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jongkook90
/
yolov8_comicbook

TensorBoard
ONNX
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1
jongkook90 commited on
Commit
13ed643
•
1 Parent(s): dfae973

Fixed bug

Browse files

removed binary suspected files

Files changed (26) hide show
  1. README.txt +1 -0
  2. convert.py +113 -0
  3. train.py +14 -0
  4. train/F1_curve.png +0 -0
  5. train/PR_curve.png +0 -0
  6. train/P_curve.png +0 -0
  7. train/R_curve.png +0 -0
  8. train/args.yaml +97 -0
  9. train/confusion_matrix.png +0 -0
  10. train/confusion_matrix_normalized.png +0 -0
  11. weights/best.pt → train/events.out.tfevents.1687303014.jongkook90-desktop.37493.0 +2 -2
  12. train/labels.jpg +0 -0
  13. train/labels_correlogram.jpg +0 -0
  14. train/results.csv +4 -0
  15. train/results.png +0 -0
  16. train/val_batch0_labels.jpg +0 -0
  17. train/val_batch0_pred.jpg +0 -0
  18. train/val_batch1_labels.jpg +0 -0
  19. train/val_batch1_pred.jpg +0 -0
  20. train/val_batch2_labels.jpg +0 -0
  21. train/val_batch2_pred.jpg +0 -0
  22. train/weights/best.onnx +3 -0
  23. train/weights/best.pt +3 -0
  24. {weights → train/weights}/last.pt +2 -2
  25. train_log.txt +45 -24
  26. weights/README.md +0 -3
README.txt ADDED
@@ -0,0 +1 @@
 
