Closed DavidFangx closed 4 years ago
qingqing01
commented
4 years ago @DavidFangx 不需要增加新的分类。可以直接使用COCO训练出来的模型作为预训练模型，训练"头盔的检测" ，参考，https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.2/docs/advanced_tutorials/TRANSFER_LEARNING_cn.md 。
会自动不加在 包含类别 的参数，迁移学习训练对应的层。
DavidFangx
commented
4 years ago 你好，" 会自动不加在 包含类别 的参数，迁移学习训练对应的层。" 没理解，请明示下，谢谢
qingqing01
commented
4 years ago @DavidFangx 参考这里https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.2/docs/advanced_tutorials/TRANSFER_LEARNING_cn.md#paddledetection%E8%BF%9B%E8%A1%8C%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0
您用的是YOLOv3模型，可以通过命令行，设置下 -o finetune_exclude_pretrained_params=yolo_output， yolo_output这个weight的shape和类别数相关，当你换了数据、类别数改变之后，这层的参数不需要加载，设置下就行。
DavidFangx
commented
4 years ago 你好，关于数据集， 继续使用coco 数据集，然后把自定义的头盔数据集与coco数据集合并成一个json文件，
问题 1） 配置文件 num_classes: 80 这个参数需要 改为 num_classes: 81 吗？
2) 合并前与合并后训练，日志统计 2020-04-15 18:35:47,635-INFO: 118287 samples in file dataset/coco\annotations/instances_train2017.json ，打印出的 118287 samples 却是相同，个人理解应该是 118287 + 155 （新增的头盔图片数） = 118442
DavidFangx
commented
4 years ago 问题 3) 没有合并自定义的头盔数据集，能正常训练，在yml 里 加了 finetune_exclude_pretrained_params: ['yolo_output'] 参数， 好像不在训练，控制台没有训练结果输出 ....
一）配置文件，部分参数，其他参数没有调整 architecture: YOLOv3 use_gpu: true max_iters: 500200 log_smooth_window: 20 save_dir: output snapshot_iter: 2000 metric: COCO pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_pretrained.tar weights: output/yolov3_darknet/model_final num_classes: 80 finetune_exclude_pretrained_params: ['yolo_output'] # 新加参数 ，其他参数不变 use_fine_grained_loss: false
二）控制台输出： (PP_GPU) D:\python3\workspace\PaddleDetection>python tools/train.py -c configs/yolov3_darknet.yml DarkNet: [32mnorm_type[0m: sync_bn depth: 53 norm_decay: 0.0 weight_prefix_name: '' EvalReader: batch_size: 8 bufsize: 32 dataset: !COCODataSet anno_path: annotations/instances_val2017.json dataset_dir: dataset/coco image_dir: val2017 sample_num: -1 with_background: false drop_empty: false inputs_def: fields:
W0415 19:04:50.731580 20672 device_context.cc:237] Please NOTE: device: 0, CUDA Capability: 75, Driver API Version: 10.2, Runtime API Version: 10.0 W0415 19:04:50.749580 20672 device_context.cc:245] device: 0, cuDNN Version: 7.6. 2020-04-15 19:04:52,395-INFO: Found C:\Users\Administrator/.cache/paddle/weights\DarkNet53_pretrained 2020-04-15 19:04:52,395-INFO: Loading parameters from C:\Users\Administrator/.cache/paddle/weights\DarkNet53_pretrained... 2020-04-15 19:04:53,268-WARNING: C:\Users\ADMINI~1\AppData\Local\Temp\tmpkddskat8\DarkNet53_pretrained.pdparams not found, try to load model file saved with [ save_params, save_persistables, save_vars ] 2020-04-15 19:04:53,268-WARNING: C:\Users\ADMINI~1\AppData\Local\Temp\tmpkddskat8\DarkNet53_pretrained.pdparams not found, try to load model file saved with [ save_params, save_persistables, save_vars ] loading annotations into memory... Done (t=20.39s) creating index... index created! 2020-04-15 19:05:16,391-INFO: 118287 samples in file dataset/coco\annotations/instances_train2017.json 2020-04-15 19:05:16,417-INFO: Use multi-thread reader instead of multi-process reader on Windows. 