YOLOv7

YOLOv7 is a state of the art object detection model.

Overview

YOLOv7 was released in July 2022 by WongKinYiu and AlexeyAB. It achieves state of the art performance on and are trained to detect the generic 80 classes in the MS COCO dataset for real-time object detection.

There are six versions of the model ranging from the namesake YOLOv7 (fastest, smallest, and least accurate) to the beefy YOLOv7-E6E (slowest, largest, and most accurate).

The differences between the different sizes of the model are:

The image input resolution
The number of anchors
The number of parameters
The number of layers

The evaluation of YOLOv7 models show that they infer faster (x-axis) and with greater accuracy (y-axis) than comparable realtime object detection models. YOLOv7 evaluates in the upper left - faster and more accurate than its peer networks.

Evolution of layer aggregation strategies in YOLOv7

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Performance

Model	Test Size	AP^test	AP₅₀^test	AP₇₅^test	batch 1 fps	batch 32 average time
YOLOv7	640	51.4%	69.7%	55.9%	161 fps	2.8 ms
YOLOv7-X	640	53.1%	71.2%	57.8%	114 fps	4.3 ms
YOLOv7-W6	1280	54.9%	72.6%	60.1%	84 fps	7.6 ms
YOLOv7-E6	1280	56.0%	73.5%	61.2%	56 fps	12.3 ms
YOLOv7-D6	1280	56.6%	74.0%	61.8%	44 fps	15.0 ms
YOLOv7-E6E	1280	56.8%	74.4%	62.1%	36 fps	18.7 ms

Use This Model

You can run fine-tuned YOLOv7 object detection models with Inference.

First, install Inference:‍


pip install inference

Retrieve your Roboflow API key and save it in an environment variable called ROBOFLOW_API_KEY:


export ROBOFLOW_API_KEY="your-api-key"

To use your model, run the following code:


import inference
model = inference.load_roboflow_model("model-name/version")
results = model.infer(image="YOUR_IMAGE.jpg")

Above, replace:

YOUR_IMAGE.jpg with the path to your image.
model_id/version with the YOLOv8 model ID and version you want to use. Learn how to retrieve your model and version ID

Label Data Automatically with YOLOv7

You can automatically label a dataset using YOLOv7 with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use YOLOv7 to train a computer vision model.

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