malaysia-ai2020
commited on
Commit
•
7390b54
1
Parent(s):
f1ec5da
Upload folder using huggingface_hub
Browse files- args.yaml +106 -0
- labels.jpg +0 -0
- labels_correlogram.jpg +0 -0
- results.csv +68 -0
- train_batch0.jpg +0 -0
- train_batch1.jpg +0 -0
- train_batch2.jpg +0 -0
- weights/best.onnx +3 -0
- weights/best.pt +3 -0
- weights/last.pt +3 -0
args.yaml
ADDED
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task: detect
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mode: train
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model: /home/ubuntu/YoloV8/runs/detect/train/weights/last.pt
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data: /home/ubuntu/YoloV8/ultralytics/ultralytics/datasets/doclaynet.yaml
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epochs: 500
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time: null
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patience: 100
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batch: 42
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imgsz: 1024
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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: 12
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project: null
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name: train
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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
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profile: false
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freeze: null
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multi_scale: false
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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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vid_stride: 1
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stream_buffer: false
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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
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embed: null
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show: false
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save_frames: 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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show_boxes: true
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line_width: null
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format: torchscript
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keras: false
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optimize: false
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int8: false
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dynamic: false
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simplify: false
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opset: null
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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: 0.0
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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
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bgr: 0.0
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mosaic: 1.0
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mixup: 0.0
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copy_paste: 0.0
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auto_augment: randaugment
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erasing: 0.4
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crop_fraction: 1.0
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cfg: null
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tracker: botsort.yaml
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save_dir: runs/detect/train
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labels.jpg
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labels_correlogram.jpg
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results.csv
ADDED
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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
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62 |
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61, 0.38276, 0.39578, 0.83896, 0.90836, 0.85966, 0.92373, 0.74967, 0.70578, 0.46599, 0.8453, 0.0088318, 0.0088318, 0.0088318
|
63 |
+
62, 0.38269, 0.39649, 0.83938, 0.9085, 0.85907, 0.92361, 0.74953, 0.70581, 0.46619, 0.84532, 0.008812, 0.008812, 0.008812
|
64 |
+
63, 0.38213, 0.39687, 0.83884, 0.90723, 0.86207, 0.92373, 0.75034, 0.70409, 0.4657, 0.84505, 0.0087922, 0.0087922, 0.0087922
|
65 |
+
64, 0.3799, 0.39235, 0.83797, 0.9071, 0.86234, 0.92399, 0.75097, 0.703, 0.46498, 0.8448, 0.0087724, 0.0087724, 0.0087724
|
66 |
+
65, 0.37886, 0.39249, 0.83704, 0.90598, 0.86258, 0.92425, 0.75145, 0.70244, 0.46459, 0.84462, 0.0087526, 0.0087526, 0.0087526
|
67 |
+
66, 0.3785, 0.39252, 0.83722, 0.90851, 0.86066, 0.92445, 0.75153, 0.70202, 0.46428, 0.84433, 0.0087328, 0.0087328, 0.0087328
|
68 |
+
67, 0.37801, 0.39168, 0.8376, 0.90505, 0.86434, 0.92456, 0.75231, 0.70045, 0.46334, 0.84381, 0.008713, 0.008713, 0.008713
|
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