2020-04-15 19:05:16,418-INFO: places would be ommited when DataLoader is not iterable I0415 19:05:16.442224 20672 parallel_executor.cc:440] The Program will be executed on CUDA using ParallelExecutor, 1 cards are used, so 1 programs are executed in parallel. I0415 19:05:16.493227 20672 build_strategy.cc:365] SeqOnlyAllReduceOps:0, num_trainers:1 I0415 19:05:16.606226 20672 parallel_executor.cc:307] Inplace strategy is enabled, when build_strategy.enable_inplace = True I0415 19:05:16.632226 20672 parallel_executor.cc:375] Garbage collection strategy is enabled, when FLAGS_eager_delete_tensor_gb = 0
DavidFangx
commented
4 years ago 数据集合并过程： 新加的155张图片已复制到 coco\train2017 目录下 ，同时把下面新增的头盔标注 json 插入到 coco 自带的 instances_train2017.json里，并在 instances_train2017.json的 categories 后面添加头盔类别了 {"supercategory": "indoor","id": 91,"name": "helmet"} :
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{"id": 202004152020041500171, "image_id": 202004152020041500154, "category_id": 91, "segmentation": [[1046.4390243902437, 180.92682926829266, 1075.7073170731705, 208.97560975609753]], "area": 870.0, "bbox": [1046.0, 180.0, 30.0, 29.0], "iscrowd": 0}
qingqing01
commented
4 years ago @DavidFangx
DavidFangx
commented
4 years ago 不只是头盔，还有识别 猫，狗，自行车，人等，但coco数据集没有头盔，所以我们整理一些含有头盔的图片，并labelMe进行了标注，coco的 categories 里的id最大值为90，所以 我添加了 头盔分类 （helmet） ID 为 class_num_id = 91 ,请参见下面：
"categories": [{"supercategory": "person","id": 1,"name": "person"},{"supercategory": "vehicle","id": 2,"name": "bicycle"},{"supercategory": "vehicle","id": 3,"name": "car"},{"supercategory": "vehicle","id": 4,"name": "motorcycle"},{"supercategory": "vehicle","id": 5,"name": "airplane"},{"supercategory": "vehicle","id": 6,"name": "bus"},{"supercategory": "vehicle","id": 7,"name": "train"},{"supercategory": "vehicle","id": 8,"name": "truck"},{"supercategory": "vehicle","id": 9,"name": "boat"},{"supercategory": "outdoor","id": 10,"name": "traffic light"},{"supercategory": "outdoor","id": 11,"name": "fire hydrant"},{"supercategory": "outdoor","id": 13,"name": "stop sign"},{"supercategory": "outdoor","id": 14,"name": "parking meter"},{"supercategory": "outdoor","id": 15,"name": "bench"},{"supercategory": "animal","id": 16,"name": "bird"},{"supercategory": "animal","id": 17,"name": "cat"},{"supercategory": "animal","id": 18,"name": "dog"},{"supercategory": "animal","id": 19,"name": "horse"},{"supercategory": "animal","id": 20,"name": "sheep"},{"supercategory": "animal","id": 21,"name": "cow"},{"supercategory": "animal","id": 22,"name": "elephant"},{"supercategory": "animal","id": 23,"name": "bear"},{"supercategory": "animal","id": 24,"name": "zebra"},{"supercategory": "animal","id": 25,"name": "giraffe"},{"supercategory": "accessory","id": 27,"name": "backpack"},{"supercategory": "accessory","id": 28,"name": "umbrella"},{"supercategory": "accessory","id": 31,"name": "handbag"},{"supercategory": "accessory","id": 32,"name": "tie"},{"supercategory": "accessory","id": 33,"name": "suitcase"},{"supercategory": "sports","id": 34,"name": "frisbee"},{"supercategory": "sports","id": 35,"name": "skis"},{"supercategory": "sports","id": 36,"name": "snowboard"},{"supercategory": "sports","id": 37,"name": "sports ball"},{"supercategory": "sports","id": 38,"name": "kite"},{"supercategory": "sports","id": 