When i was running python classify.py ./lussaris.jpg,
It gave ckpt-alexnet not Found Error.
How can I resolve this?

Hi
I am training ILSVRC2012 data using Alexnet Architecture, Here I am having train and validation data but I am not having the ILSVRC2012 meta.mat, ILSVRC2012_validation_ground_truth.txt. So I am using at meta.mat place I kept ILSVRC2014 meta.mat file and also I kept meta.mat ILSVRC2014_validation_ground_truth.txt

So here I am using 2012 dataset with 2014 meta.mat,ILSVRC2014_validation_ground_truth.txt. I am thinking due to this files I am not getting image tags accurately ?

Please can any one upload ILSVRC2012 meta.mat and ILSVRC2012_validation_ground_truth.txt files, I have tried but I didn't get

There is no PIL import for Python 3.6.3, according to this post, PIL is abandoned and shouldn't be used.

https://www.reddit.com/r/Python/comments/5mph2q/how_do_you_install_pil_to_python_360/

Hi Matteo,

I was wondering how was the loss progress during your training? For example, after 3 epochs loss didnt change at all for me. It is around 8.1 and not dropping.
I was wondering how much loss was for you when you finished training, and how much we can expect to be after 49 epochs?

All the best.

thanks！

I am getting following error while running your code: IOError: cannot identify image file u'/home/ImageNet/ILSVRC2012/train/n01688243/n01688243_9115.JPEG'
I got to know that some files are already corrupt in imageNet dataset. Could you please tell me how to resolve this issue.
Thanks

Hi,
I have successfully started training using ILSVRC2012 Train and Validation datasets. The training dataset contains 12,81,167 images and Validation dataset contains 50,000 images. I am running this code on Intel i5 Processor with 16GB RAM and 2TB HDD. I have kept default 90 epochs. How much time it will take to complete 90 epochs. I am expecting for one epoch may be it will take one and half day time. Can we reduce the time to complete training faster ?

Hi, I am using your code on both VGG and your original alexnet networks.
The way I manage my data is exact following your instructions, and I tried to use np_util.to_categorial() function instead, but the loss is always around 8 even after 100+ epochs, and 16 on the original code.
Do you have any idea what's the probable problem of it?
Any help is appreciated, thanks a lot!

Hi.
I am curious about the 'local response normalization' layer parameters you set.
The paper case is a little bit different from what you set except 'beta'.

What does it mean to be different from the actual setting?

Hi
While I am training Imagenet data I didn't found the below two files
meta.mat
ILSVRC2012_validation_ground_truth.txt
Can any one provide the above files

To try the new TensorFlow Eager style, I decided to implement the old scripts using it. They are contained in the tf_eager folder whereas the old code (unchanged) was moved to the tf folder.

Unfortunately, I do not have the time and the resources to train and test the new scripts on ImageNet. Anyone would like to take this? Only to check the constistency with the old ones. Thank you!

https://github.com/dontfollowmeimcrazy/imagenet/blob/9d60cf738777843b6706e4b7a62621094d6d9e23/classify.py#L34

Hi, I have trained my model using ILSVRC2012 dataset, after 10 epochs , I have tested the model with provided image I have got the following output with probabilities

python classify.py ./lussari.jpg

alp - score: 0.32444486022
valley, vale - score: 0.176716804504
monastery - score: 0.0272926297039
castle - score: 0.0269635356963
radio telescope, radio reflector - score: 0.0263287965208
stone wall - score: 0.0236576907337
dam, dike, dyke - score: 0.0211586020887
cliff, drop, drop-off - score: 0.0195624642074
church, church building - score: 0.0162345394492
ox - score: 0.0147680761293
mountain tent - score: 0.014573732391
oxcart - score: 0.013046768494
solar dish, solar collector, solar furnace - score: 0.0118312025443
bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis - score: 0.0116224717349
megalith, megalithic structure - score: 0.0104170087725
volcano - score: 0.0089854830876
Siberian husky - score: 0.0086589967832
king penguin, Aptenodytes patagonica - score: 0.00850845314562
fountain - score: 0.00850336998701
dalmatian, coach dog, carriage dog - score: 0.00831394083798

when I am comparing with calrifai I am not getting the results same as like in clarifai. How will I get same as like clarifai tags

hi,i had try my model use 90 epochs.here is my result:

but when i run test.py,i get very low accuracy,

i am so confused.i wonder know is something wrong?