How to Use FasterViT for Image and video Classification

Last Updated on 30/12/2025 by Eran Feit

Introduction — fastervit image classification tutorial

A fastervit image classification tutorial introduces a powerful and efficient way to recognize visual patterns in images using modern deep learning techniques. FasterViT is a hybrid model that combines the strengths of convolutional neural networks (CNNs) with vision transformers to deliver both high accuracy and fast processing. For developers and machine learning practitioners seeking to build advanced computer vision applications, this tutorial provides a practical, hands-on path to mastering image classification with FasterViT.

In traditional image classification, convolutional neural networks have long been used to extract local visual features from images. Vision transformers, on the other hand, bring a global attention mechanism that helps models discern relationships across all parts of an image. FasterViT blends these two approaches to capture both detailed features and broad context, offering improved performance over standalone architectures. This makes it particularly useful in tasks where both fine-grained and high-level visual understanding are needed.

A fastervit image classification tutorial not only covers theoretical concepts but also practical implementation steps. Beginning with setting up the Python environment and installing necessary libraries, the tutorial walks through loading a pre-trained FasterViT model and preparing input images for analysis. Learners then run inference to obtain prediction scores, convert those scores to class probabilities, and extract the top predicted categories. These systematic steps help bridge the gap between code execution and model interpretation.

Moreover, the principles learned from image classification can be extended to video classification, where each frame of a video is treated as an image and processed sequentially. By doing so, FasterViT can classify actions, scenes, or objects in motion, enabling applications such as real-time video analytics and intelligent surveillance. This multidimensional utility reinforces FasterViT’s importance as a versatile tool in the computer vision landscape.

Exploring Your fastervit image classification tutorial in Depth

Taking a closer look at a fastervit image classification tutorial reveals both architectural insights and practical workflows. At a conceptual level, FasterViT leverages convolutional blocks to extract hierarchical features and vision transformer layers to model global relationships. This dual mechanism equips the model with a broad understanding of visual patterns that is essential for accurate classification outcomes.

Implementing a fastervit image classification tutorial begins with installing and configuring the development environment. Learners install PyTorch and related packages, define the image preprocessing pipeline, and load a pre-trained FasterViT model. These steps establish the foundation for processing images in a consistent and model-compatible format. Proper preprocessing ensures that the input data matches the dimensions and normalization schemes expected by the FasterViT architecture.

Once set up, the next phase of the tutorial focuses on running the model on sample images. The tutorial demonstrates how to feed images into the model, obtain raw output scores, and apply a softmax function to convert those scores into interpretable probabilities. Extracting the top predicted classes gives a clear view of the model’s decision, helping learners validate and understand the predictions produced by FasterViT.

Extending the tutorial to video classification adds an extra layer of complexity and real-world relevance. By reading video data frame by frame and performing image classification on each frame, learners see how FasterViT can be applied to dynamic visual content. Annotating the video with predicted class labels and displaying these results in real time highlights FasterViT’s capability to handle diverse visual tasks, making it an invaluable skillset for anyone working in modern computer vision.

How FasterViT Works for Image Classification

The pipeline starts with two convolution layers that both run with a stride of 2. These layers downsample the image while extracting local visual patterns such as edges, textures, and shapes. By the time the data leaves these layers, the spatial resolution has been reduced, but the channel dimension — which represents learned features — has increased.

From there, the model progresses through a sequence of stages. The first two stages are built from standard convolutional blocks, each repeated multiple times (shown as ×N₁ and ×N₂). These blocks deepen the feature extraction process, allowing the model to detect increasingly complex patterns and structures within the image. Between stages, downsampling modules further reduce the spatial size while expanding the representational capacity. This gradual compression is intentional: it makes the model more efficient while preserving the most useful visual information.

The architecture becomes even more interesting in Stages 3 and 4. Here, the convolutional blocks are replaced by Hierarchical Attention modules. These blocks introduce transformer-style attention into the network, allowing the model to understand long-range dependencies — in other words, how distant parts of the image relate to each other. This combination of convolution early on and attention later is what gives FasterViT its hybrid strength: CNN layers are excellent at local feature extraction, while transformers excel at global reasoning. The “CT init” elements near these stages refer to initialization strategies designed to stabilize training when attention layers are introduced.

