Comments (5)
Thanks a lot for great materials here :) I changed the model from SSD7 to SSD300 and set image width and height to 300 as in the original code. Then, retraining the SSD300 model was done well even after I restarted the Keras program. Please, have a nice day!
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The input image size got messed up at some point, which results in different spatial dimensions in all layers of the network, in particular of the predictions
layer. I don't know how exactly you arrived at this point, but 10316 is exactly the number of boxes that the model outputs with the default settings in train_ssd7.ipynb
with the preset image size of (300, 480)
.
I realize that it is not ideal that the predictor_sizes
variable is hard-coded in the code cell in section 1. If you change the image size, the predictor sizes will also change, so the way the notebook is currently set up is error-prone. I'll change that.
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The potential source of this problem has been fixed in the latest commits and it should now no longer occur, at least not if you follow the instructions in the notebook.
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Thanks a lot for your kind help and great materials here ^_^ I will always keep your suggestion above in mind :) Please, have a nice day!
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I'll close this for now.
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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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