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