diff --git a/cvmart/Helmet_identification_10163/convert_to_coco.py b/cvmart/Helmet_identification_10163/convert_to_coco.py
new file mode 100644
index 00000000..5d683d4e
--- /dev/null
+++ b/cvmart/Helmet_identification_10163/convert_to_coco.py
@@ -0,0 +1,153 @@
+# coding:utf-8
+
+# pip install lxml
+
+import os
+import glob
+import json
+import shutil
+import numpy as np
+
+import xml.etree.ElementTree as ET
+
+path2 = "."
+
+START_BOUNDING_BOX_ID = 1
+
+
+def get(root, name):
+ return root.findall(name)
+
+
+def get_and_check(root, name, length):
+ vars = root.findall(name)
+ if len(vars) == 0:
+ raise NotImplementedError('Can not find %s in %s.' % (name, root.tag))
+ if length > 0 and len(vars) != length:
+ raise NotImplementedError('The size of %s is supposed to be %d, but is %d.' % (name, length, len(vars)))
+ if length == 1:
+ vars = vars[0]
+ return vars
+
+
+def convert(xml_list, json_file):
+ json_dict = {"images": [], "type": "instances", "annotations": [], "categories": []}
+ categories = pre_define_categories.copy()
+ bnd_id = START_BOUNDING_BOX_ID
+ all_categories = {}
+ for index, line in enumerate(xml_list):
+ # print("Processing %s"%(line))
+ xml_f = line
+ tree = ET.parse(xml_f)
+ root = tree.getroot()
+
+ filename = os.path.basename(xml_f)[:-4] + ".jpg"
+ image_id = 20190000001 + index
+ size = get_and_check(root, 'size', 1)
+ width = int(get_and_check(size, 'width', 1).text)
+ height = int(get_and_check(size, 'height', 1).text)
+ image = {'file_name': filename, 'height': height, 'width': width, 'id': image_id}
+ json_dict['images'].append(image)
+ ## Cruuently we do not support segmentation
+ # segmented = get_and_check(root, 'segmented', 1).text
+ # assert segmented == '0'
+ for obj in get(root, 'object'):
+ category = get_and_check(obj, 'name', 1).text
+ if category in all_categories:
+ all_categories[category] += 1
+ else:
+ all_categories[category] = 1
+ if category not in categories:
+ if only_care_pre_define_categories:
+ continue
+ new_id = len(categories) + 1
+ print(
+ "[warning] category '{}' not in 'pre_define_categories'({}), create new id: {} automatically".format(
+ category, pre_define_categories, new_id))
+ categories[category] = new_id
+ category_id = categories[category]
+ bndbox = get_and_check(obj, 'bndbox', 1)
+ xmin = int(float(get_and_check(bndbox, 'xmin', 1).text))
+ ymin = int(float(get_and_check(bndbox, 'ymin', 1).text))
+ xmax = int(float(get_and_check(bndbox, 'xmax', 1).text))
+ ymax = int(float(get_and_check(bndbox, 'ymax', 1).text))
+ assert (xmax > xmin), "xmax <= xmin, {}".format(line)
+ assert (ymax > ymin), "ymax <= ymin, {}".format(line)
+ o_width = abs(xmax - xmin)
+ o_height = abs(ymax - ymin)
+ ann = {'area': o_width * o_height, 'iscrowd': 0, 'image_id':
+ image_id, 'bbox': [xmin, ymin, o_width, o_height],
+ 'category_id': category_id, 'id': bnd_id, 'ignore': 0,
+ 'segmentation': []}
+ json_dict['annotations'].append(ann)
+ bnd_id = bnd_id + 1
+
+ for cate, cid in categories.items():
+ cat = {'supercategory': 'none', 'id': cid, 'name': cate}
+ json_dict['categories'].append(cat)
+ json_fp = open(json_file, 'w')
+ json_str = json.dumps(json_dict)
