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ENOT-AutoDL
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yolov8s_visdrone

Object Detection
TensorBoard
ultralytics
yolov8
ENOT-AutoDL
yolo
vision
Eval Results
Model card Files Metrics Community
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  1. .gitattributes +40 -0
  2. README.md +63 -0
  3. baseline_enot/args.yaml +97 -0
  4. baseline_enot/events.out.tfevents.1697772240.user-MS-7D67.210222.0 +3 -0
  5. baseline_enot/results.csv +101 -0
  6. baseline_enot/weights/best.pt +3 -0
  7. baseline_enot/weights/last.pt +3 -0
  8. baseline_enot_nano/args.yaml +98 -0
  9. baseline_enot_nano/results.csv +101 -0
  10. baseline_enot_nano/weights/best.pt +3 -0
  11. baseline_enot_nano/weights/last.pt +3 -0
  12. baseline_ultralytics/args.yaml +97 -0
  13. baseline_ultralytics/events.out.tfevents.1699532367.cube06.2218493.0 +3 -0
  14. baseline_ultralytics/results.csv +101 -0
  15. baseline_ultralytics/weights/best.pt +3 -0
  16. baseline_ultralytics/weights/last.pt +3 -0
  17. enot_neural_architecture_selection_x2/args.yaml +95 -0
  18. enot_neural_architecture_selection_x2/events.out.tfevents.1700031834.cube06.938038.0 +3 -0
  19. enot_neural_architecture_selection_x2/results.csv +101 -0
  20. enot_neural_architecture_selection_x2/weights/best.pt +3 -0
  21. enot_neural_architecture_selection_x2/weights/last.pt +3 -0
  22. enot_neural_architecture_selection_x3/best_hyperparameters.yaml +22 -0
  23. enot_neural_architecture_selection_x3/weights/best.pt +3 -0
  24. enot_neural_architecture_selection_x3/weights/last.pt +3 -0
  25. measure_macs.py +22 -0
  26. requirements.txt +2 -0
  27. validate.py +14 -0
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README.md ADDED
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1
+ ---
2
+ license: apache-2.0
3
+ datasets:
4
+ - visdrone
5
+ model-index:
6
+ - name: ENOT-AutoDL/yolov8s_visdrone
7
+ results:
8
+ - task:
9
+ type: object-detection
10
+ metrics:
11
+ - type: precision
12
+ value: 49,4
13
+ name: mAP50(baseline)
14
+ - type: precision
15
+ value: 48,4
16
+ name: mAP50(GMACs x2)
17
+ - type: precision
18
+ value: 46,0
19
+ name: mAP50(GMACs x3)
20
+ library_name: ultralytics
21
+ pipeline_tag: object-detection
22
+ tags:
23
+ - yolov8
24
+ - ENOT-AutoDL
25
+ - yolo
26
+ - vision
27
+ - ultralytics
28
+ - object-detection
29
+ ---
30
+ # ENOT-AutoDL YOLOv8 optimization on VisDrone dataset
31
+
32
+ This repository contains models accelerated with [ENOT-AutoDL](https://pypi.org/project/enot-autodl/) framework.
33
+ We trained yolov8s on VisDrone dataset and used it as our baseline.
34
+ Also we provide simple python script to measure flops and metrics.
35
+
36
+ ## YOLOv8 Small
37
+
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+ | Model | GMACs | Image Size | mAP50 | mAP50-95 |
39
+ |---------------------------|:-----------:|:-----------:|:-----------:|:-----------:|
40
+ | **[YOLOv8 Ultralytics Baseline](https://docs.ultralytics.com/datasets/detect/visdrone/#dataset-yaml)** | 14,28 | 640 | 40,2 | 24,2 |
41
+ | **YOLOv8n Enot Baseline** | 8,57 | 928 | 42,9 | 26,0 |
42
+ | **YOLOv8s Enot Baseline** | 30,03 | 928 | 49,4 | 30,6 |
43
+ | **YOLOv8s (x2)** | 15,01 (x2) | 928 | 48,3 (-1,1) | 29,8 (-0,8) |
44
+ | **YOLOv8s (x3)** | 10,01 (x3) | 928 | 46,0 (-3,4) | 28,3 (-2,3) |
45
+
46
+ # Validation
47
+
48
+ To validate results, follow this steps:
49
+
50
+ 1. Install all required packages:
51
+ ```bash
52
+ pip install -r requrements.txt
53
+ ```
54
+
55
+ 2. Use validation script:
56
+ ```bash
57
+ python validate.py enot_neural_architecture_selection_x2/weights/best.pt --imgsz 928
58
+ ```
59
+
60
+ 3. Use measure_macs script:
61
+ ```bash
62
+ python measure_macs.py enot_neural_architecture_selection_x2/weights/best.pt --imgsz 928
63
+ ```
baseline_enot/args.yaml ADDED
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+ task: detect
2
+ mode: train
3
+ model: yolov8s.pt
4
+ data: VisDrone.yaml
5
+ epochs: 100
6
+ patience: 50
7
+ batch: 16
8
+ imgsz: 928
9
+ save: true
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+ save_period: -1
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+ cache: false
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+ device: null
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+ workers: 16
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+ project: null
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+ name: visdrone_yolo8s
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+ exist_ok: false
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+ pretrained: true
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+ optimizer: auto
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+ verbose: true
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+ seed: 0
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+ deterministic: true
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+ single_cls: false
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+ rect: false
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+ cos_lr: false
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+ close_mosaic: 10
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+ resume: false
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+ amp: true
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+ fraction: 1.0
29
+ profile: false
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+ freeze: null
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+ overlap_mask: true
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+ mask_ratio: 4
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+ dropout: 0.0
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+ val: true
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+ split: val
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+ save_json: false
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+ save_hybrid: false
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+ conf: null
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+ iou: 0.7
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+ max_det: 300
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+ half: false
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+ dnn: false
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+ plots: true
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+ source: null
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+ show: false
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+ save_txt: false
