All Projects β†’ prakashjayy β†’ Pytorch_classifiers

prakashjayy / Pytorch_classifiers

Licence: gpl-3.0
Almost any Image classification problem using pytorch

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to Pytorch classifiers

LegoBrickClassification
Repository to identify Lego bricks automatically only using images
Stars: ✭ 57 (-53.28%)
Mutual labels:  image-classification, transfer-learning
Deep-Learning
It contains the coursework and the practice I have done while learning Deep Learning.πŸš€ πŸ‘¨β€πŸ’»πŸ’₯ 🚩🌈
Stars: ✭ 21 (-82.79%)
Mutual labels:  image-classification, transfer-learning
super-gradients
Easily train or fine-tune SOTA computer vision models with one open source training library
Stars: ✭ 429 (+251.64%)
Mutual labels:  image-classification, transfer-learning
backprop
Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.
Stars: ✭ 229 (+87.7%)
Mutual labels:  image-classification, transfer-learning
Artificio
Deep Learning Computer Vision Algorithms for Real-World Use
Stars: ✭ 326 (+167.21%)
Mutual labels:  image-classification, transfer-learning
deep-learning
Projects include the application of transfer learning to build a convolutional neural network (CNN) that identifies the artist of a painting, the building of predictive models for Bitcoin price data using Long Short-Term Memory recurrent neural networks (LSTMs) and a tutorial explaining how to build two types of neural network using as input the…
Stars: ✭ 43 (-64.75%)
Mutual labels:  image-classification, transfer-learning
Keras-Application-Zoo
Reference implementations of popular DL models missing from keras-applications & keras-contrib
Stars: ✭ 31 (-74.59%)
Mutual labels:  image-classification, transfer-learning
Cvpr18 Inaturalist Transfer
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning. CVPR 2018
Stars: ✭ 164 (+34.43%)
Mutual labels:  image-classification, transfer-learning
Assembled Cnn
Tensorflow implementation of "Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network"
Stars: ✭ 319 (+161.48%)
Mutual labels:  image-classification, transfer-learning
Autogluon
AutoGluon: AutoML for Text, Image, and Tabular Data
Stars: ✭ 3,920 (+3113.11%)
Mutual labels:  image-classification, transfer-learning
Skin Lesions Classification DCNNs
Transfer Learning with DCNNs (DenseNet, Inception V3, Inception-ResNet V2, VGG16) for skin lesions classification
Stars: ✭ 47 (-61.48%)
Mutual labels:  image-classification, transfer-learning
Imageclassification
Deep Learning: Image classification, feature visualization and transfer learning with Keras
Stars: ✭ 83 (-31.97%)
Mutual labels:  image-classification, transfer-learning
Transfer Learning Suite
Transfer Learning Suite in Keras. Perform transfer learning using any built-in Keras image classification model easily!
Stars: ✭ 212 (+73.77%)
Mutual labels:  image-classification, transfer-learning
favorite-research-papers
Listing my favorite research papers πŸ“ from different fields as I read them.
Stars: ✭ 12 (-90.16%)
Mutual labels:  image-classification, transfer-learning
Imageatm
Image classification for everyone.
Stars: ✭ 201 (+64.75%)
Mutual labels:  image-classification, transfer-learning
FaceClassification Tensorflow
Building a Neural Network that classifies faces using OpenCV and Tensorflow
Stars: ✭ 37 (-69.67%)
Mutual labels:  image-classification, transfer-learning
Image classifier
CNN image classifier implemented in Keras Notebook πŸ–ΌοΈ.
Stars: ✭ 139 (+13.93%)
Mutual labels:  image-classification, transfer-learning
Hub
A library for transfer learning by reusing parts of TensorFlow models.
Stars: ✭ 3,007 (+2364.75%)
Mutual labels:  image-classification, transfer-learning
Rexnet
Official Pytorch implementation of ReXNet (Rank eXpansion Network) with pretrained models
Stars: ✭ 319 (+161.48%)
Mutual labels:  image-classification, transfer-learning
Keras transfer cifar10
Object classification with CIFAR-10 using transfer learning
Stars: ✭ 120 (-1.64%)
Mutual labels:  image-classification, transfer-learning

This is an experimental setup to build code base for pytorch. Its main is to experiment faster using transfer learning on all available pre-trained models.

