Update README.md

README.md

@@ -35,13 +35,15 @@ After pre-training with Objects365, RT-DETR-R50 / R101 achieves 55.3% / 56.2% AP
 
 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]
+- **Developed by:** Yian Zhao and Sangbum Choi
+- **Funded by [optional]:**  National Key R&D Program of China (No.2022ZD0118201), Natural Science Foundation of China (No.61972217, 32071459, 62176249, 62006133, 62271465),
+and the Shenzhen Medical Research Funds in China (No.
+B2302037). 
+- **Shared by [optional]:** Sangbum Choi
+- **Model type:**
+- **Language(s) (NLP):** 
+- **License:** Apache-2.0
+- **Finetuned from model [optional]:** 
 
 ### Model Sources [optional]
 
@@ -65,20 +67,14 @@ You can use the raw model for object detection. See the [model hub](https://hugg
 
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-[More Information Needed]
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 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
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 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
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 ### Recommendations
 
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
@@ -149,7 +145,7 @@ Images are resized/rescaled such that the shortest side is at 640 pixels.
 
 #### 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:** <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6579e0eaa9e58aec614e9d97/E15I9MwZCtwNIms-W8Ra9.png)
 
@@ -157,8 +153,6 @@ Images are resized/rescaled such that the shortest side is at 640 pixels.
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
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 ## Evaluation
 
 <!-- This section describes the evaluation protocols and provides the results. -->
@@ -171,24 +165,16 @@ This model achieves an AP (average precision) of 53.1 on COCO 2017 validation. F
 
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 #### Factors
 
 <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
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 #### Metrics
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
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 ### Results
 
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 #### Summary
 
 
@@ -197,19 +183,12 @@ This model achieves an AP (average precision) of 53.1 on COCO 2017 validation. F
 
 <!-- Relevant interpretability work for the model goes here -->
 
-[More Information Needed]
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 ## 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]
 
@@ -219,16 +198,10 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ### Compute Infrastructure
 
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-
 #### Hardware
 
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 #### Software
 
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 ## 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. -->
@@ -248,22 +221,14 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 **APA:**
 
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 ## Glossary [optional]
 
 <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
 
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 ## More Information [optional]
 
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 ## Model Card Authors [optional]
 
 [Sangbum Choi](https://huggingface.co/danelcsb)
 
 ## Model Card Contact
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