2024-04-22 简化
    2023-10-24 initial detection

update information

• deep_training

install

• pip install -U -r requirements.txt
• 如果无法安装， 可以切换官方源 pip install -i https://pypi.org/simple -U -r requirements.txt

weigtht select one is suitable for you

支持且不限于以下权重

• detr-resnet-50
• detr-resnet-50 no_timm
• detr-resnet-101
• yolos-base
• yolos-small
• yolos-tiny
• table-transformer-detection

data sample

• open_data https://github.com/ssbuild/open_data
• coco https://cocodataset.org/#download

单条数据示例 path must bbox must category_id must

{"path": "/data/cv/data/coco/train2017/000000000009.jpg", "labels": {"image_id": 9, "annotations": [{"segmentation": [[500.49, 473.53, 599.73, 419.6, 612.67, 375.37, 608.36, 354.88, 528.54, 269.66, 457.35, 201.71, 420.67, 187.69, 389.39, 192.0, 19.42, 360.27, 1.08, 389.39, 2.16, 427.15, 20.49, 473.53]], "area": 120057.13925, "iscrowd": 0, "image_id": 9, "bbox": [1.08, 187.69, 611.59, 285.84], "category_id": 51, "id": 1038967}, {"segmentation": [[357.03, 69.03, 311.73, 15.1, 550.11, 4.31, 631.01, 62.56, 629.93, 88.45, 595.42, 185.53, 513.44, 230.83, 488.63, 232.99, 437.93, 190.92, 429.3, 189.84, 434.7, 148.85, 410.97, 121.89, 359.19, 74.43, 358.11, 65.8]], "area": 44434.751099999994, "iscrowd": 0, "image_id": 9, "bbox": [311.73, 4.31, 319.28, 228.68], "category_id": 51, "id": 1039564}, {"segmentation": [[249.6, 348.99, 267.67, 311.72, 291.39, 294.78, 304.94, 294.78, 326.4, 283.48, 345.6, 273.32, 368.19, 269.93, 385.13, 268.8, 388.52, 257.51, 393.04, 250.73, 407.72, 240.56, 425.79, 230.4, 441.6, 229.27, 447.25, 237.18, 447.25, 256.38, 456.28, 254.12, 475.48, 263.15, 486.78, 271.06, 495.81, 264.28, 498.07, 257.51, 500.33, 255.25, 507.11, 259.76, 513.88, 266.54, 513.88, 273.32, 513.88, 276.71, 526.31, 276.71, 526.31, 286.87, 519.53, 291.39, 519.53, 297.04, 524.05, 306.07, 525.18, 315.11, 529.69, 329.79, 529.69, 337.69, 530.82, 348.99, 536.47, 339.95, 545.51, 350.12, 555.67, 360.28, 557.93, 380.61, 561.32, 394.16, 565.84, 413.36, 522.92, 441.6, 469.84, 468.71, 455.15, 474.35, 307.2, 474.35, 316.24, 464.19, 330.92, 438.21, 325.27, 399.81, 310.59, 378.35, 301.55, 371.58, 252.99, 350.12]], "area": 49577.94434999999, "iscrowd": 0, "image_id": 9, "bbox": [249.6, 229.27, 316.24, 245.08], "category_id": 56, "id": 1058555}, {"segmentation": [[434.48, 152.33, 433.51, 184.93, 425.44, 189.45, 376.7, 195.58, 266.94, 248.53, 179.78, 290.17, 51.62, 346.66, 16.43, 366.68, 1.9, 388.63, 0.0, 377.33, 0.0, 357.64, 0.0, 294.04, 22.56, 294.37, 56.14, 300.82, 83.58, 300.82, 109.08, 289.2, 175.26, 263.38, 216.9, 243.36, 326.34, 197.52, 387.03, 172.34, 381.54, 162.33, 380.89, 147.16, 380.89, 140.06, 370.89, 102.29, 330.86, 61.94, 318.91, 48.38, 298.57, 47.41, 287.28, 37.73, 259.51, 