LibreYOLO Models
Collection
All LibreYOLO pretrained model weights • 196 items • Updated • 2
LibreECm-obj2coco
NON-COMMERCIAL USE ONLY. Commercial use requires a separate license from INTELLINDUST. These weights are not covered by LibreYOLO's MIT license.
EdgeCrafter M for detect, pretrained on Objects365 and fine-tuned
on COCO. This is an explicit opt-in variant; the COCO-only default is
LibreECm.pt. This file is not the raw 365-class pretraining model.
from libreyolo import LibreYOLO
model = LibreYOLO("LibreECm-obj2coco.pt")
results = model.predict("image.jpg")
Requires a LibreYOLO version containing EC obj2coco filename support.
Existing versions can load a downloaded local checkpoint path if they already
support the corresponding EC task.
Source
Official asset: ecdet_m_o3652coco.pth
- Release:
edgecrafterv1_o365(August 14, 2026) - Source SHA-256:
e134552c75fa1bec850cb3f95eded64eac8c015abbfe14d5dd6ae141e6da66b2 - Converted SHA-256:
24b86ada0185c125e61608fdb7db075cc1d95571f402bc1c7694c4fa72b06c70
Copyright (c) 2026 INTELLINDUST INFORMATION TECHNOLOGY (SHENZHEN) CO., LTD.
Modifications and validation
Conversion selects the released EMA when present and adds LibreYOLO metadata. Learned tensors and state-dict keys are unchanged, verified tensor by tensor. Checkpoint schema, factory loading, complete parameter coverage, and a finite 640-pixel CPU forward pass were checked. COCO accuracy and export parity have not been independently re-evaluated for this mirror.
Converter: convert_ec_weights.py.
License
EdgeCrafter License; included verbatim in LICENSE. See NOTICE for attribution and the license history.
The release asset repository still carries Apache-2.0. The main project changed to non-commercial terms on August 20, 2026, after this August 14 release. LibreYOLO's maintainer chose the stricter EdgeCrafter terms for this optional mirror. This is a disclosed redistribution policy, not upstream clarification of the conflicting notices or a claim that earlier Apache grants were revoked.
Objects365's dataset terms state academic-purpose availability, CC BY 4.0 annotations, and separate image restrictions. This repository redistributes model weights, not dataset images.