List of Important AI models and their usage

ResNet

Model Name:

ResNet (Residual Network)

Version:ResNet-50

Release Date:2015

Functionality:

1. Deep feature extraction
2. Skip-connection based learning
3. Prevents vanishing gradient
4. High-accuracy image classification
5. Transfer learning support

Training Data Required:

More or Less 2 lakh small images (224×224)

Suitable Epoch: 20–30

Best Fit For:

1. Orientation detection
2. Image feature comparison
3. Image arrangement logic
4. Broken image alignment
5. OCR pre-processing

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YOLO

Model Name: YOLO (You Only Look Once)

Version: YOLOv8

Release Date:2023

Functionality:

1. Real-time object detection
2. Single-shot prediction
3. Bounding box regression
4. Multi-class classification
5. Edge-device friendly

Training Data Required:1.5–2 lakh labeled images

Suitable Epoch:15–25

Best Fit For:

1. Object orientation detection
2. Image movement tracking
3. Image placement validation
4. Scene understanding
5. Robotics vision

VGG

Model Name: VGGNet

Version:VGG-16

Release Date: 2014

Functionality:

1. Deep convolution layers
2. Uniform kernel structure
3. Feature-rich embeddings
4. Easy fine-tuning
5. Strong baseline model

Training Data Required: More or Less 2 lakh medium images

Suitable Epoch:20

Best Fit For:

1. Image orientation classification
2. Texture analysis
3. Torn image reconstruction
4. Visual similarity checks
5. Dataset benchmarking

MobileNet

Model Name:MobileNet

Version:MobileNetV2

Release Date:2018

Functionality:

1. Depthwise separable convolution
2. Mobile-optimized inference
3. Low memory footprint
4. Fast training
5. Edge deployment

Training Data Required: More than 1–1.5 lakh small images

Suitable Epoch: 15–20

Best Fit For:

1. Orientation detection on mobile
2. Image movement sensing
3. Lightweight vision apps
4. IoT vision
5. Real-time scanning

EfficientNet

Model Name:EfficientNet

Version:B0

Release Date:2019

Functionality:

1. Compound scaling
2. High accuracy with fewer params
3. Efficient training
4. Adaptive feature learning
5. Cloud-ready

Training Data Required: 2 lakh images is sufficient for best performance

Suitable Epoch:20

Best Fit For:

1. Image orientation scoring
2. Document alignment
3. Smart cropping
4. Vision-based QA
5. Medical imaging

U-Net

Model Name:U-Net

Version:U-Net++

Release Date:2018

Functionality:

1. Pixel-level segmentation
2. Encoder-decoder structure
3. Skip-connections
4. Precise boundary detection
5. Noise robustness

Training Data Required: 1 lakh segmented images with good visibility and good quality images

Suitable Epoch:20–40

Best Fit For:

1. Image edge detection
2. Torn image separation
3. Document segmentation
4. Medical scans
5. Image cleanup

Siamese Network

Model Name:Siamese Network

Version:CNN-based Siamese

Release Date:2015

Functionality:

1. Similarity comparison
2. Distance learning
3. Feature matching
4. One-shot learning
5. Contrastive loss

Training Data Required: 2 lakh image pairs with good quality images

Suitable Epoch:20

Best Fit For:

1. Image arrangement
2. Piece matching
3. Orientation correction
4. Duplicate detection
5. Signature verification

AutoEncoder

Model Name:AutoEncoder

Version:Convolutional AE

Release Date:2016

Functionality:

1. Feature compression
2. Noise reduction
3. Latent representation
4. Reconstruction learning
5. Anomaly detection

Training Data Required: Around 2 lakh unlabeled images

Suitable Epoch:20–50

Best Fit For:

1. Image restoration
2. Orientation normalization
3. Noise removal
4. Pre-training pipelines
5. OCR enhancement

Transformer

Model Name:Vision Transformer (ViT)

Version:ViT-Base

Release Date:2020

Functionality:

1. Self-attention
2. Long-range dependency
3. Patch-based learning
4. High accuracy
5. Scalable architecture ** Training Data Required:** More or Less 2–3 lakh images

Suitable Epoch:20

Best Fit For:

