<p>the learning rate of your model is poor.You are certainly over-fitting it. <a href="https://i.stack.imgur.com/k79do.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/k79do.png" alt="this is what you should expect."></a></p>

<p>some changes are:
1.do cross-validation first 
2.try removing some features.
3.make sure your labels are coded right</p>

<p>There could be multiple things which might be wrong. Its hard to tell.
for reference I can give you the architecture.</p>

<pre><code>import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from layers import *
from data import voc, coco
import os

class SSD(nn.Module):
"""Single Shot Multibox Architecture
The network is composed of a base VGG network followed by the
added multibox conv layers.  Each multibox layer branches into
    1) conv2d for class conf scores
    2) conv2d for localization predictions
    3) associated priorbox layer to produce default bounding
       boxes specific to the layer's feature map size.
See: https://arxiv.org/pdf/1512.02325.pdf for more details.
Args:
    phase: (string) Can be "test" or "train"
    size: input image size
    base: VGG16 layers for input, size of either 300 or 500
    extras: extra layers that feed to multibox loc and conf layers
    head: "multibox head" consists of loc and conf conv layers
"""

def __init__(self, phase, size, base, extras, head, num_classes):
    super(SSD, self).__init__()
    self.phase = phase
    self.num_classes = num_classes
    self.cfg = (coco, voc)[num_classes == 21]
    self.priorbox = PriorBox(self.cfg)
    self.priors = Variable(self.priorbox.forward(), volatile=True)
    self.size = size

    # SSD network
    self.vgg = nn.ModuleList(base)
    # Layer learns to scale the l2 normalized features from conv4_3
    self.L2Norm = L2Norm(512, 20)
    self.extras = nn.ModuleList(extras)

    self.loc = nn.ModuleList(head[0])
    self.conf = nn.ModuleList(head[1])

    if phase == 'test':
        self.softmax = nn.Softmax(dim=-1)
        self.detect = Detect(num_classes, 0, 200, 0.01, 0.45)

def forward(self, x):
    """Applies network layers and ops on input image(s) x.
    Args:
        x: input image or batch of images. Shape: [batch,3,300,300].
    Return:
        Depending on phase:
        test:
            Variable(tensor) of output class label predictions,
            confidence score, and corresponding location predictions for
            each object detected. Shape: [batch,topk,7]
        train:
            list of concat outputs from:
                1: confidence layers, Shape: [batch*num_priors,num_classes]
                2: localization layers, Shape: [batch,num_priors*4]
                3: priorbox layers, Shape: [2,num_priors*4]
    """
    sources = list()
    loc = list()
    conf = list()

    # apply vgg up to conv4_3 relu
    for k in range(23):
        x = self.vgg[k](x)

    s = self.L2Norm(x)
    sources.append(s)

    # apply vgg up to fc7
    for k in range(23, len(self.vgg)):
        x = self.vgg[k](x)
    sources.append(x)

    # apply extra layers and cache source layer outputs
    for k, v in enumerate(self.extras):
        x = F.relu(v(x), inplace=True)
        if k % 2 == 1:
            sources.append(x)

    # apply multibox head to source layers
    for (x, l, c) in zip(sources, self.loc, self.conf):
        loc.append(l(x).permute(0, 2, 3, 1).contiguous())
        conf.append(c(x).permute(0, 2, 3, 1).contiguous())

    loc = torch.cat([o.view(o.size(0), -1) for o in loc], 1)
    conf = torch.cat([o.view(o.size(0), -1) for o in conf], 1)
    if self.phase == "test":
        output = self.detect(
            loc.view(loc.size(0), -1, 4),                   # loc preds
            self.softmax(conf.view(conf.size(0), -1,
                         self.num_classes)),                # conf preds
            self.priors.type(type(x.data))                  # default boxes
        )
    else:
        output = (
            loc.view(loc.size(0), -1, 4),
            conf.view(conf.size(0), -1, self.num_classes),
            self.priors
        )
    return output

def load_weights(self, base_file):
    other, ext = os.path.splitext(base_file)
    if ext == '.pkl' or '.pth':
        print('Loading weights into state dict...')
        self.load_state_dict(torch.load(base_file,
                             map_location=lambda storage, loc: storage))
        print('Finished!')
    else:
        print('Sorry only .pth and .pkl files supported.')