Finally, after the last hierarchical attention stage and a final downsampling step, the processed feature map flows into the model “Head.” This final component typically includes pooling and classification layers that convert the learned features into probabilities over the output classes. The diagram nicely communicates how resolution shrinks across the pipeline while channels expand, symbolizing the model’s shift from raw visual data to abstract, high-level semantic understanding. It’s a clean visual summary of the workflow you’d explore in a fastervit image classification tutorial.

Understanding FasterViT Performance: Accuracy vs. Throughput

This chart presents a clear visual comparison between model accuracy and throughput, showing how FasterViT performs relative to other well-known vision transformer and CNN-transformer hybrid architectures. Throughput here refers to the number of images a model can process per second — a direct indicator of speed and efficiency — while accuracy reflects Top-1 ImageNet performance. The goal is to highlight how well each model balances raw predictive power with practical runtime performance.

The blue line represents the FasterViT family, ranging from FasterViT-0 up to FasterViT-5. As you move up the series, accuracy increases gradually while throughput decreases, which is expected — larger models tend to be more accurate but slightly slower. What stands out is how far to the right these blue markers are placed compared to competing models. Even the mid-range FasterViT-2 and FasterViT-3 variants deliver higher throughput at comparable or better accuracy than models like Swin, ConvNeXt, and EfficientNetV2.

The inset table reinforces this comparison numerically. For example, FasterViT-2 achieves a Top-1 accuracy of 84.2% with a throughput of 3161 images per second, while FasterViT-3 reaches 84.9% at 1780 images per second. These numbers outperform many popular alternatives at the same accuracy level, demonstrating the strength of the architecture’s hybrid design. Models such as ConvNeXt and Swin appear noticeably clustered to the left, showing significantly lower throughput for similar accuracy.

Overall, the image communicates a compelling message: FasterViT is designed not only for accuracy, but also for speed. This makes it especially attractive for real-world deployments where latency, efficiency, and scalability matter — such as edge devices, real-time analytics, and large-scale inference systems. When viewed alongside a fastervit image classification tutorial, this chart helps connect architecture design choices with measurable performance benefits.

Link to the video tutorial : https://youtu.be/eoi9YprVvnw

You can download the code here : https://eranfeit.lemonsqueezy.com/checkout/buy/81f3b096-a086-4dec-83d1-1dd58bae2154 or here : https://ko-fi.com/s/49ad50f0c9

Link to the post for Medium users : https://medium.com/@feitgemel/how-to-use-fastervit-for-image-and-video-classification-5cd5688fc5fb

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 Want to get started with Computer Vision or take your skills to the next level ?

Great Interactive Course : “Deep Learning for Images with PyTorch” here : https://datacamp.pxf.io/zxWxnm

If you’re just beginning, I recommend this step-by-step course designed to introduce you to the foundations of Computer Vision – Complete Computer Vision Bootcamp With PyTorch & TensorFlow

If you’re already experienced and looking for more advanced techniques, check out this deep-dive course – Modern Computer Vision GPT, PyTorch, Keras, OpenCV4

Building a Practical FasterViT Tutorial for Image and Video Classification

This section focuses on the hands-on side of the fastervit image classification tutorial — the actual code you run to make FasterViT classify images and videos using PyTorch. The goal of the code is simple but powerful: load a pretrained FasterViT model, preprocess your input images or video frames, run inference, and then read the top predicted class results in a clean and understandable way. Instead of just talking about theory, the tutorial shows how to take a real image or video file, pass it through the model, and see what FasterViT thinks it contains.

The first part of the code prepares the working environment. You install PyTorch with CUDA support, add the FasterViT package, and import everything you need — like torchvision transforms, PIL for image handling, and the FasterViT model loader. Once the model is created and set to evaluation mode, the code defines a preprocessing pipeline that resizes, crops, normalizes, and batches the image. This ensures the input matches the expected format of the pretrained model. With just a few lines, the raw image is converted into a tensor that the network can understand.

After preprocessing, the tutorial moves into inference. The model runs on either CPU or GPU depending on availability, and gradients are disabled for efficiency. The output tensor contains raw class scores, which are converted into probabilities using a softmax function. The code retrieves the top-K predictions and maps them to human-readable class names. This makes the workflow very practical — you not only see numerical outputs, but also meaningful labels like “dog,” “airplane,” or “sports car,” along with the model’s confidence.

The video classification part of the code extends the same logic to moving images. Each frame of the video is read with OpenCV, preprocessed, fed into FasterViT, and overlaid with the predicted label in real time. This demonstrates how the same model used for static images can be integrated into streaming or live-processing pipelines. Altogether, the tutorial code gives you a complete end-to-end workflow: from installation, to image preprocessing, to inference, to readable results — making FasterViT both accessible and practical for real-world classification tasks.

