Comments (3)
Hi @emipasat,
- The model is already using imagenet weights. You need to read about default params of keras applications, for VGG16.
- The model is already working to train not only the last layers but all the layers.
from tf-faster-rcnn.
Sorry for not being clear. My question was if is worth to freeze the backbone layers and to train only the ones added by you, the last ones.
Would it work that, too?
Thank you
from tf-faster-rcnn.
You could give it a try. However, in my experience, fine-tuning generally gives better results than transfer learning.
from tf-faster-rcnn.
Related Issues (20)
- HOW to train my own dataset? HOT 1
- ValueError: Dimension 2 in both shapes must be equal, but are 4 and 1. Shapes are [?,1500,4] and [?,1500,1]. for '{{node roi_deltas/Select_1}} = Select[T=DT_FLOAT](roi_deltas/ExpandDims_9, roi_deltas/GatherV2_1, roi_deltas/zeros_like_1)' with input shapes: [?,1500,1], [?,1500,4], [?,1500,4].
- No such file or directory
- Training strategy HOT 1
- Erorr in reg_loss HOT 13
- 请问作者,voc2007和voc2012数据集训练时是怎么存放的?
- 怎么指定自己的VOC数据集
- ValueError: The two structures don't have the same sequence length. Input structure has length 0, while shallow structure has length 9. HOT 3
- Is there a trained checkpoint or weight h5 file?
- Question about epochs and learning rate selection
- Predict Error HOT 1
- Data Request
- variances in train_utils.py
- Bounding box resizing in preprocessing??
- Error occurred when finalizing GeneratorDataset iterator
- 数据集下载 HOT 3
- ValueError: The two structures don't have the same sequence length. Input structure has length 0, while shallow structure has length 9. HOT 3
- ValueError: The two structures don't have the same sequence length. Input structure has length 0, while shallow structure has length 9. HOT 1
- Does it support multi-GPU training? HOT 1
- ValueError: Dimension 2 in both shapes must be equal, but are 4 and 1. Shapes are [?,1500,4] and [?,1500,1]. for 'roi_deltas/Select' (op: 'Select') with input shapes: [?,1500,1], [?,1500,4], [?,1500,4]. HOT 1
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from tf-faster-rcnn.