+ json_fp.write(json_str)
+ json_fp.close()
+ print("------------create {} done--------------".format(json_file))
+ print("find {} categories: {} -->>> your pre_define_categories {}: {}".format(len(all_categories),
+ all_categories.keys(),
+ len(pre_define_categories),
+ pre_define_categories.keys()))
+ print("category: id --> {}".format(categories))
+ print(categories.keys())
+ print(categories.values())
+
+
+if __name__ == '__main__':
+ classes = ['person', 'head', 'hat']
+ pre_define_categories = {}
+ for i, cls in enumerate(classes):
+ pre_define_categories[cls] = i + 1
+ # pre_define_categories = {'a1': 1, 'a3': 2, 'a6': 3, 'a9': 4, "a10": 5}
+ only_care_pre_define_categories = True
+ # only_care_pre_define_categories = False
+
+ train_ratio = 0.9
+ save_json_train = '/project/train/src_repo/coco_annotations/instances_train2014.json'
+ save_json_val = 'instances_val2014.json'
+ xml_dir = "/home/data/831/"
+
+ xml_list = glob.glob(xml_dir + "/*.xml")
+ xml_list = np.sort(xml_list)
+ np.random.seed(100)
+ np.random.shuffle(xml_list)
+
+ train_num = int(len(xml_list) * train_ratio)
+ xml_list_train = xml_list[:train_num]
+ xml_list_val = xml_list[train_num:]
+
+ convert(xml_list_train, save_json_train)
+ convert(xml_list_val, save_json_val)
+
+ if os.path.exists(path2 + "/annotations"):
+ shutil.rmtree(path2 + "/annotations")
+ os.makedirs(path2 + "/annotations")
+ if os.path.exists(path2 + "/images/train2014"):
+ shutil.rmtree(path2 + "/images/train2014")
+ os.makedirs(path2 + "/images/train2014")
+ if os.path.exists(path2 + "/images/val2014"):
+ shutil.rmtree(path2 + "/images/val2014")
+ os.makedirs(path2 + "/images/val2014")
+
+ f1 = open("train.txt", "w")
+ for xml in xml_list_train:
+ img = xml[:-4] + ".jpg"
+ f1.write(os.path.basename(xml)[:-4] + "\n")
+ shutil.copyfile(img, path2 + "/images/train2014/" + os.path.basename(img))
+
+ f2 = open("test.txt", "w")
+ for xml in xml_list_val:
+ img = xml[:-4] + ".jpg"
+ f2.write(os.path.basename(xml)[:-4] + "\n")
+ shutil.copyfile(img, path2 + "/images/val2014/" + os.path.basename(img))
+ f1.close()
+ f2.close()
+ print("-------------------------------")
+ print("train number:", len(xml_list_train))
+ print("val number:", len(xml_list_val))
diff --git a/cvmart/Helmet_identification_10163/is_there_tensorboard.py b/cvmart/Helmet_identification_10163/is_there_tensorboard.py
new file mode 100644
index 00000000..0b1d8c85
--- /dev/null
+++ b/cvmart/Helmet_identification_10163/is_there_tensorboard.py
@@ -0,0 +1,8 @@
+import os
+
+if os.path.exists('/project/train/tensorboard'):
+ # os.system('cp -r /project/train/models/train/exp/weights/* /project/train/tensorboard/')
+ os.system('rm -rf /project/train/tensorboard')
+else:
+ os.system('mkdir /project/train/tensorboard')
+ # os.system('cp -r /project/train/tensorboard/* /project/train/models/train/exp/weights/')
diff --git a/cvmart/Helmet_identification_10163/ji.py b/cvmart/Helmet_identification_10163/ji.py
new file mode 100644
index 00000000..d9ace9a5
--- /dev/null
+++ b/cvmart/Helmet_identification_10163/ji.py
@@ -0,0 +1,102 @@
+import json # json模块是Python标准库中的一个模块，用于处理JSON格式的数据。在该代码中，使用json模块将检测结果转换为JSON格式。