1. Global orientation detection
2. Complex image layout
3. Scene understanding
4. Multimodal pipelines
5. Vision-language tasks

CRNN

Model Name:CRNN

Version:CNN+BiLSTM

Release Date:2015

Functionality:

1. Sequence prediction
2. OCR text recognition
3. Variable-width input
4. CTC loss decoding
5. Handwriting recognition

Training Data Required: Around 1–2 lakh labeled text images

Suitable Epoch:20–30

Best Fit For:

1. Text-guided image ordering
2. Orientation correction
3. Document reconstruction
4. OCR pipelines
5. Handwritten data

OpenPose

Model Name:OpenPose

Version:OpenPose 1.7

Release Date:2017

Functionality:

1. Human pose detection
2. Keypoint estimation
3. Multi-person tracking
4. Skeleton extraction
5. Motion analysis

Training Data Required: More or Less 2 lakh pose-labeled images

Suitable Epoch:20

Best Fit For:

1. Image movement
2. Pose-based alignment
3. Video analysis
4. Sports analytics
5. Gesture recognition

DeepLab

Model Name:DeepLab

Version:DeepLabV3+

Release Date:2018

Functionality:

1. Semantic segmentation
2. Atrous convolution
3. Context awareness
4. Fine boundary detection
5. Multi-scale learning

Training Data Required: Around 2 lakh annotated images

Suitable Epoch:20–30

Best Fit For:

1. Object placement
2. Image region separation
3. Scene parsing
4. Smart cropping
5. AR applications

GAN

Model Name:GAN

Version:DCGAN

Release Date:2016

Functionality:

1. Image generation
2. Data augmentation
3. Style learning
4. Image completion
5. Noise synthesis

Training Data Required : More or Less 2–3 lakh images

Suitable Epoch:30–50

Best Fit For:

1. Missing image reconstruction
2. Orientation correction
3. Data balancing
4. Synthetic training data
5. Visual enhancement

Here is Sample Code For Model Training using python .Most of the models are python model , other than python some model also available

Real-time vision-->C++
Enterprise AI-->Java / C#
Browser AI-->JavaScript
Mobile AI-->Swift
High-speed inference-->Rust / Go

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader

# Device selection
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Image transformations
transform = transforms.Compose([
    transforms.Resize((128, 128)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5], std=[0.5])
])

# Load datasets
train_dataset = datasets.ImageFolder("dataset/train", transform=transform)
val_dataset   = datasets.ImageFolder("dataset/val", transform=transform)

train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader   = DataLoader(val_dataset, batch_size=32, shuffle=False)

# Simple CNN model
class OrientationCNN(nn.Module):
    def __init__(self, num_classes=4):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, 3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),

            nn.Conv2d(32, 64, 3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )

        self.classifier = nn.Sequential(
            nn.Flatten(),
            nn.Linear(64 * 32 * 32, 128),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(128, num_classes)
        )

    def forward(self, x):
        x = self.features(x)
        return self.classifier(x)

model = OrientationCNN(num_classes=4).to(device)

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Training loop
epochs = 20
for epoch in range(epochs):
    model.train()
    running_loss = 0.0

    for images, labels in train_loader:
        images, labels = images.to(device), labels.to(device)

        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

    print(f"Epoch [{epoch+1}/{epochs}], Loss: {running_loss:.4f}")

# Save trained model
torch.save(model.state_dict(), "orientation_model.pth")
print("Model training complete and saved.")

Here are some popular model of other Language

Real-Time Vision → C++

OpenCV DNN – CNN inference, image processing
YOLO (C++ builds) – Object detection
TensorRT – Ultra-fast GPU inference
ONNX Runtime – Model deployment
Darknet – Original YOLO engine

Enterprise AI → Java / C#

Deeplearning4j – Neural networks
Weka – Classical ML
Apache Spark MLlib – Big-data AI

C#

ML.NET – Business AI
CNTK – Deep learning (legacy but used)

Browser AI → JavaScript

TensorFlow.js – CNN, pose, face models
Brain.js – Lightweight ML
ONNX.js – Web inference

Mobile AI → Swift

Core ML – iOS on-device AI
Vision Framework – Face & object detection
Create ML – Simple model creation

Thank You :Ayan Banerjee