Top 10 Pretrained Models to Get Started with Deep Learning in Computer Vision (2026)

Introduction

Deep learning has revolutionized the field of computer vision in recent years, enabling machines to understand and interpret visual information with remarkable accuracy. However, training deep learning models from scratch often requires vast amounts of labeled data and compute resources that are out of reach for many practitioners and organizations. This is where pretrained models come in.

Pretrained models are deep learning models that have already been trained on large datasets, often by technology companies or research institutions with access to extensive data and computing power. These models have learned to extract meaningful features and representations from visual data, which can then be leveraged for a wide range of downstream tasks through transfer learning or fine-tuning.

The use of pretrained models in computer vision has grown exponentially in recent years. According to a survey by researchers at Google, the number of papers on ArXiv mentioning "pretrained models" increased from just 44 in 2015 to over 1,500 in 2020 [1]. This growth has been driven by the availability of large-scale datasets like ImageNet, COCO, and Open Images, as well as the development of powerful neural network architectures like ResNet, EfficientNet, and Vision Transformers.

Pretrained models offer several key benefits for deep learning practitioners:

1. Reduced data requirements: Pretrained models have already learned general features from a large corpus of data, which can be adapted to new tasks with limited training data.
2. Improved performance: Pretrained models often achieve state-of-the-art performance on benchmark tasks and can outperform models trained from scratch on smaller datasets.
3. Faster development: Starting with a pretrained model can significantly accelerate the development process compared to training a model from scratch.
4. Accessibility: Pretrained models level the playing field by allowing practitioners without access to large datasets or compute resources to still achieve strong performance.

In this article, we‘ll explore the top 10 pretrained computer vision models that can help you get started with deep learning in 2023. We‘ll dive into the key features, architectures, and performance of each model, as well as how to start using them in popular deep learning frameworks. Let‘s get started!

1. YOLO v8

YOLO (You Only Look Once) is a family of real-time object detection models known for their speed and accuracy. The latest version, YOLO v8, was released in 2023 and builds upon previous versions with an enhanced backbone network and improved training techniques.

Key features of YOLO v8 include:

• Trained on the COCO dataset with 80 object classes
• Real-time inference with up to 100 FPS on GPU
• Anchor-free detection head for improved accuracy and simplicity
• Mosaic data augmentation for increased robustness
• Automatic optimization of model size and speed based on target device

Model	Input Size	[email protected]	Latency (ms)
YOLO v8s	640	48.5	2.9
YOLO v8m	640	52.9	5.3
YOLO v8l	640	55.0	10.6
YOLO v8x	640	57.2	17.4

Table 1. YOLO v8 model variants and performance on the COCO validation set. Latency measured on an NVIDIA A100 GPU. (Source: Ultralytics)

YOLO v8 has been used for a wide range of applications, including:

• Autonomous vehicles: Detecting and tracking pedestrians, vehicles, and road signs in real-time
• Surveillance systems: Monitoring crowds and detecting suspicious activities
• Robotics: Enabling robots to perceive and interact with objects in their environment

To get started with YOLO v8, you can use the official PyTorch implementation from Ultralytics:

from ultralytics import YOLO

# Load a pretrained model
model = YOLO(‘yolov8s.pt‘)

# Perform inference on an image
results = model(‘image.jpg‘)

# Visualize the results
results.plot()

2. Mask R-CNN

Mask R-CNN is an extension of the popular Faster R-CNN object detection architecture that adds a branch for predicting segmentation masks. This allows Mask R-CNN to not only detect and classify objects, but also precisely locate them at the pixel level.