 
1
+ https://github.com/ultralytics/ultralytics/commit/9d1e5567de48453f168013ff1032810bd95d39fe
convert.py ADDED
@@ -0,0 +1,113 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import xml.etree.ElementTree as ET
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+ import os
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+ from PIL import Image
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+ from IPython.display import display
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+ import json
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+ import pandas as pd
7
+ import yaml
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+
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+
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+ ## Generate label file
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+
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+ class Manga():
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+ def __init__(self, path, image_path, output_path):
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+
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+ manga_name = path.split("/")[-1][3:-4]
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+ print(manga_name)
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+ self.image_path = lambda page: f"{image_path}/{manga_name}/{('000'+page)[-3:]}.jpg"
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+ self.output_path = lambda obj_id, ext: f"{output_path}/{obj_id}.{ext}"
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+ self.manga_name = manga_name
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+
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+ tree = ET.parse(path)
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+ self.root = tree.getroot()
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+ self.characters = [x.get('id') for x in self.root.find("characters")]
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+ self.pages = {
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+ page.get('index') :
26
+ {
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+ "frame": {frame.attrib["id"]: frame.attrib for frame in page.findall("frame")},
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+ "face": {face.attrib["id"]: face.attrib for face in page.findall("face")},
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+ "body": {body.attrib["id"]: body.attrib for body in page.findall("body")},
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+ "text": {text.attrib["id"]: {**text.attrib, "text":text.text} for text in page.findall("text")},
31
+ }
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+ for page in self.root.find("pages")
33
+ }
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+ self.pages_size = {
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+ page.get('index') :
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+ {
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+ "page_size": (int(page.get("width")), int(page.get("height")))
38
+ }
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+ for page in self.root.find("pages")
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+ }
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+ for page_id, page, elems, v in self._loop_over_elements():
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+ self._parse_int(v)
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+ v["position"] = self._calc_location(v)
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+ v["size"] = self._calc_wh(v)
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+ v["page_id"] = page_id
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+
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+
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+ def _loop_over_elements(self):
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+ for page_id, page in self.pages.items():
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+ for elems in page.values():
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+ for v in elems.values():
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+ yield (page_id, page, elems, v)
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+ @staticmethod
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+ def _parse_int(obj):
55
+ for k in ["xmin", "xmax", "ymin", "ymax"]:
56
+ obj[k] = int(obj[k])
57
+ @staticmethod
58
+ def _calc_location(obj):
59
+ return (0.5 * (obj["xmin"]+obj["xmax"]), 0.5 * (obj["ymin"]+obj["ymax"]))
60
+ @staticmethod
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+ def _calc_wh(obj):
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+ return (obj["xmax"]-obj["xmin"]) , (obj["ymax"]-obj["ymin"])
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+
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+ def _get_image(self, obj):
65
+ image_path = self.image_path(obj["page_id"])
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+ image = Image.open(image_path)
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+ trimmed_image = image.crop((obj["xmin"],obj["ymin"], obj["xmax"], obj["ymax"]))
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+ return trimmed_image
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+
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+
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+ import json
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+ path = './annotations_ko'
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+ for xml_file in os.listdir(path):
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+ if xml_file.endswith('.xml'):
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+ xml_path = f"{path}/{xml_file}"
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+ print(xml_path)
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+ m = Manga(xml_path, './images/', "./json_features")
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+
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+ parts = {0:"frame", 1:"face", 2:"body", 3:"text"}
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+
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+ for page_id in m.pages.keys():
82
+ lines = []
83
+ orig_path = m.image_path(page_id)
84
+ new_path = "./yaml_yolo/labels/" + orig_path.replace("./images//","").replace("/","_").replace(".jpg",".txt")
85
+ for part_id, part in parts.items():
86
+ page = m.pages[page_id]
87
+ w, h = m.pages_size[page_id]["page_size"]
88
+ line = [(part_id, v["position"][0]/w, v["position"][1]/h, v["size"][0]/w, v["size"][1]/h) for k, v in page[part].items()]
89
+ if len(line)>0:
90
+ labels = "\n".join([" ".join(str(y) for y in x) for x in line])
91
+ lines.append(labels)
92
+ if len(lines)>0:
93
+ lines = "\n".join(lines)
94
+ print(orig_path, new_path)
95
+ print(lines)
96
+ with open(new_path, "wt") as f:
97