def vgg(cfg, i, batch_norm=False):
    layers = []
    in_channels = i
    for v in cfg:
        if v == 'M':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        elif v == 'C':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)]
        else:
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            if batch_norm:
                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
            else:
                layers += [conv2d, nn.ReLU(inplace=True)]
            in_channels = v
    pool5 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
    conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6)
    conv7 = nn.Conv2d(1024, 1024, kernel_size=1)
    layers += [pool5, conv6,
               nn.ReLU(inplace=True), conv7, nn.ReLU(inplace=True)]
    return layers


def add_extras(cfg, i, batch_norm=False):
    # Extra layers added to VGG for feature scaling
    layers = []
    in_channels = i
    flag = False
    for k, v in enumerate(cfg):
        if in_channels != 'S':
            if v == 'S':
                layers += [nn.Conv2d(in_channels, cfg[k + 1],
                           kernel_size=(1, 3)[flag], stride=2, padding=1)]
            else:
                layers += [nn.Conv2d(in_channels, v, kernel_size=(1, 3)[flag])]
            flag = not flag
        in_channels = v
    return layers


def multibox(vgg, extra_layers, cfg, num_classes):
    loc_layers = []
    conf_layers = []
    vgg_source = [21, -2]
    for k, v in enumerate(vgg_source):
        loc_layers += [nn.Conv2d(vgg[v].out_channels,
                                 cfg[k] * 4, kernel_size=3, padding=1)]
        conf_layers += [nn.Conv2d(vgg[v].out_channels,
                        cfg[k] * num_classes, kernel_size=3, padding=1)]
    for k, v in enumerate(extra_layers[1::2], 2):
        loc_layers += [nn.Conv2d(v.out_channels, cfg[k]
                                 * 4, kernel_size=3, padding=1)]
        conf_layers += [nn.Conv2d(v.out_channels, cfg[k]
                                  * num_classes, kernel_size=3, padding=1)]
    return vgg, extra_layers, (loc_layers, conf_layers)


base = {
    '300': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M',
            512, 512, 512],
    '512': [],
}
extras = {
    '300': [256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256],
    '512': [],
}
mbox = {
    '300': [4, 6, 6, 6, 4, 4],  # number of boxes per feature map location
    '512': [],
}


def build_ssd(phase, size=300, num_classes=21):
    if phase != "test" and phase != "train":
        print("ERROR: Phase: " + phase + " not recognized")
        return
    if size != 300:
        print("ERROR: You specified size " + repr(size) + ". However, " +
              "currently only SSD300 (size=300) is supported!")
        return
    base_, extras_, head_ = multibox(vgg(base[str(size)], 3),
                                     add_extras(extras[str(size)], 1024),
                                     mbox[str(size)], num_classes)
    return SSD(phase, size, base_, extras_, head_, num_classes)
</code></pre>
What is a good learning rate graph to gauge training peformance
<p>I am <strong>training from scratch</strong> an SSD based object detection network. I am training on 250,000 images. The dataset has a skew for some classes, but I have decent representation for minority classes as well (2000 or so).</p>

<p>I see that the model is not training well, with 150k steps, it has only reached 8% precision and 25% recall. The learning rate is also not a smooth graph. What kind of learning rate graph should I expect and what other things I can try to improve my training? </p>

<pre><code>  optimizer {
    rms_prop_optimizer {
      learning_rate {
        exponential_decay_learning_rate {
          initial_learning_rate: 0.004000000189989805
          decay_steps: 800720
          decay_factor: 0.949999988079071
        }
      }
      momentum_optimizer_value: 0.8999999761581421
      decay: 0.8999999761581421
      epsilon: 1.0
    }
  }
</code></pre>

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