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36
README.md
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@ -23,7 +23,7 @@ Over the past years, YOLOs have emerged as the predominant paradigm in the field
## Notes ## 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. - 2024/05/31: 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)! - 2024/05/30: 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. - 2024/05/27: We have updated the [checkpoints](https://huggingface.co/collections/jameslahm/yolov10-665b0d90b0b5bb85129460c2) with other attributes, like class names and training args, for ease of use.
## UPDATES 🔥 ## UPDATES 🔥
- 2024/06/01: Thanks to [ErlanggaYudiPradana](https://github.com/rlggyp) for the integration with [C++ | OpenVINO | OpenCV](https://github.com/rlggyp/YOLOv10-OpenVINO-CPP-Inference) - 2024/06/01: Thanks to [ErlanggaYudiPradana](https://github.com/rlggyp) for the integration with [C++ | OpenVINO | OpenCV](https://github.com/rlggyp/YOLOv10-OpenVINO-CPP-Inference)
@ -44,12 +44,12 @@ COCO
| Model | Test Size | #Params | FLOPs | AP<sup>val</sup> | Latency | | Model | Test Size | #Params | FLOPs | AP<sup>val</sup> | Latency |
|:---------------|:----:|:---:|:--:|:--:|:--:| |:---------------|:----:|:---:|:--:|:--:|:--:|
| [YOLOv10-N](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10n.pt) | 640 | 2.3M | 6.7G | 38.5% | 1.84ms | | [YOLOv10-N](https://huggingface.co/jameslahm/yolov10n) | 640 | 2.3M | 6.7G | 38.5% | 1.84ms |
| [YOLOv10-S](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10s.pt) | 640 | 7.2M | 21.6G | 46.3% | 2.49ms | | [YOLOv10-S](https://huggingface.co/jameslahm/yolov10s) | 640 | 7.2M | 21.6G | 46.3% | 2.49ms |
| [YOLOv10-M](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10m.pt) | 640 | 15.4M | 59.1G | 51.1% | 4.74ms | | [YOLOv10-M](https://huggingface.co/jameslahm/yolov10m) | 640 | 15.4M | 59.1G | 51.1% | 4.74ms |
| [YOLOv10-B](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10b.pt) | 640 | 19.1M | 92.0G | 52.5% | 5.74ms | | [YOLOv10-B](https://huggingface.co/jameslahm/yolov10b) | 640 | 19.1M | 92.0G | 52.5% | 5.74ms |
| [YOLOv10-L](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10l.pt) | 640 | 24.4M | 120.3G | 53.2% | 7.28ms | | [YOLOv10-L](https://huggingface.co/jameslahm/yolov10l) | 640 | 24.4M | 120.3G | 53.2% | 7.28ms |
| [YOLOv10-X](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10x.pt) | 640 | 29.5M | 160.4G | 54.4% | 10.70ms | | [YOLOv10-X](https://huggingface.co/jameslahm/yolov10x) | 640 | 29.5M | 160.4G | 54.4% | 10.70ms |
## Installation ## Installation
`conda` virtual environment is recommended. `conda` virtual environment is recommended.
@ -61,14 +61,14 @@ pip install -e .
