FOMO

Install

FOMO needs no extra beyond the base package.

pip install libreyolo

Predict

Unlike every other family on this site, LibreFOMO weights are not auto-downloaded: LibreYOLO("LibreFOMOs-point.pt") looks for that file on disk and raises a ValueError naming it rather than fetching it from Hugging Face. Download a checkpoint from the LibreYOLO org first and load it by local path, or train your own (see Train below).

from libreyolo import LibreYOLO, SAMPLE_IMAGE # LibreFOMO weights are not auto-downloaded (see Checkpoints below).# Point this at a checkpoint you already downloaded locally.model = LibreYOLO("./LibreFOMOs-point.pt")result = model(SAMPLE_IMAGE, save=True) for point in result.points:    print(point.cls, point.conf, point.xy)

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

The result carries a points payload instead of boxes: each row is x, y, class, confidence, available as result.points.data, or through the .xy, .xyn, .cls and .conf accessors. There is no iou threshold to set, because there are no boxes to suppress; predict(..., nms_radius=1) controls how many grid cells apart two detections must be to both survive, and the filename must carry FOMO's -point task suffix for the loader to recognize it. See prediction for sources, streaming and result handling.

Variants

Three sizes, s, m and l, use progressively wider MobileNetV2-style backbones at correspondingly larger, fixed input resolutions, each behind a single 1x1 classification head. This family carries no benchmark table here; checkpoint file size in the table below is the clearest per-size signal currently published.

Train

from libreyolo import LibreYOLO model = LibreYOLO("./LibreFOMOs-point.pt")model.train(    data="my-dataset.yaml",    epochs=40, batch=32, lr0=3e-4,)

# imgsz must be passed: the CLI defaults it to 640, and the s# checkpoint accepts only its native 96.libreyolo train model=./LibreFOMOs-point.pt data=my-dataset.yaml imgsz=96 epochs=40 batch=32 lr0=3e-4

imgsz is not a free choice: it defaults to the loaded checkpoint's native resolution, and passing a different value raises ValueError naming the size it expects. Those sizes are 96 for s, 192 for m and 224 for l. The CLI defaults imgsz to 640, so a libreyolo train command has to set it explicitly to match the checkpoint.

Left alone otherwise, the trainer runs 40 epochs at batch 32 with Adam at lr0=3e-4, no weight decay, and a foreground class weighted 100x over background in the per-cell cross-entropy loss, since almost every grid cell is background in a typical scene. EMA and mixed precision are both off by default, and none of the geometric or color augmentations used elsewhere in LibreYOLO are applied: mosaic, mixup, HSV jitter, flip, rotation, translation and shear are all zero.

This is the path the published LibreFOMO checkpoints were trained with, from scratch on COCO.

See training for datasets and loggers.

Validate

val() dispatches to a grid-level validator built for this family. Alongside the point-matching metrics/precision, metrics/recall and metrics/mAP@ keys shared with other point tasks, it sweeps confidence thresholds and nms_radius values and publishes the best-F1 combination under metrics/grid_F1, metrics/grid_precision, metrics/grid_recall and metrics/grid_mean_distance, plus the threshold and radius that produced it under decode/threshold and decode/nms_radius.

from libreyolo import LibreYOLO model = LibreYOLO("./LibreFOMOs-point.pt")metrics = model.val(data="my-dataset.yaml") print(metrics["metrics/grid_F1"])print(metrics["metrics/grid_precision"], metrics["metrics/grid_recall"])

libreyolo val model=./LibreFOMOs-point.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("./LibreFOMOs-point.pt")model.export(format="onnx")model.export(format="tensorrt", half=True)

libreyolo export model=./LibreFOMOs-point.pt format=onnx

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("./LibreFOMOs-point.onnx")result = model(SAMPLE_IMAGE) print(result.points.xy)

Checkpoints

Every published weight file for this family. None of them download automatically: fetch the file you want from the linked Hugging Face page and pass its local path to LibreYOLO().

Licensing