标签:rgb hub 特征 dba mon one model art input
说明:使用图像分类经典模型resnet18进行蜜蜂/蚂蚁图像分类。如下图为不同resnet的结构:
推理基本步骤:
获取数据与模型
数据变换,如RGB → 4D-Tensor
前向传播
输出保存预测结果
Inference阶段注意事项:
确保 model处于eval状态而非training
设置torch.no_grad(),减少内存消耗
数据预处理需保持一致,RGB o rBGR?
输入:任意大小图片(含有蜜蜂或蚂蚁)
输出:图像+描述
input shape:(800, 534)
time:0.033s
input shape:(500, 375)
time:0.032s
input shape:(422, 500)
time:0.038s
input shape:(2592, 1944)
time:0.050s
input shape:(500, 375)
time:0.032s
input shape:(500, 477)
time:0.029s
input shape:(399, 300)
time:0.063s
input shape:(366, 500)
time:0.036s
input shape:(348, 500)
time:0.034s
input shape:(500, 375)
time:0.036s
网络结构:生成器(由100维随机向量生成64*64*3图片)如图所,判别器则相反(由图片卷积到单一输出,表示real/fake),详见DCGAN结构解读
数据项目:http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
输入:
输出:通过网格图生成num_img个图像,每张图大小均为64*64 + 运行时间
num_img = 16
nrow = 4
noise_continue = False
time: 0.430
num_img = 16
nrow = 4
noise_continue = True
time: 0.388
num_img = 64
nrow = 8
noise_continue = False
time: 0.430
num_img = 64
nrow = 8
noise_continue = True
time: 0.430
问题描述:判断图像中目标的位置
目标检测两要素
分类:分类向量[p0, …, pn]
回归:回归边界框[x1, y1, x2, y2]
网络结构:详见Faster RCNN
Faster RCNN 数据流:
Feature map: [256, h_f, w_f]
2 Softmax:[num_anchors, h_f, w_f]
Regressors:[num_anchors*4, h_f, w_f]
NMS OUT: [n_proposals=2000, 4]
ROI Layer: [512, 256, 7, 7]
FC1 FC2: [512, 1024]
c+1 Softmax: [512, c+1]
Regressors: [512, (c+1)*4]
目标检测推荐github: https://github.com/amusi/awesome-object-detection
输入:任意大小图片
输出:探测图 + 描述
input img tensor shape:torch.Size([3, 624, 1270])
time: 3.468s
labels: [‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘handbag‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘handbag‘, ‘backpack‘, ‘person‘, ‘person‘, ‘person‘, ‘backpack‘, ‘backpack‘, ‘handbag‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘handbag‘, ‘person‘, ‘person‘, ‘handbag‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘handbag‘, ‘person‘, ‘handbag‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘handbag‘, ‘person‘, ‘person‘, ‘person‘, ‘backpack‘, ‘person‘, ‘person‘, ‘handbag‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘backpack‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘handbag‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘handbag‘, ‘person‘]
scores: [0.9861, 0.985, 0.978, 0.9777, 0.977, 0.9735, 0.9488, 0.9457, 0.9451, 0.9073, 0.8724, 0.8721, 0.8539, 0.8525, 0.8394, 0.8078, 0.7983, 0.7772, 0.7599, 0.7478, 0.7289, 0.7089, 0.6798, 0.6635, 0.6635, 0.6547, 0.6513, 0.6507, 0.6486, 0.638, 0.6087, 0.6002, 0.5873, 0.5866, 0.5681, 0.5653, 0.5568, 0.5563, 0.5497, 0.5367, 0.527, 0.5192, 0.5159, 0.4952, 0.4536, 0.4505, 0.4493, 0.4458, 0.4407, 0.4389, 0.4288, 0.4184, 0.4058, 0.3983, 0.3913, 0.3878, 0.3873, 0.3766, 0.3633, 0.3479, 0.347, 0.345, 0.3419, 0.3407, 0.3377, 0.3356, 0.3273, 0.3202, 0.3139, 0.3098, 0.2969, 0.2954, 0.2953, 0.2906, 0.281, 0.2802, 0.28, 0.2781, 0.2758, 0.2721, 0.2699, 0.2681, 0.2658, 0.2643, 0.2599, 0.2526, 0.2497, 0.2455, 0.237, 0.236, 0.2311, 0.2309, 0.2292, 0.2239, 0.221, 0.2201, 0.2136, 0.211, 0.2063, 0.1997]
input img tensor shape:torch.Size([3, 433, 649])