``` ```
## Demo ## Demo
``` ```
wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10s.pt
python app.py python app.py
# Please visit http://127.0.0.1:7860 # Please visit http://127.0.0.1:7860
``` ```
## Validation ## Validation
[`yolov10n.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10n.pt) [`yolov10s.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10s.pt) [`yolov10m.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10m.pt) [`yolov10b.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10b.pt) [`yolov10l.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10l.pt) [`yolov10x.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10x.pt) [`yolov10n`](https://huggingface.co/jameslahm/yolov10n) [`yolov10s`](https://huggingface.co/jameslahm/yolov10s) [`yolov10m`](https://huggingface.co/jameslahm/yolov10m) [`yolov10b`](https://huggingface.co/jameslahm/yolov10b) [`yolov10l`](https://huggingface.co/jameslahm/yolov10l) [`yolov10x`](https://huggingface.co/jameslahm/yolov10x)
``` ```
wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10s.pt
yolo val model=yolov10n/s/m/b/l/x.pt data=coco.yaml batch=256 yolo val model=yolov10n/s/m/b/l/x.pt data=coco.yaml batch=256
``` ```
@ -78,7 +78,7 @@ from ultralytics import YOLOv10
model = YOLOv10('yolov10{n/s/m/b/l/x}.pt') model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
# or # or
model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}.pt') model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
model.val(data='coco.yaml', batch=256) model.val(data='coco.yaml', batch=256)
``` ```
@ -98,13 +98,25 @@ model = YOLOv10()
# pretrained weights like below # pretrained weights like below
# model = YOLOv10('yolov10{n/s/m/b/l/x}.pt') # model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
# Or # Or
# model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}.pt') # model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
model.train(data='coco.yaml', epochs=500, batch=256, imgsz=640) model.train(data='coco.yaml', epochs=500, batch=256, imgsz=640)
# Note that you can upload your trained model to HuggingFace Hub like below # Note that you can upload your trained model to HuggingFace Hub like below
# model.push_to_hub("reponame", config={"model": "yolov10n/s/m/b/l/x.yaml"}) # model.push_to_hub("reponame", config={"model": "yolov10n/s/m/b/l/x.yaml"})
``` ```
## Push to hub to 🤗
Optionally, you can push your fine-tuned model to the [Hugging Face hub](https://huggingface.co/) as a public or private model:
```python
# let's say you have fine-tuned a model for crop detection
model.push_to_hub("<your-hf-username-or-organization/yolov10-finetuned-crop-detection")
# you can also pass `private=True` if you don't want everyone to see your model
model.push_to_hub("<your-hf-username-or-organization/yolov10-finetuned-crop-detection", private=True)
```
## Prediction ## 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. 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.
``` ```
@ -117,7 +129,7 @@ from ultralytics import YOLOv10
model = YOLOv10('yolov10{n/s/m/b/l/x}.pt') model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
# or # or
model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}.pt') model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
model.predict() model.predict()
``` ```

4
app.py
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@ -4,8 +4,8 @@
import gradio as gr import gradio as gr
from ultralytics import YOLOv10 from ultralytics import YOLOv10
def yolov10_inference(image, model_path, image_size, conf_threshold): def yolov10_inference(image, image_size, conf_threshold):
model = YOLOv10(model_path) model = YOLOv10.from_pretrained("jameslahm/yolov10n")
model.predict(source=image, imgsz=image_size, conf=conf_threshold, save=True) model.predict(source=image, imgsz=image_size, conf=conf_threshold, save=True)

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ultralytics/engine/model.py
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@ -13,10 +13,8 @@ from ultralytics.hub.utils import HUB_WEB_ROOT
from ultralytics.nn.tasks import attempt_load_one_weight, guess_model_task, nn, yaml_model_load from ultralytics.nn.tasks import attempt_load_one_weight, guess_model_task, nn, yaml_model_load
from ultralytics.utils import ASSETS, DEFAULT_CFG_DICT, LOGGER, RANK, SETTINGS, callbacks, checks, emojis, yaml_load from ultralytics.utils import ASSETS, DEFAULT_CFG_DICT, LOGGER, RANK, SETTINGS, callbacks, checks, emojis, yaml_load
from huggingface_hub import PyTorchModelHubMixin
class Model(nn.Module):
class Model(nn.Module, PyTorchModelHubMixin):
""" """
A base class for implementing YOLO models, unifying APIs across different model types. A base class for implementing YOLO models, unifying APIs across different model types.

4
ultralytics/models/yolov10/model.py
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@ -4,7 +4,9 @@ from .val import YOLOv10DetectionValidator
from .predict import YOLOv10DetectionPredictor from .predict import YOLOv10DetectionPredictor
from .train import YOLOv10DetectionTrainer from .train import YOLOv10DetectionTrainer
class YOLOv10(Model): from huggingface_hub import PyTorchModelHubMixin
class YOLOv10(Model, PyTorchModelHubMixin, library_name="ultralytics", repo_url="https://github.com/THU-MIG/yolov10", tags=["object-detection", "yolov10"]):
def __init__(self, model="yolov10n.pt", task=None, verbose=False, def __init__(self, model="yolov10n.pt", task=None, verbose=False,
names=None): names=None):