update readme

README.md

 # tensorpack
 Neural Network Toolbox on TensorFlow
 
-See some [examples](examples) to learn about the framework.
-They're not only for demonstration of the framework -- you can train them and reproduce the results in papers.
-
-+ [DoReFa-Net: training binary / low bitwidth CNN on ImageNet](examples/DoReFa-Net)
-+ [ResNet for ImageNet/Cifar10/SVHN classification](examples/ResNet)
-+ [InceptionV3 on ImageNet](examples/Inception/inceptionv3.py)
-+ [Fully-convolutional Network for Holistically-Nested Edge Detection(HED)](examples/HED)
-+ [Spatial Transformer Network on MNIST addition](examples/SpatialTransformer)
-+ [Generative Adversarial Network(GAN) variants, including DCGAN, Image2Image, InfoGAN](examples/GAN)
-+ [Deep Q-Network(DQN) variants on Atari games](examples/Atari2600)
-+ [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](examples/OpenAIGym)
-+ [LSTM-CTC for speech recognition](examples/TIMIT)
-+ [char-RNN language model](examples/char-rnn)
+See some [examples](examples) to learn about the framework:
+
+### Vision:
++ [DoReFa-Net: training binary / low-bitwidth CNN on ImageNet](DoReFa-Net)
++ [ResNet for ImageNet/Cifar10/SVHN](ResNet)
++ [InceptionV3 on ImageNet](Inception/inceptionv3.py)
++ [Fully-convolutional Network for Holistically-Nested Edge Detection(HED)](HED)
++ [Spatial Transformer Networks on MNIST addition](SpatialTransformer)
+
+### Reinforcement Learning:
++ [Deep Q-Network(DQN) variants on Atari games](Atari2600)
++ [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](OpenAIGym)
+
+### Unsupervised Learning:
++ [Several Generative Adversarial Network(GAN) variants, including DCGAN, Image2Image, InfoGAN](examples/GAN)
+
+### Speech / NLP:
++ [LSTM-CTC for speech recognition](TIMIT)
++ [char-rnn for fun](char-rnn)
+
+The examples are not only for demonstration of the framework -- you can train them and reproduce the results in papers.
 
 ## Features:
 

examples/README.md

@@ -6,7 +6,7 @@ Training examples with __reproducible__ and meaningful performance.
 ## Vision:
 + [An illustrative mnist example with explanation of the framework](mnist-convnet.py)
 + [A tiny SVHN ConvNet with 97.8% accuracy](svhn-digit-convnet.py)
-+ [DoReFa-Net: binary / low-bitwidth CNN on ImageNet](DoReFa-Net)
++ [DoReFa-Net: training binary / low-bitwidth CNN on ImageNet](DoReFa-Net)
 + [ResNet for ImageNet/Cifar10/SVHN](ResNet)
 + [Inception-BN with 71% accuracy](Inception/inception-bn.py)
 + [InceptionV3 with 74% accuracy (similar to the official code)](Inception/inceptionv3.py)
@@ -24,3 +24,11 @@ Training examples with __reproducible__ and meaningful performance.
 ## Speech / NLP:
 + [LSTM-CTC for speech recognition](TIMIT)
 + [char-rnn for fun](char-rnn)
+
+
+Note to contributors:
+
+We have a high bar for examples. It needs to satisfy one of the following:
++ Reproduce performance of a published or well-known paper.
++ Get state-of-the-art performance on some task.
++ Illustrate a new way of using the library that are currently not covered.

tensorpack/dataflow/dataset/ilsvrc.py

@@ -68,6 +68,7 @@ class ILSVRCMeta(object):
             for line in f.readlines():
                 name, cls = line.strip().split()
                 ret.append((name, int(cls)))
+        assert len(ret)
         return ret
 
     def get_per_pixel_mean(self, size=None):