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PaddlePaddle / PaddleDetection

Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
Apache License 2.0
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自己的数据训练表格检测。预训练模型用的是picodet_lcnet_x1_0_fgd_layout_table。检测的时候表格底部有时候会不准，原因是什么呢?要怎么优化呢? #8203

Closed rexzhengzhihong closed 10 months ago

rexzhengzhihong commented 1 year ago

问题确认 Search before asking

请提出你的问题 Please ask your question

问题:样本为25张。评估都是1。可是预测的结果有一些是不准的。比如表格的底部范围错了，怎么优化呢?如图

gdzc_v1-10 gdzc_v1-09

评估结果

python tools/eval.py \
> -c configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x1_0_layout_table.yml \
> -o weights=/home/DiskA/zncsPython/table_det/model/output_table/picodet_lcnet_x1_0_layout_table/best_model

W0508 09:32:15.859894 466581 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 8.6, Driver API Version: 11.6, Runtime API Version: 11.6
W0508 09:32:15.862742 466581 gpu_resources.cc:91] device: 0, cuDNN Version: 8.4.
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
[05/08 09:32:16] ppdet.data.source.coco INFO: Load [7 samples valid, 0 samples invalid] in file /home/DiskA/zncsPython/table_det/coco_data/annotations/instance_val.json.
[05/08 09:32:16] ppdet.utils.checkpoint INFO: Finish loading model weights: /home/DiskA/zncsPython/table_det/model/output_table/picodet_lcnet_x1_0_layout_table/best_model.pdparams
[05/08 09:32:18] ppdet.engine INFO: Eval iter: 0
[05/08 09:32:18] ppdet.metrics.metrics INFO: The bbox result is saved to bbox.json.
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
[05/08 09:32:18] ppdet.metrics.coco_utils INFO: Start evaluate...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=0.00s).
Accumulating evaluation results...
DONE (t=0.00s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 1.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 1.000
[05/08 09:32:18] ppdet.engine INFO: Total sample number: 7, average FPS: 4.0349411184269295

训练配置文件yml

_BASE_: [
  '../../../../runtime.yml',
  '../../_base_/picodet_esnet.yml',
  '../../_base_/optimizer_100e.yml',
  '../../_base_/picodet_640_reader.yml',
]

pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/LCNet_x1_0_pretrained.pdparams
save_dir: /home/DiskA/zncsPython/table_det/model/output
#pretrain_weights: https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_table.pdparams
#save_dir: /home/DiskA/zncsPython/table_det/model/output_table
#pretrain_weights: https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout.pdparams
#save_dir: /home/DiskA/zncsPython/table_det/model/output_layout
weights: output/picodet_lcnet_x1_0_layout/model_final

find_unused_parameters: True
use_ema: true
cycle_epoch: 10
snapshot_epoch: 1
epoch: 200

PicoDet:
  backbone: LCNet
  neck: CSPPAN
  head: PicoHead

LCNet:
  scale: 1.0
  feature_maps: [3, 4, 5]

metric: COCO
num_classes: 1

TrainDataset:
  !COCODataSet
    image_dir: train
    anno_path: annotations/instance_train.json
    dataset_dir: /home/DiskA/zncsPython/table_det/coco_data/
    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']

EvalDataset:
  !COCODataSet
    image_dir: val
    anno_path: annotations/instance_val.json
    dataset_dir: /home/DiskA/zncsPython/table_det/coco_data/

TestDataset:
  !ImageFolder
    anno_path: /home/DiskA/zncsPython/table_det/coco_data/annotations/instance_val.json

worker_num: 2
eval_height: &eval_height 800
eval_width: &eval_width 608
eval_size: &eval_size [*eval_height, *eval_width]

TrainReader:
  sample_transforms:
  - Decode: {}
  - RandomCrop: {}
  - RandomFlip: {prob: 0.5}
  - RandomDistort: {}
  batch_transforms:
  - BatchRandomResize: {target_size: [[768, 576], [800, 608], [832, 640]], random_size: True, random_interp: True, keep_ratio: False}
  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
  - Permute: {}
  batch_size: 24
  shuffle: true
  drop_last: true
  collate_batch: false

EvalReader:
  sample_transforms:
  - Decode: {}
  - Resize: {interp: 2, target_size: [800, 608], keep_ratio: False}
  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
  - Permute: {}
  batch_transforms:
  - PadBatch: {pad_to_stride: 32}
  batch_size: 8
  shuffle: false

TestReader:
  inputs_def:
    image_shape: [1, 3, 800, 608]
  sample_transforms:
  - Decode: {}
  - Resize: {interp: 2, target_size: [800, 608], keep_ratio: False}
  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
  - Permute: {}
  batch_transforms:
  - PadBatch: {pad_to_stride: 32}
  batch_size: 1
  shuffle: false

样本数据

{
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        {
            "height": 660,
            "width": 1520,
            "id": 1,
            "file_name": "gdzc_v1-04.jpg"
        },
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            "width": 1869,
            "id": 2,
            "file_name": "gdzc_v1-18.jpg"
        },
        {
            "height": 630,
            "width": 1517,
            "id": 3,
            "file_name": "gdzc_v1-05.jpg"
        },
        {
            "height": 613,
            "width": 1801,
            "id": 4,
            "file_name": "gdzc_v1-01.jpg"
        },
        {
            "height": 577,
            "width": 1514,
            "id": 5,
            "file_name": "gdzc_v1-08.jpg"
        },
        {
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            "id": 6,
            "file_name": "gdzc_v1-06.jpg"
        },
        {
            "height": 613,
            "width": 1801,
            "id": 7,
            "file_name": "gdzcljzj_v1_4.png"
        },
        {
            "height": 648,
            "width": 1801,
            "id": 8,
            "file_name": "gdzcljzj_v1_5.png"
        },
        {
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            "width": 1801,
            "id": 9,
            "file_name": "gdzc_v1-19.jpg"
        },
        {
            "height": 683,
            "width": 1801,
            "id": 10,
            "file_name": "gdzcljzj_v1_3.png"
        },
        {
            "height": 613,
            "width": 1801,
            "id": 11,
            "file_name": "gdzc_v1-13.jpg"
        },
        {
            "height": 494,
            "width": 1090,
            "id": 12,
            "file_name": "gdzc_v1-25.jpg"
        },
        {
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            "width": 1801,
            "id": 13,
            "file_name": "gdzc_v1-12.jpg"
        },
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            "id": 14,
            "file_name": "gdzc_v1-07.jpg"
        },
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            "file_name": "gdzc_v1-24.jpg"
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            "file_name": "gdzc_v1-20.jpg"
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            "file_name": "gdzc_v1-03.jpg"
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    ],
    "categories": [
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            "supercategory": "component",
            "id": 1,
            "name": "Table"
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rexzhengzhihong commented 10 months ago

没人回答吗

rexzhengzhihong commented 10 months ago

@thinkthinking

thinkthinking commented 10 months ago

@thinkthinkin

thinkthinking commented 10 months ago

@lyuwenyu