Comments (3)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Expected image (JPEG, PNG, or GIF), got unknown format starting with '\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000'
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Suppose you generating your tfrecord data in a incorrect way.
You can reference the official tutorial of converting CIFAR-10 dataset to .tfrecord files:
https://github.com/tensorflow/models/blob/master/tutorials/image/cifar10_estimator/generate_cifar10_tfrecords.py
from squeezenet.
Suppose you generating your tfrecord data in a incorrect way.
You can reference the official tutorial of converting CIFAR-10 dataset to .tfrecord files:
https://github.com/tensorflow/models/blob/master/tutorials/image/cifar10_estimator/generate_cifar10_tfrecords.py
If you follow the official tutorial to generate your tfrecord data, some minor changes should be done in inputs.py. Some of 'image' and 'label' in function _parse_serialized_example() and _preprocess_example() should be replaced by 'image/encoded' and 'image/class/label' like:
def _preprocess_example(self, serialized_example):
parsed_example = self._parse_serialized_example(serialized_example)
image = self._preprocess_image(parsed_example['image/encoded'])
return {'image': image}, parsed_example['image/class/label']
@staticmethod
def _parse_serialized_example(serialized_example):
features = {
'image/encoded': tf.FixedLenFeature([], tf.string),
'image/class/label': tf.FixedLenFeature([], tf.int64),
}
return tf.parse_single_example(serialized=serialized_example,
features=features)
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Related Issues (13)
- Inference output determined by batch size HOT 9
- what's the proper folder structure of tfrecords?
- It doesn't work on tensorflow 1.9
- Hyperparams arguments missing HOT 2
- How to use this architecture with custom dataset HOT 5
- Imagenet pre-trained model HOT 1
- No Relu Layer in squeezenet.py ? HOT 2
- what should be the directory structure of the images? HOT 4
- ImportError: No module named slim.deployment HOT 13
- Does the training script need images only of size 224x224 ? HOT 2
- Can you include a requirements.txt file? HOT 3
- How can I retrain it with new classes and labels?
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