39,"name": "baseball bat"},{"supercategory": "sports","id": 40,"name": "baseball glove"},{"supercategory": "sports","id": 41,"name": "skateboard"},{"supercategory": "sports","id": 42,"name": "surfboard"},{"supercategory": "sports","id": 43,"name": "tennis racket"},{"supercategory": "kitchen","id": 44,"name": "bottle"},{"supercategory": "kitchen","id": 46,"name": "wine glass"},{"supercategory": "kitchen","id": 47,"name": "cup"},{"supercategory": "kitchen","id": 48,"name": "fork"},{"supercategory": "kitchen","id": 49,"name": "knife"},{"supercategory": "kitchen","id": 50,"name": "spoon"},{"supercategory": "kitchen","id": 51,"name": "bowl"},{"supercategory": "food","id": 52,"name": "banana"},{"supercategory": "food","id": 53,"name": "apple"},{"supercategory": "food","id": 54,"name": "sandwich"},{"supercategory": "food","id": 55,"name": "orange"},{"supercategory": "food","id": 56,"name": "broccoli"},{"supercategory": "food","id": 57,"name": "carrot"},{"supercategory": "food","id": 58,"name": "hot dog"},{"supercategory": "food","id": 59,"name": "pizza"},{"supercategory": "food","id": 60,"name": "donut"},{"supercategory": "food","id": 61,"name": "cake"},{"supercategory": "furniture","id": 62,"name": "chair"},{"supercategory": "furniture","id": 63,"name": "couch"},{"supercategory": "furniture","id": 64,"name": "potted plant"},{"supercategory": "furniture","id": 65,"name": "bed"},{"supercategory": "furniture","id": 67,"name": "dining table"},{"supercategory": "furniture","id": 70,"name": "toilet"},{"supercategory": "electronic","id": 72,"name": "tv"},{"supercategory": "electronic","id": 73,"name": "laptop"},{"supercategory": "electronic","id": 74,"name": "mouse"},{"supercategory": "electronic","id": 75,"name": "remote"},{"supercategory": "electronic","id": 76,"name": "keyboard"},{"supercategory": "electronic","id": 77,"name": "cell phone"},{"supercategory": "appliance","id": 78,"name": "microwave"},{"supercategory": "appliance","id": 79,"name": "oven"},{"supercategory": "appliance","id": 80,"name": "toaster"},{"supercategory": "appliance","id": 81,"name": "sink"},{"supercategory": "appliance","id": 82,"name": "refrigerator"},{"supercategory": "indoor","id": 84,"name": "book"},{"supercategory": "indoor","id": 85,"name": "clock"},{"supercategory": "indoor","id": 86,"name": "vase"},{"supercategory": "indoor","id": 87,"name": "scissors"},{"supercategory": "indoor","id": 88,"name": "teddy bear"},{"supercategory": "indoor","id": 89,"name": "hair drier"},{"supercategory": "indoor","id": 90,"name": "toothbrush"},{"supercategory": "indoor","id": 91,"name": "helmet"}]}
以及数据合并，是否有参考示例
谢谢解答
qingqing01
commented
4 years ago @DavidFangx
https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.2/ppdet/data/tools/x2coco.py 这个是转换为COCO数据的，可以参考如何保存COCO的格式。
先读取2种数据格式，再合并保存。
AntyRia
commented
1 year ago 您好，请问coco
@DavidFangx
- 请问您最终的目标是啥？ 是只检测头盔吗？ 如果是，为什么要和COCO合并在一起呢？ 合并在一起，只有头盔的数据相比要少很多。 如果只检测头盔一类，可以用COCO的模型初始化，num_classes是1。
- 数据的合并，不是简单的拷贝在一起，需要一些代码，把2个json文件读取出来， 头盔的对应的class_num_id是多少，修正class_id等，再合并写入一个文件。
你好，请问初始化模型的链接可以提供吗
使用已有预训练模型来训练新模型，若已有预训练模型的分类为80 ，在训练新模型时，能否增加新分类，若能增加，请问怎么增加？
比如：使用这个 https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_pretrained.tar 模型 ，该模型不包含头盔分类，可以使用这个模型来训练头盔识别？