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model:
weights:
filepath: data/02_models/yolo/best/weights/best.pt
sha256: 0bf3c7ee9f720c26613c30719fea32f47ed04fc384e443de72414d9f8148ac9d
model_type: yolo11s.pt
dvc:
path: ./data/02_models/yolo/best
hash: md5
md5: be5d61086d3a2b2e68e8a5dd61b14901.dir
size: 44003643
nfiles: 24
data:
data_yaml: data/01_model_input/yolo_train_val_small/datasets/data.yaml
dvc:
path: ./data/01_model_input/yolo_train_val
hash: md5
md5: fcd56c8728d160e957bf0acd8049368d.dir
size: 1780235401
nfiles: 30387
train_run_args:
task: detect
mode: train
model: yolo11s.pt
data: /actions-runner/_work/pyro-train/pyro-train/data/01_model_input/yolo_train_val_small/datasets/data.yaml
epochs: 50
time: null
patience: 20
batch: 16
imgsz: 1024
save: true
save_period: -1
cache: false
device: '0'
workers: 8
project: /actions-runner/_work/pyro-train/pyro-train/data/02_models/yolo
name: best
exist_ok: false
pretrained: true
optimizer: AdamW
verbose: true
seed: 0
deterministic: true
single_cls: true
rect: false
cos_lr: false
close_mosaic: 10
resume: false
amp: true
fraction: 1.0
profile: false
freeze: null
multi_scale: 0.0
compile: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
conf: null
iou: 0.2
max_det: 300
half: false
dnn: false
plots: true
end2end: null
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: true
opset: null
workspace: null
nms: false
lr0: 0.0001
lrf: 0.1
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 6
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
rle: 1.0
angle: 1.0
nbs: 64
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 5.0
translate: 0.15
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
bgr: 0.0
mosaic: 1.0
mixup: 0.2
cutmix: 0.0
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
save_dir: /actions-runner/_work/pyro-train/pyro-train/data/02_models/yolo/best
dvc_lock:
schema: '2.0'
stages:
subsample_model_input:
cmd:
- uv run python ./scripts/data/model_input/build.py --input-dir ./data/01_model_input/yolo_train_val
--output-dir ./data/01_model_input/yolo_train_val_small --sampling-ratio
0.05 --random-seed 0 --loglevel info
deps:
- path: ./data/01_model_input/yolo_train_val
hash: md5
md5: fdfd35a4ac94ba1468938f7e173cfc6c.dir
size: 1735515846
nfiles: 29627
- path: ./scripts/data/model_input/build.py
hash: md5
md5: 7607af10f59267cf9859cb1d372aff26
size: 6432
outs:
- path: ./data/01_model_input/yolo_train_val_small
hash: md5
md5: e63ef47d8416c4817469f4fe55903b66.dir
size: 222775525
nfiles: 3939
train_yolo_baseline:
cmd:
- uv run python ./scripts/model/yolo/train.py --data ./data/01_model_input/yolo_train_val_small/datasets/data.yaml
--config ./scripts/model/yolo/configs/baseline.yaml --output-dir ./data/02_models/yolo/
--experiment-name baseline --loglevel info
deps:
- path: ./data/01_model_input/yolo_train_val_small/
hash: md5
md5: e63ef47d8416c4817469f4fe55903b66.dir
size: 222775525
nfiles: 3939
- path: ./scripts/model/yolo/configs/baseline.yaml
hash: md5
md5: 7e5f15f6be649fdc6ded2badd0224b61
size: 47
- path: ./scripts/model/yolo/train.py
hash: md5
md5: ec596bafe76e946d0770be47b34db816
size: 3873
outs:
- path: ./data/02_models/yolo/baseline
hash: md5
md5: 4bec09fbb10c6a3618f79c22f19d356d.dir
size: 11839587
nfiles: 21
train_yolo_best:
cmd:
- uv run python ./scripts/data/model_input/build.py --input-dir ./data/01_model_input/yolo_train_val
--output-dir ./data/01_model_input/yolo_train_val_small --sampling-ratio