+ f.write(lines)
98
+
99
+
100
+
101
+ ## Ganerate yaml file
102
+
103
+ cfg = {
104
+ "path": "../datasets/manga109",
105
+ "train": "images/train",
106
+ "val": "images/train",
107
+ "test": None,
108
+ "names":{0:"frame", 1:"face", 2:"body", 3:"text"},
109
+ }
110
+
111
+ with open('./yaml_yolo/manga109.yaml', 'w') as f:
112
+ yaml.dump(cfg, f)
113
+
train.py ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ from ultralytics import YOLO
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+
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+ # Load a model
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+ # model = YOLO("yolov8n.yaml") # build a new model from scratch
5
+ # model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
6
+ model = YOLO("yolov8m.yaml") # build a new model from scratch
7
+ model = YOLO("yolov8m.pt") # load a pretrained model (recommended for training)
8
+
9
+ # Use the model
10
+ # model.train(data="coco128.yaml", epochs=3) # train the model
11
+ model.train(data="./datasets/manga109/manga109.yaml", epochs=3) # train the model
12
+ metrics = model.val() # evaluate model performance on the validation set
13
+ # results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
14
+ path = model.export(format="onnx") # export the model to ONNX format
train/F1_curve.png ADDED
train/PR_curve.png ADDED
train/P_curve.png ADDED
train/R_curve.png ADDED
train/args.yaml ADDED
@@ -0,0 +1,97 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ task: detect
2
+ mode: train
3
+ model: yolov8m.pt
4
+ data: ./datasets/manga109/manga109.yaml
5
+ epochs: 3
6
+ patience: 50
7
+ batch: 16
8
+ imgsz: 640
9
+ save: true
10
+ save_period: -1
11
+ cache: false
12
+ device: null
13
+ workers: 8
14
+ project: null
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+ name: null
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+ exist_ok: false
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+ pretrained: true
18
+ optimizer: auto
19
+ verbose: true
20
+ seed: 0
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+ deterministic: true
22
+ single_cls: false
23
+ rect: false
24
+ cos_lr: false
25
+ close_mosaic: 0
26
+ resume: false
27
+ amp: true
28
+ fraction: 1.0
29
+ profile: false
30
+ overlap_mask: true
31
+ mask_ratio: 4
32
+ dropout: 0.0
33
+ val: true
34
+ split: val
35
+ save_json: false
36
+ save_hybrid: false
37
+ conf: null
38
+ iou: 0.7
39
+ max_det: 300
40
+ half: false
41
+ dnn: false
42
+ plots: true
43
+ source: null
44
+ show: false
45
+ save_txt: false
46
+ save_conf: false
47
+ save_crop: false
48
+ show_labels: true
49
+ show_conf: true
50
+ vid_stride: 1
51
+ line_width: null
52
+ visualize: false
53
+ augment: false
54
+ agnostic_nms: false
55
+ classes: null
56
+ retina_masks: false
57
+ boxes: true
58
+ format: torchscript
59
+ keras: false
60
+ optimize: false
61
+ int8: false
62
+ dynamic: false
63
+ simplify: false
64
+ opset: null
65
+ workspace: 4
66
+ nms: false
67
+ lr0: 0.01
68
+ lrf: 0.01
69
+ momentum: 0.937
70
+ weight_decay: 0.0005
71
+ warmup_epochs: 3.0
72
+ warmup_momentum: 0.8
73
+ warmup_bias_lr: 0.1
74
+ box: 7.5
75
+ cls: 0.5
76
+ dfl: 1.5
77
+ pose: 12.0
78
+ kobj: 1.0
79
+ label_smoothing: 0.0
80
+ nbs: 64
81
+ hsv_h: 0.015
82
+ hsv_s: 0.7
83
+ hsv_v: 0.4
84
+ degrees: 0.0
85
+ translate: 0.1
86
+ scale: 0.5
87
+ shear: 0.0
88
+ perspective: 0.0
89
+ flipud: 0.0
90
+ fliplr: 0.5
91
+ mosaic: 1.0
92
+ mixup: 0.0
93
+ copy_paste: 0.0
94
+ cfg: null
95
+ v5loader: false
96
+ tracker: botsort.yaml
97
+ save_dir: runs/detect/train5
train/confusion_matrix.png ADDED
train/confusion_matrix_normalized.png ADDED
weights/best.pt → train/events.out.tfevents.1687303014.jongkook90-desktop.37493.0 RENAMED
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+ oid sha256:8d43759de4ce7e130ce2d6ba60e04ee594cac8d9af20f1ff36080e303308ad64
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+ size 250830
train/labels.jpg ADDED
train/labels_correlogram.jpg ADDED
train/results.csv ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
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+ epoch, train/box_loss, train/cls_loss, train/dfl_loss, metrics/precision(B), metrics/recall(B), metrics/mAP50(B), metrics/mAP50-95(B), val/box_loss, val/cls_loss, val/dfl_loss, lr/pg0, lr/pg1, lr/pg2
2
+ 0, 0.96814, 0.85244, 1.1087, 0.86958, 0.8146, 0.88546, 0.63524, 0.91621, 0.63611, 1.0506, 0.00041588, 0.00041588, 0.00041588
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+ 1, 0.87945, 0.62993, 1.0581, 0.89082, 0.84483, 0.90655, 0.66293, 0.88578, 0.56822, 1.0374, 0.00055781, 0.00055781, 0.00055781
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+ 2, 0.83749, 0.57467, 1.0371, 0.90332, 0.86365, 0.9226, 0.68896, 0.82432, 0.51203, 1.0026, 0.00042473, 0.00042473, 0.00042473
train/results.png ADDED
train/val_batch0_labels.jpg ADDED
train/val_batch0_pred.jpg ADDED
train/val_batch1_labels.jpg ADDED
train/val_batch1_pred.jpg ADDED
train/val_batch2_labels.jpg ADDED
train/val_batch2_pred.jpg ADDED
train/weights/best.onnx ADDED
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{weights → train/weights}/last.pt RENAMED
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train_log.txt CHANGED
@@ -1,25 +1,46 @@
1
- Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2
- 1/3 2.56G 1.26 1.267 1.016 122 640: 100%|██████████| 533/533 [01:08<00:00, 7.75it/s]^
3
- Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 267/267 [01:07<00:00, 3.93it/s]
4
- all 8519 114053 0.97 0.282 0.306 0.216
5
-
6
- Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7
- 2/3 3.36G 1.001 0.7182 0.9132 154 640: 100%|██████████| 533/533 [00:59<00:00, 8.92it/s]
8
- Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 267/267 [01:07<00:00, 3.93it/s] all 8519 114053 0.969 0.288 0.31 0.225
9
-
10
- Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
11
- 3/3 3.37G 0.9513 0.6483 0.9023 127 640: 100%|██████████| 533/533 [00:59<00:00, 8.93it/s]
12
- Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 267/267 [01:21<00:00, 3.28it/s]
13
- all 8519 114053 0.973 0.294 0.323 0.233
14
- 3 epochs completed in 0.116 hours.
15
- Optimizer stripped from runs/detect/train3/weights/last.pt, 6.2MB Optimizer stripped from runs/detect/train3/weights/best.pt, 6.2MB
16
-
17
- Validating runs/detect/train3/weights/best.pt...
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
18
  Ultralytics YOLOv8.0.120 🚀 Python-3.9.7 torch-2.0.0+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24265MiB)
19
- Model summary (fused): 168 layers, 3006428 parameters, 0 gradients
20
- Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 267/267 [01:19<00:00, 3.37it/s]
21
- all 8519 114053 0.973 0.294 0.323 0.233
22
- frame 8519 36 1 0 0.000451 0.000198
23
- body 8519 403 1 0 0.0344 0.0144
24
- text 8519 113614 0.919 0.882 0.933 0.686
25
- Speed: 0.1ms preprocess, 0.4ms inference, 0.0ms loss, 1.0ms postprocess per image
 
