metadata
license: cc-by-nc-sa-4.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: pretrain
path: data/pretrain-*
dataset_info:
features:
- name: id
dtype: string
- name: time
dtype: string
- name: metadata
struct:
- name: id
dtype: string
- name: owner
dtype: string
- name: title
dtype: string
- name: license
dtype: string
- name: dateupload
dtype: string
- name: tags
dtype: string
- name: url_z
dtype: string
- name: height_z
dtype: string
- name: width_z
dtype: string
- name: date
dtype: string
- name: faces
sequence:
- name: bbox
sequence: int32
- name: det_score
dtype: float32
- name: boxes
sequence:
- name: bbox
sequence: float32
- name: yolo_score
dtype: float32
- name: class
dtype: string
- name: gpt_car_probability
dtype: float32
- name: gpt_model_probability
dtype: float32
- name: gpt_student_score
dtype: float32
- name: qwen_student_score
dtype: float32
splits:
- name: train
num_bytes: 294865622
num_examples: 655681
- name: test
num_bytes: 38698484
num_examples: 84830
- name: pretrain
num_bytes: 1207382492
num_examples: 2709837
download_size: 809497488
dataset_size: 1540946598
CaMiT: Car Models in Time
CaMiT (Car Models in Time) is a large-scale, fine-grained, time-aware dataset of car images collected from Flickr. It is designed to support research on temporal adaptation in visual models, continual learning, and time-aware generative modeling.
Dataset Highlights
Labeled Subset:
- 787,000 samples
- 190 car models
- 2007–2023
Unlabeled Pretraining Subset:
- 5.1 million samples
- 2005–2023
Metadata includes:
- Image URLs (not the images themselves)
- Upload time
- Bounding boxes
- Tags and licensing
- Face detections
- Car detection scores and class information
All images are linked (not redistributed) using Flickr metadata to respect copyright compliance, in line with LAION and DataComp datasets.
Tasks Supported
- Time-aware fine-grained classification
- Time-incremental continual learning
- In-domain static and incremental pretraining
- Time-aware image generation
Example Use Cases
- Evaluating representation drift over time
- Training classifiers that generalize across time periods
- Studying model degradation and adaptation across years
- Conditioning generation models on temporal context
Related Resource
A separate dataset containing precomputed image embeddings, categorized by car class and year, is available here:
License
CC BY-NC-SA 4.0 – for non-commercial research purposes only.