Comments (3)
Both the paper and the model evolved over time, both in terms of the layer names and the layer structure. The layer structure you see in the paper is the same you see in ssd300.py
, some layers are just named differently than in the paper. Layers conv8
- conv11
in the figure in the paper are the same as layers conv6
- conv9
in ssd300.py
.
The reason for this is that the naming in the latest version of the paper is not consistent with the naming in the actual prototxt
that defines the model, and I followed the naming in the prototxt
file, i.e. what you see in ssd300.py
accurately reflects the original Caffe implementation. In other words, what matters is not the figure in paper, but the Caffe model implementation file.
Regarding the pool3
layer, it too is parameterized exactly according to the original Caffe implementation of SSD300. The padding of all pooling layers is valid
except for pool5
.
I recommend you download the original Caffe model and take a look at deploy.prototxt
to convince yourself that the model definitions are identical.
from ssd_keras.
Thanks! you are totally right
from ssd_keras.
Anytime!
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Related Issues (20)
- InvalidArgumentError when compiling model with ssd_loss HOT 1
- WARNING:tensorflow:Gradients do not exist for variables ['conv4_3/bias:0',...] when minimizing the loss. HOT 1
- "Invalid argument: Index out of range using input dim 0; input has only 0 dims" during ssd300 model training
- load weight
- ValueError: Error when checking input: expected input_3 to have 4 dimensions, but got array with shape
- While training I got training terminate error . Epoch 00001: LearningRateScheduler setting learning rate to 0.001. 1/10 [==>...........................] - ETA: 4:08 - loss: nanBatch 0: Invalid loss, terminating training Epoch 00001: saving model to ssd512_URPC2018_epoch-01.h5 Process finished with exit code 0
- ValueError: An operation has `None` for gradient. Please make sure that all of your ops have a gradient defined (i.e. are differentiable). Common ops without gradient: K.argmax, K.round, K.eval.
- ValueError: Layer model expects 1 input(s), but it received 2 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, None, None, None) dtype=uint8>, <tf.Tensor 'IteratorGetNext:1' shape=(None, None, None) dtype=float32>] HOT 23
- Parameters of the model HOT 1
- Bouding boxes predictions are concentrated in left top corner HOT 1
- Ambiguous dimension while trying to load weights.
- Urgent!! Invalid Loss HOT 4
- What are the requirements to run this code?. HOT 1
- Pascal VOC Training Person Detection
- The device being used is CPU while capturing image from webcam. How do I use my GPU for processing instead?
- Label error during Coco Training HOT 1
- TypeError: Expected any non-tensor type, got a tensor instead.
- Changes make the code work in 2023 HOT 2
- custom SSD300 model
- error while training with custom dataset in COCO format
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