+from mmdet.apis import init_detector, inference_detector
+
+import mmcv
+
+# from mmdet_custom.datasets import D10007Dataset
+
+def init():
+
+ config_file = '/project/train/src_repo/mmyolo/tools/rtmdet_tiny_syncbn_fast_8xb32-300e_coco.py'
+ checkpoint_file = '/project/train/models/train/exp/weights/epoch_320.pth'
+ model = init_detector(config_file, checkpoint_file)
+ # init_detector函数用于初始化MMDetection模型，输入参数包括模型配置文件路径config_file和模型权重文件路径checkpoint_file。该函数返回一个MMDetection模型对象model，用于后续的目标检测操作。
+ return model
+
+def process_image(handle=None, input_image=None, args=None, **kwargs):
+
+ # CLASSES = D10007Dataset.CLASSES # D10007Dataset是一个自定义的数据集类，用于加载数据集的类别信息。在该代码中，通过D10007Dataset.CLASSES获取数据集的类别信息，用于后续的处理。
+ CLASSES = ['person', 'hat', 'head']
+ # CLASSES 是一个 Python列表 例如CLASSES的值为["person", "car", "dog"]
+
+ # result = inference_detector(handle, input_image)
+ # # inference_detector函数用于对输入图像进行目标检测，输入参数包括MMDetection模型对象handle和输入图像input_image。
+ # # 该函数返回一个包含检测结果的列表，其中每个元素表示一个检测框的信息，包括坐标、置信度、类别等。
+
+ # labels = result.pred_instances.labels
+ # objects = []
+ # for i, class_name in enumerate(CLASSES): # 遍历数据集的类别信息CLASSES，对每个类别的检测结果进行处理。
+ # # 具体来说，对于每个类别的检测结果，遍历其中的每个检测框，将检测框的 坐标、置信度、类别 等信息转换为字典格式，并添加到objects列表中。
+
+ # fires = result[i]
+ # for fire in fires: # 具体来说，对于每个类别的检测结果，从result列表中获取该类别的检测结果fires，然后遍历其中的每个检测框fire。
+ # obj = dict(
+ # xmin = int(fire[0].item()),
+ # ymin= int(fire[1].item()),
+ # xmax=int(fire[2].item()),
+ # ymax=int(fire[3].item()),
+ # confidence=fire[4].item(),
+ # name = CLASSES[i]
+ # )
+ # # 对于每个检测框，将其坐标、置信度、类别等信息转换为字典格式，并添加到objects列表中。
+ # # 具体来说，使用dict函数创建一个字典对象obj，其中包含检测框的左上角和右下角坐标、置信度、类别等信息。然后，将obj添加到objects列表中。
+
+ # if obj['confidence' ] >0.5:
+ # objects.append(obj)
+
+ result = inference_detector(handle, input_image)
+ bboxes = result.pred_instances.bboxes
+ scores = result.pred_instances.scores
+ labels = result.pred_instances.labels
+ objects = []
+ fan = len(bboxes)
+ for i in range(len(bboxes)):
+
+ obj = dict(
+ xmin = int(bboxes[i][0]),
+ ymin = int(bboxes[i][1]),
+ xmax = int(bboxes[i][2]),
+ ymax = int(bboxes[i][3]),
+ confidence = float(scores[i]),
+ name = CLASSES[labels[i]])
+ if obj['confidence'] > 0.5:
+ objects.append(obj)
+
+
+
+
+ # model.show_result(img, result)
+ # model.show_result(img, result, out_file='result.jpg', score_thr = 0.3)
+ r_json = dict()
+ r_json['algorithm_data'] = dict(target_info=objects, is_alert=False, target_count=0)
+ r_json['model_data'] = dict(objects=objects)
+
+ '''
+ 这段代码的作用是将MMDetection模型的检测结果转换为字典格式，并添加到objects列表中。具体分析如下：
+
+ objects是一个空列表，用于存储检测结果的信息。
+
+ 在循环体中，使用enumerate函数遍历数据集的类别信息CLASSES，对每个类别的检测结果进行处理。具体来说，对于每个类别的检测结果，从result列表中获取该类别的检测结果fires，然后遍历其中的每个检测框fire。
+
+ 对于每个检测框，将其坐标、置信度、类别等信息转换为字典格式，并添加到objects列表中。具体来说，使用dict函数创建一个字典对象obj，
+ 其中包含检测框的左上角和右下角坐标、置信度、类别等信息。然后，将obj添加到objects列表中。
+
+ 在添加obj到objects列表之前，使用if语句判断检测框的置信度是否大于0.5。如果是，则将obj添加到objects列表中；否则，忽略该检测框。
+
+ 最后，将objects列表转换为字典格式，并添加到r_json字典中。其中，r_json字典包含两个键值对，分别为algorithm_data和model_data。
+ algorithm_data表示算法的输出结果，包括目标信息、是否报警、目标数量等；model_data表示模型的输出结果，包括检测框的信息。
+ '''
+
+ if objects.__len__( ) >0:
