Dataset
ImageNet-1K Image Classification Dataset
The gold standard image classification benchmark with 1,000 classes and 1.43M labeled images, a milestone in deep learning history.
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ImageNet-1K Dataset
ImageNet-1K
Image Classification Dataset
ImageNet-1K is the gold standard benchmark dataset for image classification in the field of computer vision. Originating from the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), it contains 1.43 million manually labeled images covering 1,000 object categories. The breakthrough performance of AlexNet on this dataset in 2012 sparked the deep learning revolution and it remains the most widely used visual benchmark to this day.
1.43M images
1,000 categories
256x256 average size
Since 2009
1.43M
Total Images
1,000
Object Categories
256x256
Average Image Size
Since 2009
Released
Dataset Highlights
The most authoritative image classification benchmark dataset in the field of computer vision
Gold Standard Benchmark
The most widely cited and recognized image classification dataset in the history of computer vision, serving as the industry standard for evaluating model performance.
1,000 Categories
Covers a wide range of diverse object categories from animals, vehicles to everyday items, food, etc.
Human Annotation
Each image is manually annotated by humans through the Amazon Mechanical Turk platform, ensuring label quality.
Hierarchical Labels
All categories are organized according to the WordNet noun hierarchy, supporting fine-grained and coarse-grained classification analysis.
Standard Data Split
Provides a standard training/validation/testing set split (1.28 million training, 50,000 validation, 100,000 testing) for fair comparison.
Historically Significant
The breakthrough performance of AlexNet on this dataset in 2012 initiated the deep learning revolution, profoundly changing the trajectory of AI development.
Applicable Scenarios
From academic research to industrial applications, ImageNet-1K is an essential foundational resource
Image Classification
Train and evaluate image classification models, comparing the accuracy performance of different algorithms on standard benchmarks
Transfer Learning
Pre-trained feature extractors for downstream visual tasks, significantly reducing the data requirements and training time for target tasks
Architecture Research
Compare the performance of different neural network architectures on standard benchmarks, driving innovation in model design
Representation Learning
Learn general visual representations for various downstream visual tasks such as object detection and semantic segmentation
computer-vision
image-classification
benchmark
deep-learning
ILSVRC
Quick Start with ImageNet-1K
Quickly access the ImageNet-1K dataset via API
import requests
# Set your API token
API_TOKEN = "your_api_token_here"
# Request ImageNet-1K dataset
response = requests.get(
"https://api.acedata.cloud/datasets/imagenet-1k",
headers={
"Authorization": f"Bearer {API_TOKEN}",
"Accept": "application/json"
},
params={
"split": "validation",
"limit": 10
}
)
# Parse the response
data = response.json()
print(f"Total samples: {data.get('total', 0)}")
for item in data.get("results", []):
print(f" Label: {item['label']}, Class: {item['class_name']}")
3 Steps to Get Started Quickly
From registration to usage, you can start your computer vision project in just a few minutes
01
Register an Account
Register for an Ace Data Cloud account at platform.acedata.cloud and quickly complete the registration process.
02
Get API Key
Create an API Key in the console for authentication to access the ImageNet-1K dataset interface.
03
Call Dataset API
Use the API Key to call the ImageNet-1K dataset interface and start training and evaluating your vision model.
Start Exploring the ImageNet-1K Dataset
The gold standard in computer vision, with 1.43 million manually labeled images and 1,000 object categories. Whether you are a deep learning researcher or a computer vision engineer, ImageNet-1K is an essential benchmark dataset.
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