Key features of Mask R-CNN include:

• Trained on datasets like COCO and LVIS for instance segmentation
• Two-stage architecture with region proposal network and ROI align
• Feature Pyramid Network (FPN) backbone for multi-scale feature extraction
• RoIAlign layer for precise spatial localization
• Mask head for predicting segmentation masks

Model	AP (mask)	AP (box)
Mask R-CNN R-50-FPN	34.6	37.8
Mask R-CNN R-101-FPN	36.1	39.5
Mask R-CNN X-101-FPN	37.1	41.1

Table 2. Mask R-CNN performance on the COCO instance segmentation task with different backbone networks. (Source: Detectron2)

Some interesting applications of Mask R-CNN include:

• Autonomous shopping carts that can recognize and locate individual items
• Medical image analysis for segmenting tumors and organs
• Agricultural monitoring for detecting and measuring crops and livestock

You can use Mask R-CNN with the Detectron2 library in PyTorch:

from detectron2.config import get_cfg
from detectron2.engine import DefaultPredictor

# Load config and pretrained weights
cfg = get_cfg()
cfg.merge_from_file("configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
cfg.MODEL.WEIGHTS = "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl"

# Create predictor
predictor = DefaultPredictor(cfg)

# Run inference on an image
outputs = predictor(image)

3. Vision Transformer (ViT)

The Vision Transformer (ViT) is a novel architecture that applies the self-attention mechanisms used in natural language processing to learn global dependencies and relationships in images. ViT models have achieved state-of-the-art performance on benchmarks like ImageNet classification and have shown promising results in few-shot learning and transfer learning.

Key features of ViT include:

• Trained on large-scale datasets like ImageNet-21K and JFT-300M
• Transformer encoder architecture with self-attention and multi-layer perceptrons (MLPs)
• Patch embedding to convert images into sequences of tokens
• Global receptive field at every layer
• Scalability to very large model sizes and dataset sizes

Model	Params (M)	Top-1 Acc.	Top-5 Acc.
ViT-B/16	86	77.9	93.8
ViT-B/32	88	76.5	92.7
ViT-L/16	307	76.5	93.3
ViT-L/32	306	71.2	90.5

Table 3. Vision Transformer performance on ImageNet classification with different model sizes and patch sizes. (Source: Google AI Blog)

Some promising applications of ViT include:

• Large-scale image retrieval and search
• Few-shot learning for medical image classification
• Multimodal learning with text and images

You can experiment with ViT using the timm library in PyTorch:

import timm

# Load a pretrained ViT model
model = timm.create_model(‘vit_base_patch16_224‘, pretrained=True)

# Perform inference on an image
output = model(image)

Ethical Considerations

While pretrained models are a powerful tool for democratizing deep learning, they also raise important ethical considerations around bias, fairness, and accountability. Many large-scale datasets used for pretraining, such as ImageNet and JFT-300M, have been shown to contain biases and underrepresentation of certain demographics [4].

When using pretrained models in sensitive domains like healthcare, security, or hiring, it‘s crucial to carefully validate their performance and audit them for potential biases. Techniques like subgroup analysis, bias mitigation, and interpretability can help ensure that models are fair and transparent.

Furthermore, the environmental impact of training large-scale models is significant, with some estimates suggesting that training a single large language model can emit as much carbon as five cars over their lifetimes [5]. As the scale of pretrained models continues to grow, it‘s important for researchers and practitioners to consider the environmental costs and to invest in more efficient training techniques like distillation and quantization.

Conclusion

Pretrained models are a powerful tool for democratizing deep learning and enabling rapid progress in computer vision. By leveraging the knowledge and representations learned from large-scale datasets, practitioners can achieve state-of-the-art performance on a wide range of tasks with minimal data and compute requirements.

In this article, we‘ve explored some of the most promising pretrained models for computer vision in 2023, including YOLO v8 for object detection, Mask R-CNN for instance segmentation, and Vision Transformers for image classification. We‘ve also discussed some of the ethical considerations and best practices for using these models responsibly.

As the field of computer vision continues to evolve, we can expect to see even more powerful and efficient pretrained models emerge, as well as new techniques for transfer learning, few-shot learning, and multimodal learning. By staying up-to-date with these developments and using them thoughtfully, we can push the boundaries of what‘s possible with computer vision and create applications that have a positive impact on the world.

References

[1] https://ai.googleblog.com/2021/12/democratizing-transfer-learning-with.html
[2] https://docs.ultralytics.com/
[3] https://ai.facebook.com/blog/detectron2-is-now-available-to-the-pytorch-community/
[4] https://ai.googleblog.com/2021/07/vision-transformer-vit-image.html
[5] https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/