+ r_json['algorithm_data']['is_alert'] = True
+ r_json['algorithm_data']['target_count'] = objects.__len__()
+
+ # return json.dumps(objects, indent=4)
+ return json.dumps(r_json, indent=4)
+
+if __name__ == "__main__":
+ handle = init()
+ # 或者 img = mmcv.imread(img), 这将只加载图像一次．
+ img = "/home/data/831/helmet_10809.jpg"
+
+ process_image(handle, img, '{"mask_output_path": "result.png"}')
diff --git a/cvmart/Helmet_identification_10163/move_log.py b/cvmart/Helmet_identification_10163/move_log.py
new file mode 100644
index 00000000..0b945552
--- /dev/null
+++ b/cvmart/Helmet_identification_10163/move_log.py
@@ -0,0 +1,7 @@
+import os
+
+if os.path.exists('/project/train/tensorboard'):
+ os.system('cp -r /project/train/models/train/exp/weights/* /project/train/tensorboard/')
+else:
+ os.system('mkdir /project/train/tensorboard')
+ os.system('cp -r /project/train/models/train/exp/weights/* /project/train/tensorboard/')
diff --git a/cvmart/Helmet_identification_10163/move_model.py b/cvmart/Helmet_identification_10163/move_model.py
new file mode 100644
index 00000000..0e701231
--- /dev/null
+++ b/cvmart/Helmet_identification_10163/move_model.py
@@ -0,0 +1,8 @@
+import os
+
+if os.path.exists('/project/train/tensorboard'):
+ # os.system('cp -r /project/train/models/train/exp/weights/* /project/train/tensorboard/')
+ os.system('cp -r /project/train/tensorboard/* /project/train/models/train/exp/weights/')
+else:
+ os.system('mkdir /project/train/tensorboard')
+ os.system('cp -r /project/train/tensorboard/* /project/train/models/train/exp/weights/')
diff --git a/cvmart/Helmet_identification_10163/rtmdet_tiny_syncbn_fast_8xb32-300e_coco.py b/cvmart/Helmet_identification_10163/rtmdet_tiny_syncbn_fast_8xb32-300e_coco.py
new file mode 100644
index 00000000..f2ed3a6b
--- /dev/null
+++ b/cvmart/Helmet_identification_10163/rtmdet_tiny_syncbn_fast_8xb32-300e_coco.py
@@ -0,0 +1,504 @@
+default_scope = 'mmyolo'
+default_hooks = dict(
+ timer=dict(type='IterTimerHook'),
+ logger=dict(type='LoggerHook', interval=50),
+ param_scheduler=dict(type='ParamSchedulerHook'),
+ checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=3),
+ sampler_seed=dict(type='DistSamplerSeedHook'),
+ visualization=dict(type='mmdet.DetVisualizationHook'))
+env_cfg = dict(
+ cudnn_benchmark=True,
+ mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+ dist_cfg=dict(backend='nccl'))
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+ type='mmdet.DetLocalVisualizer',
+ vis_backends=[dict(type='LocalVisBackend')],
+ name='visualizer')
+log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
+log_level = 'INFO'
+load_from = None
+resume = False
+file_client_args = dict(backend='disk')
+_file_client_args = dict(backend='disk')
+tta_model = dict(
+ type='mmdet.DetTTAModel',
+ tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.65), max_per_img=300))
+img_scales = [(640, 640), (320, 320), (960, 960)]
+_multiscale_resize_transforms = [
+ dict(
+ type='Compose',
+ transforms=[
+ dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),
+ dict(
+ type='LetterResize',
+ scale=(640, 640),
+ allow_scale_up=False,
+ pad_val=dict(img=114))
+ ]),