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+ save_conf: false
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+ save_crop: false
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+ show_labels: true
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+ show_conf: true
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+ vid_stride: 1
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+ stream_buffer: false
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+ line_width: null
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+ visualize: false
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+ augment: false
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+ agnostic_nms: false
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+ classes: null
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+ retina_masks: false
59
+ boxes: true
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+ format: torchscript
61
+ keras: false
62
+ optimize: false
63
+ int8: false
64
+ dynamic: false
65
+ simplify: false
66
+ opset: null
67
+ workspace: 4
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+ nms: false
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+ lr0: 0.01
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+ lrf: 0.01
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+ momentum: 0.937
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+ weight_decay: 0.0005
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+ warmup_epochs: 3.0
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+ warmup_momentum: 0.8
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+ warmup_bias_lr: 0.1
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+ box: 7.5
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+ cls: 0.5
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+ dfl: 1.5
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+ pose: 12.0
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+ kobj: 1.0
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+ label_smoothing: 0.0
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+ nbs: 64
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+ hsv_h: 0.015
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+ hsv_s: 0.7
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+ hsv_v: 0.4
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+ degrees: 0.0
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+ translate: 0.1
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+ scale: 0.5
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+ shear: 0.0
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+ perspective: 0.0
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+ flipud: 0.0
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+ fliplr: 0.5
93
+ mosaic: 1.0
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+ mixup: 0.0
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+ copy_paste: 0.0
96
+ cfg: null
97
+ tracker: botsort.yaml
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+ 16, 1.2754, 0.91294, 0.93883, 0.54154, 0.43035, 0.44883, 0.2728, 1.2506, 0.91463, 0.93892, 0.008614, 0.008614, 0.008614
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+ 17, 1.2695, 0.90378, 0.93665, 0.52216, 0.43034, 0.44458, 0.27034, 1.2613, 0.92401, 0.9424, 0.008515, 0.008515, 0.008515
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+ 19, 1.2573, 0.88592, 0.93319, 0.55241, 0.4299, 0.45315, 0.27676, 1.2436, 0.90916, 0.93595, 0.008317, 0.008317, 0.008317
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+ 20, 1.2553, 0.87851, 0.93287, 0.54586, 0.43819, 0.45975, 0.28177, 1.2307, 0.89486, 0.93085, 0.008218, 0.008218, 0.008218
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+ 37, 1.1984, 0.79751, 0.91607, 0.57059, 0.45467, 0.47666, 0.2936, 1.2217, 0.86785, 0.92748, 0.006535, 0.006535, 0.006535
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enot_neural_architecture_selection_x2/weights/best.pt ADDED
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enot_neural_architecture_selection_x3/best_hyperparameters.yaml ADDED
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+ lr0: 0.01178
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+ lrf: 0.009
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+ momentum: 0.92873
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+ weight_decay: 0.00041
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+ warmup_epochs: 3.87259
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+ warmup_momentum: 0.70941
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+ box: 11.1414
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+ cls: 0.59602
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+ dfl: 1.17484
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+ perspective: 0.0
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+ copy_paste: 0.0
enot_neural_architecture_selection_x3/weights/best.pt ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ size 7559662
enot_neural_architecture_selection_x3/weights/last.pt ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ size 7561326
measure_macs.py ADDED
@@ -0,0 +1,22 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import argparse
2
+
3
+ import torch
4
+ from fvcore.nn import FlopCountAnalysis
5
+ from ultralytics import YOLO
6
+
7
+
8
+ if __name__ == '__main__':
9
+ parser = argparse.ArgumentParser()
10
+ parser.add_argument('model', type=str, help='Model path for validation.')
11
+ parser.add_argument('--imgsz', default=928, type=int, help='Image size to validate.')
12
+ args = parser.parse_args()
13
+
14
+ model = torch.load(args.model, map_location='cpu')['model'].float()
15
+ fca = FlopCountAnalysis(
16
+ model=model.eval(),
17
+ inputs=torch.rand(1, 3, args.imgsz, args.imgsz),
18
+ )
19
+ fca.unsupported_ops_warnings(False)
20
+ fca.uncalled_modules_warnings(False)
21
+
22
+ print(f"{fca.total() * 1e-9:.2f} GMACS ")
requirements.txt ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ ultralytics
2
+ fvcore
validate.py ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import argparse
2
+
3
+ import torch
4
+ from ultralytics import YOLO
5
+
6
+
7
+ if __name__ == '__main__':
8
+ parser = argparse.ArgumentParser()
9
+ parser.add_argument('model', type=str, help='Model path for validation.')
10
+ parser.add_argument('--imgsz', default=928, type=int, help='Image size to validate.')
11
+ args = parser.parse_args()
12
+
13
+ model = YOLO(args.model, task='detect')
14
+ model.val(data="VisDrone.yaml", imgsz=args.imgsz, batch=1)