Dataset: Plant Seedlings classification

Classes present:

  • Black-grass
  • Charlock
  • Cleavers
  • Common Chickweed
  • Common wheat
  • Fat Hen
  • Loose Silky-bent
  • Maize
  • Scentless Mayweed
  • Shepherds Purse
  • Small-flowered Cranesbill
  • Sugar beet

Run the following commands

To train the models:

make -f Makefile.train

To test the models:

make -f Makefile.predict

Results

total number of train images: 4268 total number of val images: 482 total number of test images: 794

Results with Full-Agumentation strategy:

Trained the networks in three methods:

  • Full Finetuning
  • Freeze first few layers

Case-1 - Finetuning entire network

Models Train Accuracy_score Val Accuracy_score
resnet18 0.92783 0.93153
resnet34 0.9522 0.94190
resnet50 0.95665 0.94398
resnet101 0.96696 0.96265
resnet152 0.96555 0.95643
squeezenet1_0 0.94329 0.92738
squeezenet1_1 0.93955 0.93153
densenet121 0.95243 0.9336
densenet169 0.96626 0.93983
densenet201 0.96063 0.95020
inception_v3 0.94212 0.93568
vgg11 0.93814 0.93153
vgg13 0.94493 0.94190
vgg16 0.95665 0.93568
vgg19 0.95009 0.93775
vgg11_bn 0.94142 0.93775
vgg13_bn 0.94423 0.92738
vgg16_bn 0.94634 0.94190
vgg19_bn 0.94915 0.93360
alexnet 0.91260 0.90456
resnext101_64x4d 0.98055 0.96887
resnext101_32x4d 0.98172 0.96887
nasnetalarge 0.96907 0.96265
inceptionresnetv2 0.96134 0.95435
inceptionv4 0.96930 0.96473

Case-2 - Freezed first few layers(look at code)

Models Train Accuracy_score Val Accuracy_score
resnet18 0.9196 0.91493
resnet34 0.94845 0.93983
resnet50 0.9564 0.93983
resnet101 0.96790 0.96265
resnet152 0.96508 0.95643
squeezenet1_0 0.94048 0.92738
squeezenet1_1 0.93088 0.92116
densenet121 0.95173 0.95228
densenet169 0.96087 0.94813
densenet201 0.95384 0.95020
inception_v3 0.94025 0.93775
vgg11 0.93697 0.92946
vgg13 0.93533 0.92323
vgg16 0.94821 0.93983
vgg19 0.95243 0.94190
vgg11_bn 0.93416 0.92738
vgg13_bn 0.93322 0.92323
vgg16_bn 0.94728 0.93775
vgg19_bn 0.94798 0.94190
alexnet 0.89784 0.88589
resnext101_64x4d 0.98617 0.96887
resnext101_32x4d 0.98195 0.96473
nasnetalarge 0.95970 0.96265
inceptionresnetv2 0.95103 0.94813
inceptionv4 0.96251 0.93775

Results with Basic Agumentation

Case-1 (Freezed all layers except last one)

Models Train Accuracy_score Val Accuracy_score
resnet18 0.77553 0.75518
resnet152 0.82778 0.81535
resnet101 0.82333 0.80290
resnet50 0.79943 0.78630
resnet34 0.78655 0.74688
squeezenet1_0 0.91447 0.87966
squeezenet1_1 0.90089 0.87344
densenet121 0.80880 0.81120
densenet169 0.84746 0.82987
densenet201 0.86621 0.86514
inception_v3 0.76101 0.74688
vgg11 0.78209 0.78008
vgg13 0.75960 0.72821
vgg16 0.77038 0.71576
vgg19 0.71204 0.64522
vgg11_bn 0.76522 0.74481
vgg13_bn 0.76241 0.76348
vgg16_bn 0.76265 0.75726
vgg19_bn 0.75773 0.73858
alexnet 0.83153 0.76348

Case-2 Finetuning the entire network

Models Train Accuracy_score Val Accuracy_score
resnet18 0.98477 0.96058
resnet152 0.99273 0.97717
resnet101 0.99367 0.97717
resnet50 0.99133 0.97510
resnet34 0.98969 0.97095
squeezenet1_0 0.96274 0.94190
squeezenet1_1 0.96485 0.92738
densenet121 0.99086 0.96887
densenet169 0.99507 0.97510
densenet201 0.99390 0.97717
inception_v3 0.98898 0.97302
vgg11 0.98031 0.95020
vgg13 0.98078 0.95643
vgg16 0.98266 0.95435
vgg19 0.98430 0.95643
vgg11_bn 0.98570 0.96265
vgg13_bn 0.98687 0.97095
vgg16_bn 0.99179 0.96680
vgg19_bn 0.99297 0.96680
alexnet 0.95970 0.92946

Submissions:

  • densenet201 LB - to 97.22
  • ensemble1 - mode of all best performing models LB - 97.32

TO_DO

  1. Adding mixup strategy to all the networks
  2. Ensembling model outputs
  3. Model stacking
  4. Extracting bottleneck features and using - ML to train the model - Visualization using T-sne
  5. Solve issue with bninception(Model is not training)
  6. Train Vggm network
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].