+ dict(
+ type='Compose',
+ transforms=[
+ dict(type='YOLOv5KeepRatioResize', scale=(320, 320)),
+ dict(
+ type='LetterResize',
+ scale=(320, 320),
+ allow_scale_up=False,
+ pad_val=dict(img=114))
+ ]),
+ dict(
+ type='Compose',
+ transforms=[
+ dict(type='YOLOv5KeepRatioResize', scale=(960, 960)),
+ dict(
+ type='LetterResize',
+ scale=(960, 960),
+ allow_scale_up=False,
+ pad_val=dict(img=114))
+ ])
+]
+tta_pipeline = [
+ dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
+ dict(
+ type='TestTimeAug',
+ transforms=[[{
+ 'type':
+ 'Compose',
+ 'transforms': [{
+ 'type': 'YOLOv5KeepRatioResize',
+ 'scale': (640, 640)
+ }, {
+ 'type': 'LetterResize',
+ 'scale': (640, 640),
+ 'allow_scale_up': False,
+ 'pad_val': {
+ 'img': 114
+ }
+ }]
+ }, {
+ 'type':
+ 'Compose',
+ 'transforms': [{
+ 'type': 'YOLOv5KeepRatioResize',
+ 'scale': (320, 320)
+ }, {
+ 'type': 'LetterResize',
+ 'scale': (320, 320),
+ 'allow_scale_up': False,
+ 'pad_val': {
+ 'img': 114
+ }
+ }]
+ }, {
+ 'type':
+ 'Compose',
+ 'transforms': [{
+ 'type': 'YOLOv5KeepRatioResize',
+ 'scale': (960, 960)
+ }, {
+ 'type': 'LetterResize',
+ 'scale': (960, 960),
+ 'allow_scale_up': False,
+ 'pad_val': {
+ 'img': 114
+ }
+ }]
+ }],
+ [{
+ 'type': 'mmdet.RandomFlip',
+ 'prob': 1.0
+ }, {
+ 'type': 'mmdet.RandomFlip',
+ 'prob': 0.0
+ }], [{
+ 'type': 'mmdet.LoadAnnotations',
+ 'with_bbox': True
+ }],
+ [{
+ 'type':
+ 'mmdet.PackDetInputs',
+ 'meta_keys':
+ ('img_id', 'img_path', 'ori_shape', 'img_shape',
+ 'scale_factor', 'pad_param', 'flip', 'flip_direction')
+ }]])
+]
+data_root = 'data/coco/'
+train_ann_file = 'annotations/instances_train2017.json'
+train_data_prefix = 'train2017/'
+val_ann_file = 'annotations/instances_val2017.json'
+val_data_prefix = 'val2017/'
+
+class_name = ('person', 'head', 'hat')
+metainfo = dict(
+classes=class_name,
+palette=[(255, 0, 0),
+(255, 128, 0),
+(255, 255, 0)]
+# (128, 255, 0),
+# (0, 255, 0),
+# (0, 255, 128),
+# (0, 255, 255),
+# (0, 128, 255),
+# (0, 0, 255),
+# (127, 0, 255),
+# (255, 0, 255),
+# (255, 0, 127),
+# (128, 128, 128)] # 画图时候的颜色，随便设置即可
+)
+
+num_classes = 3
+train_batch_size_per_gpu = 32
+train_num_workers = 10
+persistent_workers = True
+base_lr = 0.004
+max_epochs = 300
+num_epochs_stage2 = 20
+model_test_cfg = dict(
+ multi_label=True,
+ nms_pre=30000,
+ score_thr=0.001,
+ nms=dict(type='nms', iou_threshold=0.65),
+ max_per_img=300)
+img_scale = (640, 640)
+random_resize_ratio_range = (0.5, 2.0)
+mosaic_max_cached_images = 20
+mixup_max_cached_images = 10
+dataset_type = 'YOLOv5CocoDataset'
+val_batch_size_per_gpu = 32
+val_num_workers = 10
+batch_shapes_cfg = dict(
+ type='BatchShapePolicy',
+ batch_size=32,
+ img_size=640,
+ size_divisor=32,
+ extra_pad_ratio=0.5)
+deepen_factor = 0.167
+widen_factor = 0.375
+strides = [8, 16, 32]
+norm_cfg = dict(type='BN')
+lr_start_factor = 1e-05
+dsl_topk = 13
+loss_cls_weight = 1.0
+loss_bbox_weight = 2.0
+qfl_beta = 2.0
+weight_decay = 0.05
+save_checkpoint_intervals = 10
+val_interval_stage2 = 1
+max_keep_ckpts = 3
+load_from = '/project/train/src_repo/mmyolo/tools/pth/rtmdet_tiny_syncbn_fast_8xb32-300e_coco.pth'
+model = dict(
