+- **Paper:** https://arxiv.org/pdf/2204.12484
+- **Demo:** https://huggingface.co/spaces?sort=trending&search=vitpose
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+The ViTPose model, developed by the ViTAE-Transformer team, is primarily designed for pose estimation tasks. Here are some direct uses of the model:
 
-### Downstream Use [optional]
+Human Pose Estimation: The model can be used to estimate the poses of humans in images or videos. This involves identifying the locations of key body joints such as the head, shoulders, elbows, wrists, hips, knees, and ankles.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+Action Recognition: By analyzing the poses over time, the model can help in recognizing various human actions and activities.
 
-[More Information Needed]
+Surveillance: In security and surveillance applications, ViTPose can be used to monitor and analyze human behavior in public spaces or private premises.
 
-### Out-of-Scope Use
+Health and Fitness: The model can be utilized in fitness apps to track and analyze exercise poses, providing feedback on form and technique.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+Gaming and Animation: ViTPose can be integrated into gaming and animation systems to create more realistic character movements and interactions.
 
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+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.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+```python
+import torch
+import requests
+import numpy as np
+
+from PIL import Image
+
+from transformers import (
+    AutoProcessor,
+    RTDetrForObjectDetection,
+    VitPoseForPoseEstimation,
+)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+url = "http://images.cocodataset.org/val2017/000000000139.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+# ------------------------------------------------------------------------
+# Stage 1. Detect humans on the image
+# ------------------------------------------------------------------------
+
+# You can choose detector by your choice
+person_image_processor = AutoProcessor.from_pretrained("PekingU/rtdetr_r50vd_coco_o365")
+person_model = RTDetrForObjectDetection.from_pretrained("PekingU/rtdetr_r50vd_coco_o365", device_map=device)
+
+inputs = person_image_processor(images=image, return_tensors="pt").to(device)
+
+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
+)
+result = results[0]  # take first image results
+
+# Human label refers 0 index in COCO dataset
+person_boxes = result["boxes"][result["labels"] == 0]
+person_boxes = person_boxes.cpu().numpy()
+
+# Convert boxes from VOC (x1, y1, x2, y2) to COCO (x1, y1, w, h) format
+person_boxes[:, 2] = person_boxes[:, 2] - person_boxes[:, 0]
+person_boxes[:, 3] = person_boxes[:, 3] - person_boxes[:, 1]
+
+# ------------------------------------------------------------------------
+# Stage 2. Detect keypoints for each person found
+# ------------------------------------------------------------------------
+
+image_processor = AutoProcessor.from_pretrained("usyd-community/vitpose-plus-huge")
+model = VitPoseForPoseEstimation.from_pretrained("usyd-community/vitpose-plus-huge", device_map=device)
+
+inputs = image_processor(image, boxes=[person_boxes], return_tensors="pt").to(device)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+
+pose_results = image_processor.post_process_pose_estimation(outputs, boxes=[person_boxes], threshold=0.3)
+image_pose_result = pose_results[0]  # results for first image
+
+for i, person_pose in enumerate(image_pose_result):
+    print(f"Person #{i}")
+    for keypoint, label, score in zip(
+        person_pose["keypoints"], person_pose["labels"], person_pose["scores"]
+    ):
+        keypoint_name = model.config.id2label[label.item()]
+        x, y = keypoint
+        print(f" - {keypoint_name}: x={x.item():.2f}, y={y.item():.2f}, score={score.item():.2f}")
+
+```
+Output:
+```
+Person #0
+ - Nose: x=428.25, y=170.88, score=0.98
+ - L_Eye: x=428.76, y=168.03, score=0.97
+ - R_Eye: x=428.09, y=168.15, score=0.82
+ - L_Ear: x=433.28, y=167.72, score=0.95
+ - R_Ear: x=440.77, y=166.66, score=0.88
+ - L_Shoulder: x=440.52, y=177.60, score=0.92
+ - R_Shoulder: x=444.64, y=178.11, score=0.70
+ - L_Elbow: x=436.64, y=198.21, score=0.92
+ - R_Elbow: x=431.42, y=201.19, score=0.76
+ - L_Wrist: x=430.96, y=218.39, score=0.98
+ - R_Wrist: x=419.95, y=213.27, score=0.85
+ - L_Hip: x=445.33, y=222.93, score=0.77
+ - R_Hip: x=451.91, y=222.52, score=0.75
+ - L_Knee: x=443.31, y=255.61, score=0.83
+ - R_Knee: x=451.42, y=255.03, score=0.84
+ - L_Ankle: x=447.76, y=287.33, score=0.68
+ - R_Ankle: x=456.78, y=286.08, score=0.83
+Person #1
+ - Nose: x=398.23, y=181.74, score=0.89
+ - L_Eye: x=398.31, y=179.77, score=0.84
+ - R_Eye: x=395.99, y=179.46, score=0.91
+ - R_Ear: x=388.95, y=180.24, score=0.86
+ - L_Shoulder: x=397.35, y=194.22, score=0.73
+ - R_Shoulder: x=384.50, y=190.86, score=0.58
+```
 