How to Use FasterViT for Image and Video Classification

A complete FasterViT Image Classification Tutorial using PyTorch

A powerful vision model like FasterViT opens the door to high-performance image and video classification using modern hybrid deep learning techniques.

In this tutorial, we walk step-by-step through the practical Python code that loads a pre-trained FasterViT model, preprocesses input, performs inference, and outputs predictions both on static images and video frames.

FasterViT blends convolutional feature extraction with transformer-style attention, giving you both speed and accuracy — ideal for real-world applications that require efficient and intelligent vision systems.

Below you’ll find the code broken into meaningful parts with explanations and ready-to-copy code blocks.

Setting Up the Environment for FasterViT

Before writing any inference code, we need a Python environment with the right libraries installed.

This part of the tutorial shows how to create a conda environment and install PyTorch with CUDA support, which enables GPU acceleration if available.

Once the environment is prepared, you install the FasterViT package and Python dependencies such as timm, matplotlib, and OpenCV.

This setup ensures the rest of the code runs smoothly on both image and video input.

### Create a new conda environment named fasterVit with Python 3.11 conda create -n fasterVit python=3.11 ### Activate the fasterVit environment conda activate fasterVit   ### Check the CUDA version installed on the system nvcc --version  ### Install PyTorch 2.5.0 with CUDA 12.4 support conda install pytorch==2.5.0 torchvision==0.20.0 torchaudio==2.5.0 pytorch-cuda=12.4 -c pytorch -c nvidia  ### Install FasterViT and supporting libraries pip install fastervit==0.9.8 pip install timm==0.9.12 pip install matplotlib pip opencv-python==4.10.0.84 

In this part we prepare the development environment to work with FasterViT.
We install PyTorch with GPU support and all necessary libraries to run the subsequent code.
After this setup, you will have all dependencies to load models and process images and videos.

Loading and Preprocessing Input for Image Classification

In this section we load a pre-trained FasterViT model and prepare the image preprocessing pipeline.

The goal is to resize and normalize an input image so the model can interpret it correctly.

We define a PyTorch transform that resizes the image, crops it to square dimensions, and normalizes with standard mean and standard deviation values.

Then we load an image from disk and convert it into a tensor that FasterViT expects.

The test images :

### Import PyTorch and image transform modules import torch ### Import transformation tools from torchvision from torchvision import transforms ### Import PIL image library from PIL import Image ### Import FasterViT create_model function from fastervit import create_model  ### Define the FasterVit-0 model with 224x224 input size and 1000 classes model = create_model("faster_vit_0_224",                      pretrained=True,                      model_path="d:/temp/models/faster_vit_0.pth.tar")  ### Set the model to evaluation mode for inference model.eval()  ### Define the image preprocessing steps with resize, crop, tensor conversion and normalization preprocess = transforms.Compose([     transforms.Resize(256),     transforms.CenterCrop(224),     transforms.ToTensor(),     transforms.Normalize(mean=[0.485, 0.456, 0.406],                          std=[0.229, 0.224, 0.225]), ])  ### Load an image from the file system image_path = "Visual-Language-Models-Tutorials/FasterViT - Image classification using Fast Vision Transformers/Basketball.jpg" ### Open the image img = Image.open(image_path)  ### Preprocess the opened image input_tensor = preprocess(img) ### Add a batch dimension to the tensor input_batch = input_tensor.unsqueeze(0) 

After running this code, the model is ready to process real input.
The image is converted into the right tensor format for PyTorch and FasterViT.
This step prepares the image for the model’s inference process.

Performing Inference and Interpreting Results

Now that the model and input are ready, we perform inference and convert the raw output scores into human-readable class labels and probabilities.

In this part, we move the model and input to either GPU or CPU, run the model on the input image, apply softmax to get probabilities, and list the top predictions.

Then we download class labels and prepare a dictionary to map predicted indices to readable names.