time: 3.446s
labels: [‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘frisbee‘, ‘backpack‘, ‘person‘, ‘frisbee‘, ‘handbag‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘backpack‘, ‘person‘, ‘backpack‘, ‘baseball glove‘, ‘person‘, ‘handbag‘, ‘cell phone‘, ‘person‘, ‘baseball glove‘, ‘handbag‘, ‘person‘, ‘skateboard‘, ‘person‘, ‘handbag‘, ‘person‘, ‘bench‘, ‘tie‘, ‘person‘, ‘person‘, ‘person‘, ‘baseball glove‘, ‘baseball glove‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘car‘, ‘person‘, ‘baseball glove‘, ‘person‘, ‘baseball glove‘]
scores: [0.9989, 0.9984, 0.998, 0.998, 0.9958, 0.9945, 0.9927, 0.987, 0.9841, 0.977, 0.9709, 0.9694, 0.9575, 0.95, 0.9377, 0.916, 0.9138, 0.8392, 0.7031, 0.6829, 0.6816, 0.4896, 0.4661, 0.4086, 0.3937, 0.3934, 0.3471, 0.2957, 0.2796, 0.2519, 0.2344, 0.2308, 0.1834, 0.1582, 0.1572, 0.1476, 0.1428, 0.1385, 0.1196, 0.118, 0.1108, 0.1054, 0.1012, 0.0976, 0.0959, 0.0937, 0.0873, 0.0868, 0.0854, 0.0828, 0.0781, 0.0731, 0.0726, 0.0721, 0.0701, 0.0691, 0.0663, 0.0632, 0.0616, 0.0612, 0.0592, 0.0589, 0.057, 0.0559, 0.0552, 0.0551, 0.0531, 0.0516, 0.0511]
input img tensor shape:torch.Size([3, 404, 435])
time: 2.437s
labels: [‘person‘, ‘person‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘person‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘person‘, ‘handbag‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘person‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘motorcycle‘, ‘bicycle‘, ‘bicycle‘, ‘backpack‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘car‘, ‘person‘, ‘bicycle‘, ‘skateboard‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘stop sign‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘, ‘bicycle‘]
scores: [0.9997, 0.9995, 0.9962, 0.9865, 0.976, 0.9674, 0.9191, 0.8953, 0.8745, 0.8735, 0.8365, 0.8203, 0.8016, 0.7921, 0.7511, 0.7246, 0.7214, 0.6532, 0.6327, 0.616, 0.6086, 0.5796, 0.5732, 0.5494, 0.534, 0.532, 0.5016, 0.4768, 0.451, 0.4438, 0.4215, 0.3844, 0.3572, 0.3446, 0.3433, 0.3162, 0.3137, 0.2468, 0.241, 0.2177, 0.1947, 0.1934, 0.1751, 0.1486, 0.1417, 0.1263, 0.1187, 0.1187, 0.1149, 0.1145, 0.1071, 0.1051, 0.0987, 0.0983, 0.0895, 0.0845, 0.0837, 0.081, 0.0783, 0.0767, 0.0707, 0.0703, 0.0697, 0.0605, 0.0574, 0.0573, 0.0536]
input img tensor shape:torch.Size([3, 438, 567])
time: 3.521s
labels: [‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘bicycle‘, ‘backpack‘, ‘person‘, ‘bicycle‘, ‘backpack‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘backpack‘, ‘backpack‘, ‘bicycle‘, ‘handbag‘, ‘person‘, ‘person‘, ‘bicycle‘, ‘person‘, ‘person‘, ‘person‘, ‘handbag‘, ‘bicycle‘, ‘person‘, ‘tennis racket‘, ‘backpack‘, ‘backpack‘, ‘person‘, ‘bicycle‘, ‘person‘, ‘handbag‘, ‘handbag‘, ‘person‘, ‘potted plant‘, ‘handbag‘, ‘person‘, ‘handbag‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘chair‘, ‘bicycle‘, ‘person‘, ‘person‘, ‘person‘, ‘bicycle‘, ‘person‘, ‘potted plant‘, ‘person‘, ‘backpack‘, ‘person‘, ‘motorcycle‘, ‘person‘, ‘handbag‘, ‘bicycle‘, ‘handbag‘, ‘person‘, ‘backpack‘, ‘handbag‘, ‘bicycle‘, ‘backpack‘, ‘tie‘, ‘backpack‘, ‘person‘, ‘bicycle‘, ‘person‘, ‘backpack‘, ‘bicycle‘, ‘person‘, ‘motorcycle‘]