 ## Training Details
 
@@ -79,121 +176,75 @@ Use the code below to get started with the model.
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
+Dataset details. We use MS COCO [28], AI Challenger [41], MPII [3], and CrowdPose [22] datasets
+for training and evaluation. OCHuman [54] dataset is only involved in the evaluation stage to measure
+the models’ performance in dealing with occluded people. The MS COCO dataset contains 118K
+images and 150K human instances with at most 17 keypoint annotations each instance for training.
+The dataset is under the CC-BY-4.0 license. MPII dataset is under the BSD license and contains
+15K images and 22K human instances for training. There are at most 16 human keypoints for each
+instance annotated in this dataset. AI Challenger is much bigger and contains over 200K training
+images and 350 human instances, with at most 14 keypoints for each instance annotated. OCHuman
+contains human instances with heavy occlusion and is just used for val and test set, which includes
+4K images and 8K instances.
 
 
 #### 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 -->
-
-#### Speeds, Sizes, Times [optional]
+- **Training regime:** ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6579e0eaa9e58aec614e9d97/Gj6gGcIGO3J5HD2MAB_4C.png)
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+#### Speeds, Sizes, Times
 
-[More Information Needed]
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6579e0eaa9e58aec614e9d97/rsCmn48SAvhi8xwJhX8h5.png)
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+OCHuman val and test set. To evaluate the performance of human pose estimation models on the
+human instances with heavy occlusion, we test the ViTPose variants and representative models on
+the OCHuman val and test set with ground truth bounding boxes. We do not adopt extra human
+detectors since not all human instances are annotated in the OCHuman datasets, where the human
+detector will cause a lot of “false positive” bounding boxes and can not reflect the true ability of
+pose estimation models. Specifically, the decoder head of ViTPose corresponding to the MS COCO
+dataset is used, as the keypoint definitions are the same in MS COCO and OCHuman datasets.
 
-[More Information Needed]
+MPII val set. We evaluate the performance of ViTPose and representative models on the MPII val
+set with the ground truth bounding boxes. Following the default settings of MPII, we use PCKh
+as metric for performance evaluation.
 
 ### Results
 
-[More Information Needed]
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6579e0eaa9e58aec614e9d97/FcHVFdUmCuT2m0wzB8QSS.png)
 
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6579e0eaa9e58aec614e9d97/kf3e1ifJkVtOMbISvmMsM.png)
 
 #### Hardware
 
-[More Information Needed]
-
-#### Software
+The models are trained on 8 A100 GPUs based on the mmpose codebase
 
-[More Information Needed]
 
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+```bibtex
+@article{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}
+}
+@misc{xu2023vitposevisiontransformergeneric,
+      title={ViTPose++: Vision Transformer for Generic Body Pose Estimation}, 
+      author={Yufei Xu and Jing Zhang and Qiming Zhang and Dacheng Tao},
+      year={2023},
+      eprint={2212.04246},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2212.04246}, 
+}
+```