ultralytics/yolov3: v8 - Final Darknet Compatible Release
Bibliographic record
Abstract
This is the final release of the darknet-compatible version of the https://github.com/ultralytics/yolov3 repository. This release is backwards-compatible with darknet *.cfg files for model configuration. All pytorch (.pt) and darknet (.weights) models/backbones available are attached to this release in the Assets section below. Breaking Changes There are no breaking changes in this release. Bug Fixes Various Added Functionality Various Speed https://cloud.google.com/deep-learning-vm/ Machine type: preemptible n1-standard-8 (8 vCPUs, 30 GB memory) CPU platform: Intel Skylake GPUs: K80 ($0.14/hr), T4 ($0.11/hr), V100 ($0.74/hr) CUDA with Nvidia Apex FP16/32 HDD: 300 GB SSD Dataset: COCO train 2014 (117,263 images) Model: yolov3-spp.cfg Command: python3 train.py --data coco2017.data --img 416 --batch 32 GPU n --batch-size img/s epoch time epoch cost K80 1 32 x 2 11 175 min $0.41 T4 1 2 32 x 2 64 x 1 41 61 48 min 32 min $0.09 $0.11 V100 1 2 32 x 2 64 x 1 122 178 16 min 11 min $0.21 $0.28 2080Ti 1 2 32 x 2 64 x 1 81 140 24 min 14 min - - mAP Size COCO mAP @0.5...0.95 COCO mAP @0.5 YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP-ultralytics 320 14.0 28.7 30.5 37.7 29.1 51.8 52.3 56.8 YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP-ultralytics 416 16.0 31.2 33.9 41.2 33.0 55.4 56.9 60.6 YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP-ultralytics 512 16.6 32.7 35.6 42.6 34.9 57.7 59.5 62.4 YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP-ultralytics 608 16.6 33.1 37.0 43.1 35.4 58.2 60.7 62.8 TODO NA
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.147 | 0.210 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".