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CNN_Classifications Alternatives
Similar projects and alternatives to CNN_Classifications
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Lottery_Ticket_Hypothesis-TensorFlow_2
Implementing "The Lottery Ticket Hypothesis" paper by "Jonathan Frankle, Michael Carbin"
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
NOTE:
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Hence, a higher number means a better CNN_Classifications alternative or higher similarity.
CNN_Classifications reviews and mentions
Posts with mentions or reviews of CNN_Classifications.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-03-09.
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Object Localization from scratch TF2
Object localization trained from scratch for emoji dataset in TensorFlow 2.8. Getting an IoU = 0.5969 and classification output accuracy = 100%. The code can be referred here. Though in fairness, I am using only 9 classes out of the emoji dataset. Thoughts?
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ResNets PyTorch CIFAR-10
I have trained ResNet-18, ResNet-18 (dropout), ResNet-34 and ResNet-50 from scratch using He weights initializations and other SOTA practices and their implementations in Python 3.8 and PyTorch 1.8. ResNet-18/34 has a different architecture as compared to ResNet-50/101/152 due to bottleneck as specified by Kaiming He et al. in their research paper.
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VGG-18 PyTorch
You can access the code here and here. According to some research papers, for deep learning architectures, using SGD vs. Adam optimizer leads to faster convergence.
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PyTorch Pruning
I have implemented "Unstructured Global absolute magnitude" pruning using "torch.nn.utils.prune" with LeNet-5 trained on MNIST with iterative pruning. You can refer to the code here.
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Learning Rate: Decay, Warmup & Scheduler
While trying to implement different SOTA research papers, I had a roadblock in terms of finding working code for different learning rate: decay, warmup & schedules (piece-wise decay, step decay, exponential decay, etc.). Therefore, I created a Jupyter Notebook implementing these concepts using TensorFlow 2.4, Python3.8 specifically for custom training loops with tf.GradientTape since most of the tutorials/blogs only show "model.fit()" method. The code can be accessed here.
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ResNet from scratch - ImageNet
You can refer to my experiments here
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ResNet-18 vs ResNet-34
Have a look at the code for manual early stopping using LeNet-300-100 FC Neural Network for MNIST dataset and let me know your thoughts.
I have trained ResNet-18 and ResNet-34 from scratch using PyTorch on CIFAR-10 dataset. The validation accuracy I get for ResNet-18 is 84.01%, whereas for ResNet-34 is 82.43%. Is this a sign of ResNet-34 overfitting as compared to ResNet-18? Ideally, ResNet-34 should achieve a higher validation accuracy as compared to ResNet-18.
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ResNet-18 from scratch
I have implemented ResNet-18 CNN from scatch in Python and PyTorch using CIFAR-10 dataset. You can see it here.
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ValueError: TensorFlow2 Input 0 is incompatible with layer model
I am trying to code a ResNet CNN architecture based on the paper by using Python3, TensorFlow2 and CIFAR-10 dataset. You can access the Jupyter notebook here.
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Stats
Basic CNN_Classifications repo stats
12
6
6.4
4 months ago
The primary programming language of CNN_Classifications is Jupyter Notebook.
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