- https://github.com/DataXujing/EfficientDet_pytorch
- https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch
我们将数据存放在项目的dataset下：
..
└─underwater
├─Annotations #xml标注
└─JPEGImages #jpg原图
# 首先划分训练集和验证集：我们采用9:1的随机换分，划分好的数据等待转化为COCO数据
划分训练集和验证集后的数据结构：
..
├─train
│ ├─Annotations
│ └─JPEGImages
└─val
├─Annotations
└─JPEGImages
将VOC转COCO：
python voc2coco.py train.txt ./train/Annotations instances_train.json ./train/JPEGImages
python voc2coco.py val.txt ./val/Annotations instances_val.json ./val/JPEGImages
# 生成的json文件存放在了dataset/underwater/annotations/*.jpg
1.新建dataset文件夹用以存放训练和验证数据
dataset
└─underwater # 项目数据集名称
├─annotations # instances_train.json,instances_val.json
├─train # train jpgs
└─val # val jpgs
2.新建logs文件夹
logs存放了训练过程中的tensprboardX保存的日志及模型
3.修改train.py[训练使用]
def get_args():
parser.add_argument('-p', '--project', type=str, default='underwater', help='project file that contains parameters')
parser.add_argument('--batch_size', type=int, default=16, help='The number of images per batch among all devices')
4.修改efficientdet_test.py[测试新图像使用]
# obj_list = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
# 'fire hydrant', '', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
# 'cow', 'elephant', 'bear', 'zebra', 'giraffe', '', 'backpack', 'umbrella', '', '', 'handbag', 'tie',
# 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',
# 'skateboard', 'surfboard', 'tennis racket', 'bottle', '', 'wine glass', 'cup', 'fork', 'knife', 'spoon',
# 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut',
# 'cake', 'chair', 'couch', 'potted plant', 'bed', '', 'dining table', '', '', 'toilet', '', 'tv',
# 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
# 'refrigerator', '', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier',
# 'toothbrush']
obj_list = ["holothurian","echinus","scallop","starfish"]# 换成自己的
compound_coef = 2 # D0-D6
model.load_state_dict(torch.load("./logs/underwater/efficientdet-d2_122_38106.pth")) # 模型地址
5.修改coco_eval.py[评估模型使用]
ap.add_argument('-p', '--project', type=str, default='underwater', help='project file that contains parameters')
6.修改efficientdet/config.py
# COCO_CLASSES = ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat",
# "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog",
# "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella",
# "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite",
# "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle",
# "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange",
# "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant",
# "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
# "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors",
# "teddy bear", "hair drier", "toothbrush"]
COCO_CLASSES = ["holothurian","echinus","scallop","starfish"]
7.新建yml配置文件(./projects/underwater.yml)[训练的配置文件]
project_name: underwater # also the folder name of the dataset that under data_path folder
train_set: train
val_set: val
num_gpus: 1
# mean and std in RGB order, actually this part should remain unchanged as long as your dataset is similar to coco.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
# this is coco anchors, change it if necessary
anchors_scales: '[2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)]'
anchors_ratios: '[(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]'
# must match your dataset's category_id.
# category_id is one_indexed,
# for example, index of 'car' here is 2, while category_id of is 3
obj_list: ["holothurian","echinus","scallop","starfish"]
# 从头训练自己的数据集 EfficientDet-D2
python train.py -c 2 --batch_size 16 --lr 1e4
# train efficientdet-d2 在自己的数据集上使用预训练的模型(推荐)
python train.py -c 2 --batch_size 8 --lr 1e-5 --num_epochs 10 \
--load_weights /path/to/your/weights/efficientdet-d2.pth
# with a coco-pretrained, you can even freeze the backbone and train heads only
# to speed up training and help convergence.
python train.py -c 2 --batch_size 8 --lr 1e-5 --num_epochs 10 \
--load_weights /path/to/your/weights/efficientdet-d2.pth \
--head_only True
# Early stopping
#Ctrl+c,
# the program will catch KeyboardInterrupt
# and stop training, save current checkpoint.
# 断点训练
python train.py -c 2 --batch_size 8 --lr 1e-5 \
--load_weights last \
--head_only True
1.评估模型使用coco的map
python coco_eval.py -p underwater -c 2 -w ./logs/underwater/efficientdet-d2_122_38106.pth
# 评价结果
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.381
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.714
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.368
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.170
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.351
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.426
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.149
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.433
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.464
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.267
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.429
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.507
2.训练过程中的Debug
# when you get bad result, you need to debug the training result.
python train.py -c 2 --batch_size 8 --lr 1e-5 --debug True
# then checkout test/ folder, there you can visualize the predicted boxes during training
# don't panic if you see countless of error boxes, it happens when the training is at early stage.
# But if you still can't see a normal box after several epoches, not even one in all image,
# then it's possible that either the anchors config is inappropriate or the ground truth is corrupted.
3.推断新的图像
python efficientdet_test.py