---
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:

👉 [CaMiT Embeddings](https://huggingface.co/datasets/fredericlin/CaMiT-embeddings)

## License

CC BY-NC-SA 4.0 – for non-commercial research purposes only.