Merge remote-tracking branch 'upstream/main' into feature/update_urls

README.md

@@ -11,7 +11,7 @@ Official PyTorch implementation of **YOLOv10**.
 
 [YOLOv10: Real-Time End-to-End Object Detection](https://arxiv.org/abs/2405.14458).\
 Ao Wang, Hui Chen, Lihao Liu, Kai Chen, Zijia Lin, Jungong Han, and Guiguang Ding\
-[![arXiv](https://img.shields.io/badge/arXiv-2405.14458-b31b1b.svg)](https://arxiv.org/abs/2405.14458) <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov10-object-detection-on-custom-dataset.ipynb#scrollTo=SaKTSzSWnG7s"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>  [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/kadirnar/Yolov10)   [![Transformers.js Demo](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Transformers.js-blue)](https://huggingface.co/spaces/Xenova/yolov10-web)
+[![arXiv](https://img.shields.io/badge/arXiv-2405.14458-b31b1b.svg)](https://arxiv.org/abs/2405.14458) <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov10-object-detection-on-custom-dataset.ipynb#scrollTo=SaKTSzSWnG7s"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue)](https://huggingface.co/collections/jameslahm/yolov10-665b0d90b0b5bb85129460c2)  [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/kadirnar/Yolov10)   [![Transformers.js Demo](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Transformers.js-blue)](https://huggingface.co/spaces/Xenova/yolov10-web)
 
 <details>
   <summary>
@@ -23,16 +23,16 @@ Over the past years, YOLOs have emerged as the predominant paradigm in the field
 ## Notes
 - 2024/05/31: Please use the [exported format](https://github.com/THU-MIG/yolov10?tab=readme-ov-file#export) for benchmark. In the non-exported format, e.g., pytorch, the speed of YOLOv10 is biased because the unnecessary `cv2` and `cv3` operations in the `v10Detect` are executed during inference.
 - 2024/05/30: We provide [some clarifications and suggestions](https://github.com/THU-MIG/yolov10/issues/136) for detecting smaller objects or objects in the distance with YOLOv10. Thanks to [SkalskiP](https://github.com/SkalskiP)!
-
+- 2024/05/27: We have updated the [checkpoints](https://github.com/THU-MIG/yolov10/releases/tag/v1.1) with other attributes, like class names and training args, for ease of use.
 
 ## UPDATES 🔥
+- 2024/06/01: Thanks to [NielsRogge](https://github.com/NielsRogge) and [AK](https://x.com/_akhaliq) for hosting the models on the HuggingFace Hub!
 - 2024/05/31: Build [yolov10-jetson](https://github.com/Seeed-Projects/jetson-examples/blob/main/reComputer/scripts/yolov10/README.md) docker image by [youjiang](https://github.com/yuyoujiang)!
 - 2024/05/31: Thanks to [mohamedsamirx](https://github.com/mohamedsamirx) for the integration with [BoTSORT, DeepOCSORT, OCSORT, HybridSORT, ByteTrack, StrongSORT using BoxMOT library](https://colab.research.google.com/drive/1-QV2TNfqaMsh14w5VxieEyanugVBG14V?usp=sharing)!
 - 2024/05/31: Thanks to [kaylorchen](https://github.com/kaylorchen) for the integration with [rk3588](https://github.com/kaylorchen/rk3588-yolo-demo)!
 - 2024/05/30: Thanks to [eaidova](https://github.com/eaidova) for the integration with [OpenVINO™](https://github.com/openvinotoolkit/openvino_notebooks/blob/0ba3c0211bcd49aa860369feddffdf7273a73c64/notebooks/yolov10-optimization/yolov10-optimization.ipynb)!
 - 2024/05/29: Add the gradio demo for running the models locally. Thanks to [AK](https://x.com/_akhaliq)!
 - 2024/05/27: Thanks to [sujanshresstha](sujanshresstha) for the integration with [DeepSORT](https://github.com/sujanshresstha/YOLOv10_DeepSORT.git)!
-- 2024/05/27: We have updated the [checkpoints](https://github.com/THU-MIG/yolov10/releases/tag/v1.1) with other attributes, like class names, for ease of use.
 - 2024/05/26: Thanks to [CVHub520](https://github.com/CVHub520) for the integration into [X-AnyLabeling](https://github.com/CVHub520/X-AnyLabeling)!
 - 2024/05/26: Thanks to [DanielSarmiento04](https://github.com/DanielSarmiento04) for integrate in [c++ | ONNX | OPENCV](https://github.com/DanielSarmiento04/yolov10cpp)!
 - 2024/05/25: Add [Transformers.js demo](https://huggingface.co/spaces/Xenova/yolov10-web) and onnx weights(yolov10[n](https://huggingface.co/onnx-community/yolov10n)/[s](https://huggingface.co/onnx-community/yolov10s)/[m](https://huggingface.co/onnx-community/yolov10m)/[b](https://huggingface.co/onnx-community/yolov10b)/[l](https://huggingface.co/onnx-community/yolov10l)/[x](https://huggingface.co/onnx-community/yolov10x)). Thanks to [xenova](https://github.com/xenova)!
@@ -70,17 +70,56 @@ python app.py
 yolo val size="nano" data=coco.yaml batch=256
 ```
 
+Or
+```python
+from ultralytics import YOLOv10
+
+model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
+# or
+model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}.pt')
+
+model.val(data='coco.yaml', batch=256)
+```
+
+
 ## Training 
 ```
 yolo detect train data=coco.yaml model=yolov10n/s/m/b/l/x.yaml epochs=500 batch=256 imgsz=640 device=0,1,2,3,4,5,6,7
 ```
 
+Or
+```python
+from ultralytics import YOLOv10
+
+model = YOLOv10()
+# If you want to finetune the model with pretrained weights, you could load the 
+# pretrained weights like below
+# model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
+# Or
+# model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}.pt')
+
+model.train(data='coco.yaml', epochs=500, batch=256, imgsz=640)
+# Note that you can upload your trained model to HuggingFace Hub like below
+# model.push_to_hub("reponame")
+```
+
 ## Prediction
 Note that a smaller confidence threshold can be set to detect smaller objects or objects in the distance. Please refer to [here](https://github.com/THU-MIG/yolov10/issues/136) for details.
 ```
 yolo predict model=yolov10n/s/m/b/l/x.pt
 ```
 
+Or
+```python
+from ultralytics import YOLOv10
+
+model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
+# or
+model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}.pt')
+
+model.predict()
+```
+
 ## Export
 ```
 # End-to-End ONNX
@@ -96,6 +135,17 @@ trtexec --onnx=yolov10n/s/m/b/l/x.onnx --saveEngine=yolov10n/s/m/b/l/x.engine --
 yolo predict model=yolov10n/s/m/b/l/x.engine
 ```
 
+Or
+```python
+from ultralytics import YOLOv10
+
+model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
+# or
+model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}.pt')
+
+model.export(...)
+```
+
 ## Acknowledgement
 
 The code base is built with [ultralytics](https://github.com/ultralytics/ultralytics) and [RT-DETR](https://github.com/lyuwenyu/RT-DETR).

requirements.txt

@@ -11,3 +11,4 @@ gradio==4.31.5
 opencv-python==4.9.0.80
 psutil==5.9.8
 py-cpuinfo==9.0.0
+huggingface-hub==0.23.2