Update README.md

README.md

@@ -38,9 +38,6 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 - **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:** apache-2.0
 - **Finetuned from model [optional]:** [More Information Needed]
 
@@ -50,29 +47,22 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 - **Repository:** https://github.com/ViTAE-Transformer/ViTPose
 - **Paper [optional]:** https://arxiv.org/pdf/2204.12484
-- **Demo [optional]:** [More Information Needed]
+- **Demo [optional]:** 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. -->
+The ViTPose model, developed by the ViTAE-Transformer team, is primarily designed for pose estimation tasks. Here are some direct uses of the model:
 
-### Direct Use
+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 without fine-tuning or plugging 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.
 
-### Downstream Use [optional]
+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 is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+Gaming and Animation: ViTPose can be integrated into gaming and animation systems to create more realistic character movements and interactions.
 
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
@@ -88,12 +78,6 @@ prompt-based tuning to demonstrate the flexibility of ViTPose further. In additi
 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
-
-<!-- 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.
-
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
@@ -173,15 +157,16 @@ for pose_result in pose_results:
 
 <!-- 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
@@ -199,69 +184,31 @@ for pose_result in pose_results:
 
 <!-- This section describes the evaluation protocols and provides the results. -->
 
-### Testing Data, Factors & Metrics
-
-#### Testing Data
+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.
 
-<!-- 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. -->
-
-[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
 
 ![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
 
 The models are trained on 8 A100 GPUs based on the mmpose codebase [11]
 
-#### Software
-
-[More Information Needed]
 
 ## Citation [optional]
 
@@ -277,26 +224,4 @@ The models are trained on 8 A100 GPUs based on the mmpose codebase [11]
       archivePrefix={arXiv},
       primaryClass={cs.CV},
       url={https://arxiv.org/abs/2204.12484}, 
-}
-
-**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]
+}