 
 
 
 
 
1
+ Logging results to runs/detect/train5
2
+ Starting training for 3 epochs...
3
+
4
+ Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5
+ 1/3 8.35G 0.9681 0.8524 1.109 449 640: 100%|██████████| 533/533 [03:36<00:00, 2.47it/s]
6
+ Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 267/267 [01:08<00:00, 3.89it/s]
7
+ all 8519 401038 0.87 0.815 0.885 0.635
8
+
9
+ Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10
+ 2/3 9.16G 0.8795 0.6299 1.058 600 640: 100%|██████████| 533/533 [01:36<00:00, 5.54it/s]
11
+ Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 267/267 [01:08<00:00, 3.90it/s]
12
+ all 8519 401038 0.891 0.845 0.907 0.663
13
+
14
+ Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
15
+ 3/3 10.2G 0.8375 0.5747 1.037 590 640: 100%|██████████| 533/533 [01:34<00:00, 5.63it/s]
16
+ Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 267/267 [02:00<00:00, 2.21it/s]Bn
17
+ all 8519 401038 0.903 0.864 0.923 0.689
18
+
19
+ 3 epochs completed in 0.191 hours.
20
+ Optimizer stripped from runs/detect/train5/weights/last.pt, 52.0MB
21
+ Optimizer stripped from runs/detect/train5/weights/best.pt, 52.0MB
22
+
23
+ Validating runs/detect/train5/weights/best.pt...
24
+ Ultralytics YOLOv8.0.120 🚀 Python-3.9.7 torch-2.0.0+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24265MiB)
25
+ Model summary (fused): 218 layers, 25842076 parameters, 0 gradients
26
+ Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 267/267 [01:56<00:00, 2.29it/s]
27
+ all 8519 401038 0.903 0.864 0.923 0.689
28
+ frame 8519 82124 0.944 0.972 0.989 0.942
29
+ face 8519 87174 0.903 0.77 0.873 0.467
30
+ body 8519 118126 0.852 0.811 0.885 0.642
31
+ text 8519 113614 0.915 0.9 0.944 0.704
32
+ Speed: 0.1ms preprocess, 2.1ms inference, 0.0ms loss, 0.8ms postprocess per image
33
+ Results saved to runs/detect/train5
34
  Ultralytics YOLOv8.0.120 🚀 Python-3.9.7 torch-2.0.0+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24265MiB)
35
+ Model summary (fused): 218 layers, 25842076 parameters, 0 gradients
36
+ val: Scanning /media/jongkook90/Morpho DB/dataset-manga109-ko/yolov8-frame/datasets/manga109/labels/train.cache... 8155 images, 364 backgrounds, 0 corrupt: 100%|██████████| 8519/8519 [00:00<?, ?it/s]
37
+ Class Images Instances Box(P R mAP50 mAP50-95): 0%| | 2/533 [00:08<43:38, 4.93s/it]WARNING ⚠️ NMS time limit 1.300s exceeded
38
+ Class Images Instances Box(P R mAP50 mAP50-95): 1%| | 3/533 [00:14<47:58, 5.43s/it]WARNING ⚠️ NMS time limit 1.300s exceeded
39
+ Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 533/533 [02:06<00:00, 4.22it/s]
40
+ all 8519 401038 0.904 0.861 0.92 0.688
41
+ frame 8519 82124 0.944 0.969 0.986 0.941
42
+ face 8519 87174 0.903 0.769 0.87 0.467
43
+ body 8519 118126 0.852 0.809 0.882 0.641
44
+ text 8519 113614 0.916 0.897 0.941 0.704
45
+ Speed: 0.1ms preprocess, 4.1ms inference, 0.0ms loss, 1.5ms postprocess per image
46
+ Results saved to runs/detect/val3
weights/README.md DELETED
@@ -1,3 +0,0 @@
1
- yolov8 3epoch training
2
-
3
- TODO: data augmentation, train result check