Comments (2)
If anyone is interested in this, the metrics that I got on the test set using the labels provided for it on Stanford's site, are a precision of .8922 and recall of .8887 which are right in line with the accuracy measure.
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Thanks @BaruchG
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Related Issues (20)
- Issue while running demo HOT 1
- Getting Wrong Results HOT 1
- Unable to sort images HOT 1
- License Information HOT 1
- Error on running demo HOT 4
- Trained model HOT 1
- How to add own car?
- Image not found HOT 3
- How to eradicate error from analyze code?
- Typo in analyze.py HOT 1
- Testing accuracy HOT 2
- OutOfMemory issue on Jetson Nano HOT 1
- Negative dimension size caused by subtracting 3 from 2 for '{{node max_pooling2d/MaxPool}} = MaxPool[T=DT_FLOAT, data_format="NHWC", ksize=[1, 3, 3, 1], padding="VALID", strides=[1, 2, 2, 1]](activation/Relu)' with input shapes: [?,2,112,64]. HOT 15
- Convert into tflite HOT 2
- model.prediect() is too slow when runing demo.py HOT 9
- Train.py (i am getting import issue)
- tf and keras versions
- 大佬,您这个模型是不是不包含国产车呀? HOT 1
- IndentationError: expected an indented block
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