+ type='YOLODetector',
+ data_preprocessor=dict(
+ type='YOLOv5DetDataPreprocessor',
+ mean=[103.53, 116.28, 123.675],
+ std=[57.375, 57.12, 58.395],
+ bgr_to_rgb=False),
+ backbone=dict(
+ type='CSPNeXt',
+ arch='P5',
+ expand_ratio=0.5,
+ deepen_factor=0.167,
+ widen_factor=0.375,
+ channel_attention=True,
+ norm_cfg=dict(type='BN'),
+ act_cfg=dict(type='SiLU', inplace=True),
+ init_cfg=dict(
+ type='Pretrained',
+ prefix='backbone.',
+ checkpoint=
+ 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth',
+ map_location='cpu')),
+ neck=dict(
+ type='CSPNeXtPAFPN',
+ deepen_factor=0.167,
+ widen_factor=0.375,
+ in_channels=[256, 512, 1024],
+ out_channels=256,
+ num_csp_blocks=3,
+ expand_ratio=0.5,
+ norm_cfg=dict(type='BN'),
+ act_cfg=dict(type='SiLU', inplace=True)),
+ bbox_head=dict(
+ type='RTMDetHead',
+ head_module=dict(
+ type='RTMDetSepBNHeadModule',
+ num_classes=3,
+ in_channels=256,
+ stacked_convs=2,
+ feat_channels=256,
+ norm_cfg=dict(type='BN'),
+ act_cfg=dict(type='SiLU', inplace=True),
+ share_conv=True,
+ pred_kernel_size=1,
+ featmap_strides=[8, 16, 32],
+ widen_factor=0.375),
+ prior_generator=dict(
+ type='mmdet.MlvlPointGenerator', offset=0, strides=[8, 16, 32]),
+ bbox_coder=dict(type='DistancePointBBoxCoder'),
+ loss_cls=dict(
+ type='mmdet.QualityFocalLoss',
+ use_sigmoid=True,
+ beta=2.0,
+ loss_weight=1.0),
+ loss_bbox=dict(type='mmdet.GIoULoss', loss_weight=2.0)),
+ train_cfg=dict(
+ assigner=dict(
+ type='BatchDynamicSoftLabelAssigner',
+ num_classes=3,
+ topk=13,
+ iou_calculator=dict(type='mmdet.BboxOverlaps2D')),
+ allowed_border=-1,
+ pos_weight=-1,
+ debug=False),
+ test_cfg=dict(
+ multi_label=True,
+ nms_pre=30000,
+ score_thr=0.001,
+ nms=dict(type='nms', iou_threshold=0.65),
+ max_per_img=300))
+train_pipeline = [
+ dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
+ dict(type='LoadAnnotations', with_bbox=True),
+ dict(
+ type='Mosaic',
+ img_scale=(640, 640),
+ use_cached=True,
+ max_cached_images=20,
+ random_pop=False,
+ pad_val=114.0),
+ dict(
+ type='mmdet.RandomResize',
+ scale=(1280, 1280),
+ ratio_range=(0.5, 2.0),
+ resize_type='mmdet.Resize',
+ keep_ratio=True),
+ dict(type='mmdet.RandomCrop', crop_size=(640, 640)),
+ dict(type='mmdet.YOLOXHSVRandomAug'),
+ dict(type='mmdet.RandomFlip', prob=0.5),
+ dict(type='mmdet.Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
+ dict(
+ type='YOLOv5MixUp',
+ use_cached=True,
+ random_pop=False,
+ max_cached_images=10,
+ prob=0.5),
+ dict(type='mmdet.PackDetInputs')
+]
+train_pipeline_stage2 = [
+ dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
+ dict(type='LoadAnnotations', with_bbox=True),
+ dict(
+ type='mmdet.RandomResize',
+ scale=(640, 640),
+ ratio_range=(0.5, 2.0),
+ resize_type='mmdet.Resize',
+ keep_ratio=True),
+ dict(type='mmdet.RandomCrop', crop_size=(640, 640)),
+ dict(type='mmdet.YOLOXHSVRandomAug'),
+ dict(type='mmdet.RandomFlip', prob=0.5),
+ dict(type='mmdet.Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
+ dict(type='mmdet.PackDetInputs')
+]
+test_pipeline = [
+ dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
+ dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),
