TensorFlow implementation of AlexNet and its training and testing on ImageNet ILSVRC 2012 dataset

Matteo Dunnhofer

Last update: Nov 25, 2022

Related tags

Deep Learning deep-learning tensorflow imagenet alexnet convolutional-neural-networks tensorflow-eager

Overview

AlexNet training on ImageNet LSVRC 2012

This repository contains an implementation of AlexNet convolutional neural network and its training and testing procedures on the ILSVRC 2012 dataset, all using TensorFlow.

Folder tf contains code in the "classic TensorFlow" framework whereas code in the tf_eager directory has been developed with TensorFlow's new impearative style, TensorFlow eager.

The two implementations are independent and refer to the READMEs inside the folders for specific instruction on how to train and to test.

References

Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Inforamtion Processing Systems 25, 2012.
Olga Russakovsky°, Jia Deng°, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei. (° = equal contribution) ImageNet Large Scale Visual Recognition Challenge. IJCV, 2015

Comments

model testing

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

opened by rakashi 13
loss progress

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.

opened by Sh0lim 9
Estimate Training Time

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 ?

opened by rakashi 4
Can PIL be replaced with 'pillow' ?

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/

opened by jefflomax 2
Please Upload ILSVRC2012 meta.mat,ILSVRC2012_validation_ground_truth.txt

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

opened by rakashi 2
IOError: cannot identify image file u'/home/ImageNet/ILSVRC2012/train/n01688243/n01688243_9115.JPEG'

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

opened by sakshi404 1
Wonder about lrn parameters.

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?

opened by sicnarf1a 1
Upload Imagenet text files (provide links to get the text files)

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

opened by rakashi 1
test model get very low accuracy

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?

opened by iodncookie 8
loss didn't get down

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!

opened by chutongz 2
TF eager version training and testing

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!
help wanted

opened by matteo-dunnhofer 1

Owner

Matteo Dunnhofer

A bear experimenting AI.

GitHub

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TensorFlow implementation of AlexNet and its training and testing on ImageNet ILSVRC 2012 dataset

Related tags

Overview

AlexNet training on ImageNet LSVRC 2012

References

Comments

model testing

loss progress

Estimate Training Time

Can PIL be replaced with 'pillow' ?

Please Upload ILSVRC2012 meta.mat,ILSVRC2012_validation_ground_truth.txt

IOError: cannot identify image file u'/home/ImageNet/ILSVRC2012/train/n01688243/n01688243_9115.JPEG'

Wonder about lrn parameters.

Upload Imagenet text files (provide links to get the text files)

test model get very low accuracy

loss didn't get down

TF eager version training and testing

Owner

Matteo Dunnhofer

PyTorch implementation of CVPR 2020 paper (Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence) and pre-trained model on ImageNet dataset

Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)

A PyTorch Image-Classification With AlexNet And ResNet50.

NFT-Price-Prediction-CNN - Using visual feature extraction, prices of NFTs are predicted via CNN (Alexnet and Resnet) architectures.

Reproduction process of AlexNet

Pytorch implementation of "Training a 85.4% Top-1 Accuracy Vision Transformer with 56M Parameters on ImageNet"

Neural Magic Eye: Learning to See and Understand the Scene Behind an Autostereogram, arXiv:2012.15692.

We have implemented shaDow-GNN as a general and powerful pipeline for graph representation learning. For more details, please find our paper titled Deep Graph Neural Networks with Shallow Subgraph Samplers, available on arXiv (https//arxiv.org/abs/2012.01380).

Source Code for DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances (https://arxiv.org/pdf/2012.01775.pdf)

My personal code and solution to the Synacor Challenge from 2012 OSCON.

Code for our method RePRI for Few-Shot Segmentation. Paper at http://arxiv.org/abs/2012.06166

Third party Pytorch implement of Image Processing Transformer (Pre-Trained Image Processing Transformer arXiv:2012.00364v2)

A set of simple scripts to process the Imagenet-1K dataset as TFRecords and make index files for NVIDIA DALI.

Python scripts for performing object detection with the 1000 labels of the ImageNet dataset in ONNX.

Code of PVTv2 is released! PVTv2 largely improves PVTv1 and works better than Swin Transformer with ImageNet-1K pre-training.

A complete, self-contained example for training ImageNet at state-of-the-art speed with FFCV

CIFAR-10_train-test - training and testing codes for dataset CIFAR-10

Implementation of Restricted Boltzmann Machine (RBM) and its variants in Tensorflow

This is the official source code for SLATE. We provide the code for the model, the training code, and a dataset loader for the 3D Shapes dataset. This code is implemented in Pytorch.