There are a number of recent pretrained models available in TensorFlow-Slim Research for which users can download and finetune to other datasets, or, evaluate for classification tasks. However, there was no AlexNetin the list and this repo helps you reproduce that for ImageNet dataset. I also provide a pretrained model captured at 9 million iterations with Top-5 accuracy of 79.85% and a Top-1 accuracy of 56.8% for those who doesn't want to train it from scracth.
If you have optimized the training hyperparameters and managed to converge to a higher accuracy, please share your code here in the comment section bellow, so other can benefit from it as well.
Here is the Conv and FC layers of AlexNet with their corresponding parameter and shape size:
#AlexNet Layer's reference #Shape #Params
LAYER=['alexnet_v2/conv1/weights:0', # shape=(11, 11, 3, 64) 23 232
'alexnet_v2/conv2/weights:0', # shape=(5, 5, 64, 192) 307 200
'alexnet_v2/conv3/weights:0', # shape=(3, 3, 192, 384) 663 552
'alexnet_v2/conv4/weights:0', # shape=(3, 3, 384, 384) 1 327 104
'alexnet_v2/conv5/weights:0', # shape=(3, 3, 384, 256) 884 736
'alexnet_v2/fc6/weights:0', # shape=(5, 5, 256, 4096) 26 214 400
'alexnet_v2/fc7/weights:0', # shape=(1, 1, 4096, 4096) 16 777 216
'alexnet_v2/fc8/weights:0'] # shape=(1, 1, 4096, 1001) 4 100 096
#Total= 50 297 536
The definition of the AlexNet_v2 is available at here.
In order to do a tf.slim way of training alexnet_v2 with imagenet, you need to have a preprocessing.py file located in models/slim/preprocessing. There is also a preprocessing_factory.py file that maps the specific preprocessing file for a specific model and you need to add a line to tell TF to use the one you want for AlexNet.
I have tried to implement them from scracth, but empirically found lower inference accuracy with respect to using inception_preprocessing. Thus, for this gist we stick to that for alexNet. In order to do that, just add a line where preprocessing_fn_map defines as:
preprocessing_fn_map = {
'alexnet_v2': inception_preprocessing, # Needs to be added
'cifarnet': cifarnet_preprocessing,
'inception': inception_preprocessing,
'inception_v1': inception_preprocessing,
'inception_v2': inception_preprocessing,
'inception_v3': inception_preprocessing,
'inception_v4': inception_preprocessing,
'inception_resnet_v2': inception_preprocessing,
'lenet': lenet_preprocessing,
'mobilenet_v1': inception_preprocessing,
'resnet_v1_50': vgg_preprocessing,
'resnet_v1_101': vgg_preprocessing,
'resnet_v1_152': vgg_preprocessing,
'resnet_v1_200': vgg_preprocessing,
'resnet_v2_50': vgg_preprocessing,
'resnet_v2_101': vgg_preprocessing,
'resnet_v2_152': vgg_preprocessing,
'resnet_v2_200': vgg_preprocessing,
'vgg': vgg_preprocessing,
'vgg_a': vgg_preprocessing,
'vgg_16': vgg_preprocessing,
'vgg_19': vgg_preprocessing,
}
At this point you are good to strat training alexnet_v2 as per another tf.slim model, since you both have the architecture definition and the preprocessing:
$ python train_image_classifier.py --train_dir=amir-alexnet-v2-results-oct2018/trained --dataset_name=imagenet --dataset_split_name=train --dataset_dir=${DATASET_DIR} --model_name=alexnet_v2
This uses mostly default hyperparameters as:
---batch_size=32
--learning_rate=0.01
--end_learning_rate=0.0001
--num_epochs_per_decay=2.0
You can easily evaluate the accuracy by executing this:
$ python eval_image_classifier.py --alsologtostderr --checkpoint_path=${CHECKPOINT_FILE} --dataset_dir=${DATASET_DIR} --dataset_name=imagenet --dataset_split_name=validation --model_name=alexnet_v2
, and here is the result:
2019-01-09 11:22:00.396964: I tensorflow/core/kernels/logging_ops.cc:79] eval/Accuracy[0.56828]
2019-01-09 11:22:00.403138: I tensorflow/core/kernels/logging_ops.cc:79] eval/Recall_4[0.7755]
2019-01-09 11:22:00.403139: I tensorflow/core/kernels/logging_ops.cc:79] eval/Recall_3[0.742]
2019-01-09 11:22:00.403139: I tensorflow/core/kernels/logging_ops.cc:79] eval/Recall_5[0.79854]
INFO:tensorflow:Finished evaluation at 2019-01-09-16:22:00
For those who wants to use the preatrained model, I have uploaded the model files as AlexNet_Pretrained. The .zip file contains:
model.ckpt-9048119.data-00000-of-00001
model.ckpt-9048119.index
model.ckpt-9048119.meta