33.85, 240.14, 32.56, 240.14, 28.36, 247.57, 24.17, 271.46, 15.13, 282.11, 13.51, 296.96, 18.68, 336.34, 55.48, 391.55, 106.81, 432.87, 147.16], [62.46, 97.21, 130.25, 69.77, 161.25, 59.12, 183.52, 52.02, 180.94, 59.12, 170.93, 78.17, 170.28, 90.76, 157.05, 95.92, 130.25, 120.78, 119.92, 129.49, 102.17, 115.29, 64.72, 119.81, 0.0, 137.89, 0.0, 120.13, 0.0, 117.87]], "area": 24292.781700000007, "iscrowd": 0, "image_id": 9, "bbox": [0.0, 13.51, 434.48, 375.12], "category_id": 51, "id": 1534147}, {"segmentation": [[376.2, 61.55, 391.86, 46.35, 424.57, 40.36, 441.62, 43.59, 448.07, 50.04, 451.75, 63.86, 448.07, 68.93, 439.31, 70.31, 425.49, 73.53, 412.59, 75.38, 402.92, 84.13, 387.71, 86.89, 380.8, 70.77]], "area": 2239.2924, "iscrowd": 0, "image_id": 9, "bbox": [376.2, 40.36, 75.55, 46.53], "category_id": 55, "id": 1913551}, {"segmentation": [[473.92, 85.64, 469.58, 83.47, 465.78, 78.04, 466.87, 72.08, 472.84, 59.59, 478.26, 47.11, 496.71, 38.97, 514.62, 40.6, 521.13, 49.28, 523.85, 55.25, 520.05, 63.94, 501.06, 72.62, 482.6, 82.93]], "area": 1658.8913000000007, "iscrowd": 0, "image_id": 9, "bbox": [465.78, 38.97, 58.07, 46.67], "category_id": 55, "id": 1913746}, {"segmentation": [[385.7, 85.85, 407.12, 80.58, 419.31, 79.26, 426.56, 77.94, 435.45, 74.65, 442.7, 73.66, 449.95, 73.99, 456.87, 77.94, 463.46, 83.87, 467.74, 92.77, 469.39, 104.63, 469.72, 117.15, 469.39, 135.27, 468.73, 141.86, 466.09, 144.17, 449.29, 141.53, 437.1, 136.92, 430.18, 129.67]], "area": 3609.3030499999995, "iscrowd": 0, "image_id": 9, "bbox": [385.7, 73.66, 84.02, 70.51], "category_id": 55, "id": 1913856}, {"segmentation": [[458.81, 24.94, 437.61, 4.99, 391.48, 2.49, 364.05, 56.1, 377.77, 73.56, 377.77, 56.1, 392.73, 41.14, 403.95, 41.14, 420.16, 39.9, 435.12, 42.39, 442.6, 46.13, 455.06, 31.17]], "area": 2975.276, "iscrowd": 0, "image_id": 9, "bbox": [364.05, 2.49, 94.76, 71.07], "category_id": 55, "id": 1914001}]}}

infer

# infer_finetuning.py 推理微调模型
# infer_lora_finetuning.py 推理微调模型
 python infer_finetuning.py

training

    # 制作数据
    cd scripts
    bash train_full.sh -m dataset 

    
    注: num_process_worker 为多进程制作数据 ， 如果数据量较大 ， 适当调大至cpu数量
    dataHelper.make_dataset_with_args(data_args.train_file,mixed_data=False, shuffle=True,mode='train',num_process_worker=0)
    
    # 全参数训练 
        bash train_full.sh -m train

训练参数

训练参数

友情链接

• pytorch-task-example
• tf-task-example
• chatmoss_finetuning
• chatglm_finetuning
• chatglm2_finetuning
• chatglm3_finetuning
• t5_finetuning
• llm_finetuning
• llm_rlhf
• chatglm_rlhf
• t5_rlhf
• rwkv_finetuning
• baichuan_finetuning

纯粹而干净的代码

参考

https://arxiv.org/abs/2005.12872

https://github.com/facebookresearch/detr

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