YOLOv7

Install

YOLOv7 needs no extra beyond the base package.

pip install libreyolo

Predict

Weights download from Hugging Face on first use and are cached locally.

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreYOLO7b.pt")result = model(SAMPLE_IMAGE, save=True) for box in result.boxes:    print(box.cls, box.conf, box.xyxy)

libreyolo predict model=LibreYOLO7b.pt source=https://raw.githubusercontent.com/LibreYOLO/libreyolo/release/libreyolo/assets/parkour.jpg save=True

The returned Results object is the one every family returns, so swapping in a different detector is a one line change. conf sets the confidence threshold and iou the NMS threshold applied after the anchor-based head is decoded. See prediction for sources, streaming and result handling.

Variants

LibreYOLO ships one size, b. Upstream publishes a single YOLOv7 model, so there is no size to choose between.

Train

from libreyolo import LibreYOLO model = LibreYOLO("LibreYOLO7b.pt")model.train(data="my-dataset.yaml", epochs=300, imgsz=640, batch=16, lr0=0.01)

libreyolo train model=LibreYOLO7b.pt data=my-dataset.yaml \  epochs=300 imgsz=640 batch=16 lr0=0.01

from libreyolo import LibreYOLO7 # pretrained=True always loads the published LibreYOLO7b.pt checkpoint,# regardless of what this instance was constructed with. Constructing# the class directly, rather than through LibreYOLO(), starts with no# weights loaded at all.model = LibreYOLO7(None, size="b")model.train(data="my-dataset.yaml", epochs=300, pretrained=True)

pretrained is read, unlike the no-op of the same name on some other families here: pass True to warm-start from the published LibreYOLO7b.pt checkpoint (auto-downloaded), or a path or name for anything else. That published checkpoint is 80-class COCO, so requesting it on a model already rebuilt for a different class count first rebuilds back to 80, loads it, then transfers every shape-matching tensor into the target head count once the dataset's class count is read. resume=True cannot be combined with pretrained. Left at the default None, training continues from whatever the model was constructed with, or from a random initialization if nothing was loaded.

Left alone otherwise, the trainer runs 300 epochs at lr0=0.01 with SGD momentum 0.937, a 3-epoch warmup, and the same SimOTA assignment and final 15-epoch no-augmentation phase YOLOX uses, adapted to the anchor-based head. The one difference: YOLOX adds an L1 box-regression refinement during those final epochs that v7 skips, because v7's SimOTA loss carries no raw-offset L1 branch to refine.

See training for datasets, augmentation, multi-GPU and loggers.

Validate

val() returns a dictionary of metrics/ keys covering precision, recall, mAP 50 and mAP 50-95, measured against any dataset in the format you trained on.

from libreyolo import LibreYOLO model = LibreYOLO("LibreYOLO7b.pt")metrics = model.val(data="my-dataset.yaml") print(metrics["metrics/mAP50-95"])print(metrics["metrics/mAP50"])

libreyolo val model=LibreYOLO7b.pt data=my-dataset.yaml

Export

An exported artifact loads back through LibreYOLO() on its file suffix, so a .onnx or .engine file behaves like a checkpoint and returns the same Results. Running the graph in a bare runtime, with no LibreYOLO installed, is also supported, but then preprocessing and postprocessing are yours to write.

from libreyolo import LibreYOLO model = LibreYOLO("LibreYOLO7b.pt")model.export(format="onnx", imgsz=640)model.export(format="tensorrt", imgsz=640, half=True)

libreyolo export model=LibreYOLO7b.pt format=onnx imgsz=640libreyolo export model=LibreYOLO7b.pt format=tensorrt imgsz=640 half=True

from libreyolo import LibreYOLO, SAMPLE_IMAGE # The factory routes on the file suffix, so an exported artifact loads# like any checkpoint and returns the same Results object.model = LibreYOLO("LibreYOLO7b.onnx")result = model(SAMPLE_IMAGE) print(result.boxes.xyxy)

Checkpoints

Every published weight file for this family.

Licensing

Citation

@inproceedings{wang2022yolov7,
      title={{YOLOv7}: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors},
      author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
      year={2023},
      booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
}