1 --random-seed 0 --loglevel info
- uv run python ./scripts/model/yolo/train.py --data ./data/01_model_input/yolo_train_val_small/datasets/data.yaml
--config ./scripts/model/yolo/configs/best.yaml --output-dir ./data/02_models/yolo/
--experiment-name best --loglevel info
deps:
- path: ./data/01_model_input/yolo_train_val
hash: md5
md5: fcd56c8728d160e957bf0acd8049368d.dir
size: 1780235401
nfiles: 30387
- path: ./scripts/data/model_input/build.py
hash: md5
md5: 7607af10f59267cf9859cb1d372aff26
size: 6432
- path: ./scripts/model/yolo/configs/best.yaml
hash: md5
md5: 82ab9c26b11cfc5f72e1398bfceeb838
size: 194
- path: ./scripts/model/yolo/train.py
hash: md5
md5: ec596bafe76e946d0770be47b34db816
size: 3873
outs:
- path: ./data/02_models/yolo/best
hash: md5
md5: be5d61086d3a2b2e68e8a5dd61b14901.dir
size: 44003643
nfiles: 24
build_manifest_yolo_best:
cmd:
- uv run python ./scripts/model/yolo/build_manifest.py --save-dir ./data/03_reporting/yolo/best/
--dir-model ./data/02_models/yolo/best/ --loglevel info
deps:
- path: ./data/02_models/yolo/best/
hash: md5
md5: 3e6507d8d2bc028c2799bf615dae5d08.dir
size: 43849333
nfiles: 24
- path: ./scripts/model/yolo/build_manifest.py
hash: md5
md5: 494e7a9e52fb74ae3a371b9438d33682
size: 3014
outs:
- path: ./data/03_reporting/yolo/best/
hash: md5
md5: 63a14cea2caf1871b9c77621bf717d32.dir
size: 11525
nfiles: 1
export_yolo_best@onnx-cpu:
cmd:
- uv run python ./scripts/model/yolo/export.py --output-dir ./data/02_models/yolo-export/best/
--model-dir ./data/02_models/yolo/best/ --format onnx --device cpu --loglevel
info
deps:
- path: ./data/02_models/yolo/best/
hash: md5
md5: 3e6507d8d2bc028c2799bf615dae5d08.dir
size: 43849333
nfiles: 24
- path: ./scripts/model/yolo/export.py
hash: md5
md5: 3d65796d064f204e4847fce93ee8ef46
size: 3648
outs:
- path: ./data/02_models/yolo-export/best/onnx/cpu
hash: md5
md5: 7c34c4326c3922a9e35a7142aba6ce6f.dir
size: 38452425
nfiles: 1
export_yolo_best@onnx-mps:
cmd:
- uv run python ./scripts/model/yolo/export.py --output-dir ./data/02_models/yolo-export/best/
--model-dir ./data/02_models/yolo/best/ --format onnx --device mps --loglevel
info
deps:
- path: ./data/02_models/yolo/best/
hash: md5
md5: 3e6507d8d2bc028c2799bf615dae5d08.dir
size: 43849333
nfiles: 24
- path: ./scripts/model/yolo/export.py
hash: md5
md5: 3d65796d064f204e4847fce93ee8ef46
size: 3648
outs:
- path: ./data/02_models/yolo-export/best/onnx/mps
hash: md5
md5: d62c35ef82626b96af091a0125b1a4e0.dir
size: 38163894
nfiles: 1
export_yolo_best@ncnn-cpu:
cmd:
- uv run python ./scripts/model/yolo/export.py --output-dir ./data/02_models/yolo-export/best/
--model-dir ./data/02_models/yolo/best/ --format ncnn --device cpu --loglevel
info
deps:
- path: ./data/02_models/yolo/best/
hash: md5
md5: 3e6507d8d2bc028c2799bf615dae5d08.dir
size: 43849333
nfiles: 24
- path: ./scripts/model/yolo/export.py
hash: md5
md5: 3d65796d064f204e4847fce93ee8ef46
size: 3648
outs:
- path: ./data/02_models/yolo-export/best/ncnn/cpu
hash: md5
md5: cd601ec02f4c0a89eca2a39780f8aa78.dir
size: 38001888
nfiles: 5
export_yolo_best@ncnn-mps:
cmd:
- uv run python ./scripts/model/yolo/export.py --output-dir ./data/02_models/yolo-export/best/
--model-dir ./data/02_models/yolo/best/ --format ncnn --device mps --loglevel
info
deps:
- path: ./data/02_models/yolo/best/
hash: md5
md5: 3e6507d8d2bc028c2799bf615dae5d08.dir
size: 43849333
nfiles: 24
- path: ./scripts/model/yolo/export.py
hash: md5
md5: 3d65796d064f204e4847fce93ee8ef46
