diff --git a/README.md b/README.md index 7b914b7..b7e8fa2 100644 --- a/README.md +++ b/README.md @@ -27,7 +27,7 @@ You can finally install YOLOv5 o
This yolov5 package contains everything from ultralytics/yolov5 at this commit plus:
-1. Easy installation via pip: `pip install yolov5` +1. Easy installation via pip: pip install yolov5
2. Full CLI integration with fire package
@@ -45,30 +45,24 @@ This yolov5 package contains everything from ultralytics/yolov5 Install -
-Install yolov5 using pip (for Python >=3.7) +Install yolov5 using pip (for Python >=3.7) ```console pip install yolov5 ``` -
+##
Model Zoo
-
-Install yolov5 using pip `(for Python 3.6)` -```console -pip install "numpy>=1.18.5,<1.20" "matplotlib>=3.2.2,<4" -pip install yolov5 -``` -
+
-##
Use from Python
+Effortlessly explore and use finetuned YOLOv5 models with one line of code: awesome-yolov5-models + +
-
-Basic +##
Use from Python
```python import yolov5 @@ -112,52 +106,6 @@ results.save(save_dir='results/') ``` -
- -
-Alternative - -```python -from yolov5 import YOLOv5 - -# set model params -model_path = "yolov5/weights/yolov5s.pt" -device = "cuda:0" # or "cpu" - -# init yolov5 model -yolov5 = YOLOv5(model_path, device) - -# load images -image1 = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg' -image2 = 'https://github.com/ultralytics/yolov5/blob/master/data/images/bus.jpg' - -# perform inference -results = yolov5.predict(image1) - -# perform inference with larger input size -results = yolov5.predict(image1, size=1280) - -# perform inference with test time augmentation -results = yolov5.predict(image1, augment=True) - -# perform inference on multiple images -results = yolov5.predict([image1, image2], size=1280, augment=True) - -# parse results -predictions = results.pred[0] -boxes = predictions[:, :4] # x1, y1, x2, y2 -scores = predictions[:, 4] -categories = predictions[:, 5] - -# show detection bounding boxes on image -results.show() - -# save results into "results/" folder -results.save(save_dir='results/') -``` - -
-
Train/Detect/Test/Export