### Move model to the appropriate device (GPU if available, otherwise CPU) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") ### Move the model to the selected device model.to(device) ### Move the input batch to the same device input_batch = input_batch.to(device)  ### Run the model without computing gradients with torch.no_grad():     output = model(input_batch)  ### Print the raw output for all 1000 ImageNet classes print("Output for 1000 classes: ") print(output)  ### Convert raw output into probabilities prob = torch.nn.functional.softmax(output[0], dim=0)  ### Get the top 5 class probabilities and indices top5_prob , top5_catid = torch.topk(prob, 5) for i in range(top5_prob.size(0)):     print(f"Category: {top5_catid[i]}, Probability: {top5_prob[i].item()}")  ### Download class names from online source import json  import requests response = requests.get(url) class_names = response.text.splitlines()  ### Print the class names list print(class_names)  ### Map class indices to class names class_idx = {i: class_names[i] for i in range(len(class_names))}  ### Save the class index mappings to a file with open("d:/temp/imagenet_class_index.json", "w") as f:     json.dump(class_idx, f) 

This part runs inference on a single image.
We convert model outputs into interpretable class probabilities and map them to class names.
You now see exactly which classes the model predicts with what confidence.

Using Results and Visualization

After computing class scores and mapping them to labels, we print the top predictions with class names and probabilities for clarity.

Then we display the image with the predicted class label overlaid directly on the image using matplotlib.

### Load the image and display with matplotlib from PIL import Image  import matplotlib.pyplot as plt  ### Show the image without axes plt.imshow(img) plt.axis('off')  ### Get the class with the highest probability top_prob , top_catid = torch.topk(prob, 1) predicted_class = top_catid[0].item() ### Find the class name predicted_class_name = idx_to_labels[str(predicted_class)] ### Get the top probability probability = top_prob[0].item()  ### Overlay the predicted class name and probability on the image plt.text(20,20, f"Predicted: {predicted_class_name} ({probability:.4f})", color='white', backgroundcolor='black', fontsize=12, bbox=dict(facecolor='black', alpha=0.5))  ### Show the final result plt.show() 

Now the model’s top prediction is not only printed, but also shown on the image.
This visual feedback makes it easy to verify whether the prediction matches your expectations.

Extending to Video Classification

This final part adapts the image classification workflow to real-time processing of video frames.

We load a video file with OpenCV, preprocess each frame, feed it into the model, and overlay predicted class labels and probabilities on every frame in a display window.

You can find the video file as part of the code here : https://eranfeit.lemonsqueezy.com/checkout/buy/81f3b096-a086-4dec-83d1-1dd58bae2154, or you can send me email : feitgemel@feitgemel

### Import OpenCV for handling video import cv2  video_path = "Visual-Language-Models-Tutorials/FasterViT - Image classification using Fast Vision Transformers/Airplane.mp4" cap = cv2.VideoCapture(video_path)  ### Check if the video opened successfully if not cap.isOpened():     print("Error : Could not open video ....")     exit()  ### Set desired output video size output_width = 640  output_height = 480  ### Process each frame in the video while cap.isOpened():     ret , frame = cap.read()     if not ret:         break       ### Resize the frame     frame = cv2.resize(frame , (output_width, output_height))      ### Convert frame to PIL image     img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))      ### Apply preprocessing      input_tensor = preprocess(img)     input_batch = input_tensor.unsqueeze(0)      ### Move batch to the processing device     input_batch = input_batch.to(device)      ### Run the model     with torch.no_grad():         output = model(input_batch)      ### Get predicted class     probs = torch.nn.functional.softmax(output[0], dim=0)      top_prob , top_catid = torch.topk(probs, 1)     predicted_class = top_catid[0].item()     predicted_class_name = idx_to_labels[str(predicted_class)]     probability = top_prob[0].item()      ### Annotate the frame with class name and probability     cv2.putText(frame, f'Class: {predicted_class_name}', (10,30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2)     cv2.putText(frame, f'Probability: {probability}', (10,70), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2)      ### Display the frame     cv2.imshow("Video Classification ", frame)      if cv2.waitKey(1) & 0xFF == ord('q'):         break  ### Release video capture and close windows cap.release() cv2.destroyAllWindows() 

This code demonstrates how FasterViT can classify each frame in a video stream.
It turns FasterViT from a static image classifier into a real-time video recognition tool, annotating each frame with predictions.

📌 FAQ — Key Concepts & Practical Tips

Conclusion

In this post you learned how to run a complete FasterViT image classification tutorial using PyTorch.
We prepared the environment, loaded a pre-trained model, preprocessed input, ran inference, and interpreted predictions for both static images and video streams.
By going step-by-step, this code becomes approachable and practical — ready to integrate into real projects.
FasterViT’s hybrid architecture lets you balance accuracy and speed, giving you a tool capable of handling a wide range of classification tasks.

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