size: 3648
outs:
- path: ./data/02_models/yolo-export/best/ncnn/mps
hash: md5
md5: dd0bd9fcc6896aafdaaea48e94cd125c.dir
size: 38001888
nfiles: 5
fetch_model_input:
cmd:
- uv run dvc get https://github.com/pyronear/pyro-dataset data/processed/yolo_train_val
--rev v2.1.0 --out ./data/01_model_input/yolo_train_val || uv run dvc get
git@github.com:pyronear/pyro-dataset.git data/processed/yolo_train_val --rev
v2.1.0 --out ./data/01_model_input/yolo_train_val
outs:
- path: ./data/01_model_input/yolo_train_val
hash: md5
md5: fcd56c8728d160e957bf0acd8049368d.dir
size: 1780235401
nfiles: 30387
fetch_sequential_val:
cmd:
- uv run dvc get https://github.com/pyronear/pyro-dataset data/processed/sequential_train_val/val
--rev v2.1.0 --out ./data/01_model_input/sequential_train_val/val || uv
run dvc get git@github.com:pyronear/pyro-dataset.git data/processed/sequential_train_val/val
--rev v2.1.0 --out ./data/01_model_input/sequential_train_val/val
outs:
- path: ./data/01_model_input/sequential_train_val/val
hash: md5
md5: 2ca47638a4b0cc9ca7d4011376c54e45.dir
size: 631259421
nfiles: 12448
evaluate_sequential_yolo_best:
cmd:
- uv run python ./scripts/model/yolo/evaluate_sequential.py --model-path ./data/02_models/yolo-export/best/onnx/cpu/best.onnx
--data-dir ./data/01_model_input/sequential_train_val/val --output-dir ./data/03_reporting/yolo/sequential/best/
--max-frames 15 --loglevel warning --force-cpu
deps:
- path: ./data/01_model_input/sequential_train_val/val
hash: md5
md5: 2ca47638a4b0cc9ca7d4011376c54e45.dir
size: 631259421
nfiles: 12448
- path: ./data/02_models/yolo-export/best/onnx/cpu
hash: md5
md5: 7c34c4326c3922a9e35a7142aba6ce6f.dir
size: 38452425
nfiles: 1
- path: ./scripts/model/yolo/evaluate_sequential.py
hash: md5
md5: 2e3cbddd3142874d2127fc875c9faca7
size: 8450
outs:
- path: ./data/03_reporting/yolo/sequential/best/
hash: md5
md5: 93d5fb63078e1f20386e9e5be7a2c839.dir
size: 39230
nfiles: 1
predict_sequential_val:
cmd:
- uv run python predict_sequential.py --model-path ./data/02_models/yolo/best/weights/best.pt
--data-dir ./data/01_model_input/sequential_train_val/val --labels-dir ./data/03_reporting/sequential/predictions_labels_val
deps:
- path: ./data/01_model_input/sequential_train_val/val
hash: md5
md5: 2ca47638a4b0cc9ca7d4011376c54e45.dir
size: 631259421
nfiles: 12448
- path: ./data/02_models/yolo/best/weights/best.pt
hash: md5
md5: 56994899133e7926c522fc63f36dca49
size: 19228051
- path: predict_sequential.py
hash: md5
md5: 01105704d08d0fa6a9286d0dc6d167f2
size: 4500
outs:
- path: ./data/03_reporting/sequential/predictions_labels_val
hash: md5
md5: e5f9b64df619d2cd779c81d74ee158f5.dir
size: 109181
nfiles: 3438
optimize_sequential_val:
cmd:
- uv run python optimize_sequential.py --labels-dir ./data/03_reporting/sequential/predictions_labels_val
--min-frames 0 --output ./data/03_reporting/sequential/grid_search_val.tsv
--output-top ./data/03_reporting/sequential/grid_search_val_top20.tsv --top-n
20
deps:
- path: ./data/03_reporting/sequential/predictions_labels_val
hash: md5
md5: e5f9b64df619d2cd779c81d74ee158f5.dir
size: 109181
nfiles: 3438
- path: optimize_sequential.py
hash: md5
md5: 824da7d2cc55809d597c83cfc5cf1915
size: 8645
outs:
- path: ./data/03_reporting/sequential/grid_search_val.tsv
hash: md5
md5: 12a5736876d1e09dedada2c1c0351215
size: 1207
- path: ./data/03_reporting/sequential/grid_search_val_top20.tsv
hash: md5
md5: 96c8fe1f18da3f47f6dd2dd26b64fb86
size: 1018