+ dict(
+ type='LetterResize',
+ scale=(640, 640),
+ allow_scale_up=False,
+ pad_val=dict(img=114)),
+ dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
+ dict(
+ type='mmdet.PackDetInputs',
+ meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+ 'scale_factor', 'pad_param'))
+]
+train_dataloader = dict(
+ batch_size=16,
+ num_workers=4,
+ persistent_workers=True,
+ pin_memory=True,
+ collate_fn=dict(type='yolov5_collate'),
+ sampler=dict(type='DefaultSampler', shuffle=True),
+ dataset=dict(
+ type='YOLOv5CocoDataset',
+ metainfo = metainfo,
+ data_root='/project/train/src_repo/',
+ ann_file='coco_annotations/instances_train2014.json',
+ data_prefix=dict(img='dataset/images'),
+ filter_cfg=dict(filter_empty_gt=True, min_size=32),
+ pipeline=[
+ dict(
+ type='LoadImageFromFile',
+ file_client_args=dict(backend='disk')),
+ dict(type='LoadAnnotations', with_bbox=True),
+ dict(
+ type='Mosaic',
+ img_scale=(640, 640),
+ use_cached=True,
+ max_cached_images=20,
+ random_pop=False,
+ pad_val=114.0),
+ dict(
+ type='mmdet.RandomResize',
+ scale=(1280, 1280),
+ ratio_range=(0.5, 2.0),
+ resize_type='mmdet.Resize',
+ keep_ratio=True),
+ dict(type='mmdet.RandomCrop', crop_size=(640, 640)),
+ dict(type='mmdet.YOLOXHSVRandomAug'),
+ dict(type='mmdet.RandomFlip', prob=0.5),
+ dict(
+ type='mmdet.Pad',
+ size=(640, 640),
+ pad_val=dict(img=(114, 114, 114))),
+ dict(
+ type='YOLOv5MixUp',
+ use_cached=True,
+ random_pop=False,
+ max_cached_images=10,
+ prob=0.5),
+ dict(type='mmdet.PackDetInputs')
+ ]))
+val_dataloader = dict(
+ batch_size=16,
+ num_workers=4,
+ persistent_workers=True,
+ pin_memory=True,
+ drop_last=False,
+ sampler=dict(type='DefaultSampler', shuffle=False),
+ dataset=dict(
+ type='YOLOv5CocoDataset',
+ metainfo = metainfo,
+ data_root='/project/train/src_repo/',
+ ann_file='coco_annotations/instances_train2014.json',
+ data_prefix=dict(img='dataset/images'),
+ test_mode=True,
+ batch_shapes_cfg=dict(
+ type='BatchShapePolicy',
+ batch_size=32,
+ img_size=640,
+ size_divisor=32,
+ extra_pad_ratio=0.5),
+ pipeline=[
+ dict(
+ type='LoadImageFromFile',
+ file_client_args=dict(backend='disk')),
+ dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),
+ dict(
+ type='LetterResize',
+ scale=(640, 640),
+ allow_scale_up=False,
+ pad_val=dict(img=114)),
+ dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
+ dict(
+ type='mmdet.PackDetInputs',
+ meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+ 'scale_factor', 'pad_param'))
+ ]))
+test_dataloader = dict(
+ batch_size=16,
+ num_workers=4,
+ persistent_workers=True,
+ pin_memory=True,
+ drop_last=False,
+ sampler=dict(type='DefaultSampler', shuffle=False),
+ dataset=dict(
+ type='YOLOv5CocoDataset',
+ metainfo = metainfo,
+ data_root='/project/train/src_repo/',
+ ann_file='coco_annotations/instances_train2014.json',
+ data_prefix=dict(img='dataset/images'),
+ test_mode=True,
+ batch_shapes_cfg=dict(
+ type='BatchShapePolicy',
+ batch_size=32,
+ img_size=640,
+ size_divisor=32,
+ extra_pad_ratio=0.5),
+ pipeline=[
+ dict(
+ type='LoadImageFromFile',
+ file_client_args=dict(backend='disk')),
+ dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),
+ dict(
+ type='LetterResize',
+ scale=(640, 640),
+ allow_scale_up=False,
+ pad_val=dict(img=114)),
+ dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
+ dict(
+ type='mmdet.PackDetInputs',
+ meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+ 'scale_factor', 'pad_param'))
+ ]))
+val_evaluator = dict(
+ type='mmdet.CocoMetric',
+ proposal_nums=(100, 1, 10),
+ ann_file='/project/train/src_repo/coco_annotations/instances_train2014.json',
+ metric='bbox')
+test_evaluator = dict(
+ type='mmdet.CocoMetric',
+ proposal_nums=(100, 1, 10),
+ ann_file='/project/train/src_repo/coco_annotations/instances_train2014.json',
+ metric='bbox')
+optim_wrapper = dict(
+ type='OptimWrapper',
+ optimizer=dict(type='AdamW', lr=0.004, weight_decay=0.05),
+ paramwise_cfg=dict(
+ norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
+param_scheduler = [
+ dict(
+ type='LinearLR', start_factor=1e-05, by_epoch=False, begin=0,
+ end=1000),
+ dict(
+ type='CosineAnnealingLR',
+ eta_min=0.0002,
+ begin=150,
+ end=300,
+ T_max=150,
+ by_epoch=True,
+ convert_to_iter_based=True)
+]
+custom_hooks = [
+ dict(
+ type='EMAHook',
+ ema_type='ExpMomentumEMA',
+ momentum=0.0002,
+ update_buffers=True,
+ strict_load=False,
+ priority=49),
+ dict(
+ type='mmdet.PipelineSwitchHook',
+ switch_epoch=280,
+ switch_pipeline=[
+ dict(
+ type='LoadImageFromFile',
+ file_client_args=dict(backend='disk')),
+ dict(type='LoadAnnotations', with_bbox=True),
+ dict(
+ type='mmdet.RandomResize',
+ scale=(640, 640),
+ ratio_range=(0.5, 2.0),
+ resize_type='mmdet.Resize',
+ keep_ratio=True),
+ dict(type='mmdet.RandomCrop', crop_size=(640, 640)),
+ dict(type='mmdet.YOLOXHSVRandomAug'),
+ dict(type='mmdet.RandomFlip', prob=0.5),
+ dict(
+ type='mmdet.Pad',
+ size=(640, 640),
+ pad_val=dict(img=(114, 114, 114))),
+ dict(type='mmdet.PackDetInputs')
+ ])
+]
+train_cfg = dict(
+ type='EpochBasedTrainLoop',
+ max_epochs=600,
+ val_interval=10,
+ dynamic_intervals=[(280, 1)])
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
+checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth'
+launcher = 'none'
+work_dir = '/project/train/models/train/exp/weights'
diff --git a/cvmart/Helmet_identification_10163/run.sh b/cvmart/Helmet_identification_10163/run.sh
new file mode 100644
index 00000000..1a789611
--- /dev/null
+++ b/cvmart/Helmet_identification_10163/run.sh
@@ -0,0 +1,9 @@
+rm -rf /project/train/models/train/exp/weights
+
+mkdir /project/train/models/train/exp/weights
+rm -rf /project/train/tensorboard
+
+cp /home/data/831/*.jpg /project/train/src_repo/dataset/images
+python /project/train/src_repo/convert_to_coco.py
+
+python /project/train/src_repo/mmyolo/tools/train.py /project/train/src_repo/mmyolo/tools/rtmdet_tiny_syncbn_fast_8xb32-300e_coco.py
diff --git a/cvmart/readme.md b/cvmart/readme.md
new file mode 100644
index 00000000..91658c60
--- /dev/null
+++ b/cvmart/readme.md
@@ -0,0 +1,384 @@
+# 极市打榜-安全帽识别-新手任务（白银榜）
+
+本教程和相关脚本代码由社区同学 @geoffreyfan 贡献~
+
+## 打榜链接：https://cvmart.net/topList/10163?tab=RealTime
+
+
+## 操作流程
+
+1、点击开发者工作台
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