scores: [0.9992, 0.9977, 0.9966, 0.9948, 0.9912, 0.9906, 0.9861, 0.9855, 0.9731, 0.9693, 0.9678, 0.9662, 0.959, 0.9394, 0.936, 0.8802, 0.8656, 0.8219, 0.819, 0.7878, 0.7354, 0.6874, 0.6777, 0.6356, 0.4796, 0.4632, 0.4269, 0.4146, 0.326, 0.2994, 0.2711, 0.2325, 0.2219, 0.2054, 0.1846, 0.184, 0.1698, 0.1687, 0.1613, 0.1577, 0.1575, 0.1533, 0.1526, 0.1486, 0.1432, 0.1426, 0.1304, 0.1222, 0.1209, 0.1139, 0.1126, 0.1122, 0.1026, 0.0974, 0.097, 0.091, 0.0883, 0.0853, 0.0791, 0.0781, 0.0754, 0.0664, 0.0656, 0.0647, 0.0631, 0.0625, 0.0602, 0.0578, 0.0562, 0.053, 0.0513, 0.051, 0.0505, 0.0504]
input img tensor shape:torch.Size([3, 399, 683])
time: 4.199s
labels: [‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘person‘, ‘handbag‘, ‘potted plant‘, ‘potted plant‘, ‘person‘, ‘potted plant‘, ‘person‘, ‘person‘, ‘handbag‘, ‘backpack‘, ‘person‘, ‘handbag‘, ‘person‘, ‘handbag‘, ‘handbag‘, ‘person‘, ‘person‘, ‘potted plant‘, ‘handbag‘, ‘backpack‘, ‘person‘, ‘backpack‘, ‘potted plant‘, ‘person‘, ‘person‘, ‘person‘, ‘tie‘, ‘person‘, ‘handbag‘, ‘backpack‘, ‘potted plant‘, ‘handbag‘, ‘handbag‘, ‘potted plant‘, ‘person‘, ‘potted plant‘, ‘backpack‘, ‘bicycle‘, ‘handbag‘, ‘potted plant‘, ‘chair‘, ‘potted plant‘, ‘potted plant‘, ‘potted plant‘, ‘potted plant‘, ‘handbag‘, ‘backpack‘, ‘tie‘, ‘person‘, ‘backpack‘, ‘potted plant‘, ‘dining table‘, ‘potted plant‘, ‘bench‘, ‘handbag‘, ‘tie‘, ‘potted plant‘, ‘handbag‘, ‘backpack‘, ‘potted plant‘, ‘backpack‘, ‘potted plant‘, ‘backpack‘, ‘potted plant‘]
scores: [0.9997, 0.9991, 0.9985, 0.9807, 0.9731, 0.9043, 0.8989, 0.8985, 0.8772, 0.6565, 0.5354, 0.5205, 0.4876, 0.4571, 0.421, 0.4015, 0.3939, 0.3668, 0.3612, 0.3448, 0.3254, 0.3165, 0.29, 0.2851, 0.2605, 0.2525, 0.2439, 0.2414, 0.2204, 0.2136, 0.1969, 0.196, 0.1757, 0.1695, 0.1635, 0.1608, 0.1571, 0.1536, 0.15, 0.145, 0.1413, 0.1404, 0.1312, 0.1172, 0.1164, 0.1025, 0.1023, 0.0899, 0.0888, 0.0879, 0.087, 0.0822, 0.081, 0.0757, 0.0748, 0.0738, 0.0729, 0.0709, 0.0707, 0.0681, 0.0663, 0.0648, 0.0627, 0.0588, 0.0567, 0.0531, 0.0525, 0.052]
问题描述:将人物与背景相分离,逐像素的二分类问题
网络结构:详见u-net
输入:任意大小图片
输出:分割图 + 时间
time: 0.735s
time: 0.415s
time: 0.471s
问题:将图像中每个像素分类为21类(‘__background__‘, ‘aeroplane‘, ‘bicycle‘, ‘bird‘, ‘boat‘, ‘bottle‘, ‘bus‘, ‘car‘, ‘cat‘, ‘chair‘, ‘cow‘, ‘diningtable‘, ‘dog‘, ‘horse‘, ‘motorbike‘, ‘person‘, ‘pottedplant‘, ‘sheep‘, ‘sofa‘, ‘train‘, ‘tvmonitor‘)的一种
特点:逐像素分类,无法区分个体
模型如何完成图像分割?图像分割由模型(将数据映射到特征)与人类(定义特征的物理意义,解决实际问题)配合完成
输入:一张任意大小的图片
输出:语义分割图 + 描述
网络结构:详见deeplabv3,网络结构图如下
input img tensor shape:torch.Size([1, 3, 433, 649])
output img tensor shape:torch.Size([21, 433, 649])
time: 4.205s
input img tensor shape:torch.Size([1, 3, 433, 649])
output img tensor shape:torch.Size([21, 433, 649])
time: 4.534s
input img tensor shape:torch.Size([1, 3, 730, 574])
output img tensor shape:torch.Size([21, 730, 574])
time: 6.472s
标签:rgb hub 特征 dba mon one model art input
原文地址:https://www.cnblogs.com/NAG2020/p/12902820.html