Resnet50.a1 in1k

Table of Contents

Model Overview

The ResNet50 A1 IN1K model is a powerful image classification model that uses a ResNet-B architecture. It features ReLU activations, a single layer 7x7 convolution with pooling, and a 1x1 convolution shortcut downsample. This model was trained on the ImageNet-1k dataset using the LAMB optimizer with BCE loss and a cosine LR schedule with warmup.

Tasks and rank

Image Recognition 9th

Classification 6th

Learn More About the Ranking System

Capabilities

This model can be used for image classification tasks. It can be fine-tuned for specific tasks by adding a new classification layer on top of the pre-trained model.

• Image Classification: The model can classify images into different categories with high accuracy.
• Feature Map Extraction: The model can extract feature maps from images, which can be used for other tasks such as object detection and segmentation.
• Image Embeddings: The model can generate image embeddings, which can be used for tasks such as image similarity search and clustering.

Strengths

• High Accuracy: The model has high accuracy on image classification tasks, especially when trained on large datasets such as ImageNet.
• Efficient: The model is relatively efficient compared to other image classification models, making it suitable for deployment on a variety of devices.
• Flexible: The model can be fine-tuned for specific tasks and datasets, making it a versatile tool for a wide range of applications.

Unique Features

• ReLU Activations: The model uses ReLU activations, which help to improve the stability and efficiency of the model.
• Single Layer 7x7 Convolution with Pooling: The model uses a single layer 7x7 convolution with pooling, which helps to reduce the spatial dimensions of the input data and increase the number of channels.
• 1x1 Convolution Shortcut Downsample: The model uses a 1x1 convolution shortcut downsample, which helps to reduce the spatial dimensions of the input data and increase the number of channels.

Alternatives

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Performance

ResNet50 A1 IN1K showcases remarkable performance in image classification tasks, offering a balance between speed and accuracy.

• Speed: The model processes images at a rate of 4.1 GMACs (Giga Multiply-Accumulate Operations per second), which is relatively fast compared to other models.
• Accuracy: ResNet50 A1 IN1K achieves an accuracy of 83.46% on the ImageNet-1k dataset, which is a standard benchmark for image classification models.

Example Code

from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'))
model = timm.create_model('resnet50.a1_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)

Examples

Classify the image from the URL https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png top5_probabilities: tensor([88.25, 6.37, 3.13, 1.42, 0.63]), top5_class_indices: tensor([651, 966, 808, 459, 661])

Extract the feature map from the image from the URL https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png torch.Size([1, 64, 112, 112]), torch.Size([1, 256, 56, 56]), torch.Size([1, 512, 28, 28]), torch.Size([1, 1024, 14, 14]), torch.Size([1, 2048, 7, 7])

Generate the image embeddings from the image from the URL https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png tensor([[-0.0127, 0.0122, 0.0185,..., -0.0149, -0.0111, 0.0045]])

Limitations

Current Model has several limitations that are important to consider when using it for image classification tasks.

• Limited Image Size: The model is trained on images of size 224 x 224 pixels, which may not be suitable for larger or smaller images.
• Limited Training Data: The model is trained on the ImageNet-1k dataset, which may not cover all possible scenarios or objects.
• Overfitting: The model has a large number of parameters (25.6M) which can lead to overfitting, especially when the training data is limited.
• Limited Generalizability: The model is trained on a specific dataset and may not generalize well to other datasets or tasks.

Format

ResNet50 is an image classification model that uses a ResNet-B architecture. It’s trained on ImageNet-1k and features ReLU activations, single layer 7x7 convolution with pooling, and 1x1 convolution shortcut downsample.

• Model Architecture: The model consists of several layers, including convolutional layers with ReLU activation, max pooling layers, residual connections (1x1 convolution shortcut downsample), and fully connected layers for classification.
• Supported Data Formats: The model accepts input images in the following formats: RGB images with size 224x224 (training) or 288x288 (testing).
• Input Requirements: To use the model, you need to preprocess your images to the required size, normalize the pixel values to the range [0, 1], and convert the images to PyTorch tensors.
• Output Format: The model outputs a tensor with shape (batch_size, num_classes), where num_classes is the number of classes in the classification problem.