chore: track *.jsonl via Git LFS

.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+*.jsonl filter=lfs diff=lfs merge=lfs -text
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+*.avi filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+pretty_name: "InstVL: An Instance-Aware Vision-Language Dataset"
+language:
+  - en
+multilinguality: monolingual
+task_categories:
+  - image-to-text
+  - text-to-image
+  - video-text-to-text
+  - text-to-video
+  - object-detection
+size_categories:
+  - "1M<n<10M"
+annotations_creators:
+  - machine-generated
+source_datasets:
+  - original
+configs:
+  - config_name: image
+    data_files:
+      - train/instvl_img_2m.jsonl
+      - test/instvl_img_1k.jsonl
+      - test/instvl_img_10k.jsonl
+      - test/instvl_img_zero_1k.jsonl
+      - test/instvl_img_zero_10k.jsonl
+  - config_name: video
+    data_files:
+      - train/instvl_video_50k.jsonl
+      - test/instvl_video_1k.jsonl
+      - test/instvl_video_10k.jsonl
+---
+
+# InstVL: An Instance-Aware Vision-Language Dataset
+
+![InstVL Teaser](media/instvl_teaser.png)
+
+This is the official repository for the **InstVL** dataset, introduced in the paper **INST-AP: Instance-Aware Vision-Language Pre-Train for Spatial-Temporal Understanding**.
+
+InstVL is a large-scale dataset of images and videos designed to bridge the gap between holistic scene understanding and fine-grained, instance-level comprehension. It addresses the limitation that most pre-training paradigms excel at global semantics but struggle with details about fine-grained instances.
+
+InstVL provides two levels of detailed textual annotations:
+
+- **Global Captions** — A comprehensive description of the entire scene.
+- **Instance Captions** — Fine-grained descriptions grounded to specific object regions (in images) or trajectories (in videos).
+
+The dataset contains ***over 3.4 million instances** in **over 2 million images** and **50,000 videos**, providing rich supervision for instance-centric pre-training and benchmarking.
+
+---
+
+## 💻 How to Load with 🤗 Datasets
+
+You can load the dataset directly using the Hugging Face `datasets` library. You must specify which configuration (**image** or **video**) you want to load.
+
+### Loading the Image Dataset
+
+```python
+from datasets import load_dataset
+
+img_ds = load_dataset(
+    "visionai-gai/instvl",
+    name="image",
+    data_files={
+        "train": "train/instvl_img_2m.jsonl",
+        "test_1k": "test/instvl_img_1k.jsonl",
+        "test_10k": "test/instvl_img_10k.jsonl",
+        "test_zero_1k": "test/instvl_img_zero_1k.jsonl",
+        "test_zero_10k": "test/instvl_img_zero_10k.jsonl",
+    },
+)
+
+# Access a split
+train_split = img_ds["train"]
+
+print(train_split[0])
+```
+
+### Loading the Video Dataset
+
+```python
+from datasets import load_dataset
+
+video_ds = load_dataset(
+    "visionai-gai/instvl",
+    name="video",
+    data_files={
+        "train": "train/instvl_video_50k.jsonl",
+        "test_1k": "test/instvl_video_1k.jsonl",
+        "test_10k": "test/instvl_video_10k.jsonl",
+    }
+)
+# Access a split
+train_split = video_ds["train"]
+
+# Print the first example
+print(train_split[0])
+```
+
+---
+
+## 📋 Dataset Structure
+
+The dataset is organized into `train` and `test` splits, with data provided in the JSON Lines (`.jsonl`) format.
+
+```text
+.
+├── test
+│   ├── instvl_img_10k.jsonl
+│   ├── instvl_img_1k.jsonl
+│   ├── instvl_img_zero_10k.jsonl
+│   ├── instvl_img_zero_1k.jsonl
+│   ├── instvl_video_10k.jsonl
+│   └── instvl_video_1k.jsonl
+└── train
+    ├── instvl_img_2m.jsonl
+    └── instvl_video_50k.jsonl
+```
+
+---
+
+## 🖼️ Image Data Structure
+
+Each line in the image `.jsonl` files represents a single image and its annotations.
+
+### Fields
+
+| Key            | Data Type          | Description                                                                                                                                                    |
+|----------------|--------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `image`        | String             | The relative file path to the JPG image.                                                                                                                       |
+| `caption`      | String             | A detailed, holistic caption describing the entire image scene.                                                                                                |
+| `image_id`     | String             | A unique identifier for the image.                                                                                                                             |                                                                                                   |
+| `instance_data`| List of Objects    | A list of annotations for specific object instances within the image. Each annotation pairs a bounding box with a free-form sentence describing that instance. Can be `[]`. |
+
+### `instance_data` Object
+
+| Key                 | Data Type       | Description                                                                                           |
+|---------------------|-----------------|-------------------------------------------------------------------------------------------------------|
+| `instance_id`       | String          | A unique ID for the detected object within the image.                                                 |
+| `instance_category` | String          | The category or class assigned to the object (e.g., `"Shoes"`, `"Player"`).                           |
+| `bbox`              | List of Int     | The bounding box coordinates for the object, in `[x, y, width, height]` format.                       |
+| `instance_caption`  | String          | A fine-grained caption describing only the object within the bounding box.                             |
+
+---
+
+## 📹 Video Data Structure
+
+Each line in the video `.jsonl` files represents a single video segment and its annotations.
+
+### Fields
+
+| Key                   | Data Type       | Description                                                                                                    |
+|-----------------------|-----------------|----------------------------------------------------------------------------------------------------------------|
+| `video`               | String          | The filename of the segmented MP4 video clip.                                                                  |
+| `resolution`          | List of Int     | The video's resolution as `[height, width]`.                                                                   |
+| `caption`             | String          | A detailed summary of the events and scene within the entire video segment.                                    |
+| `duration`            | Float           | The total duration of the video segment in seconds.                                                            |
+| `segment_frame_range` | List of Int     | The `[start_frame, end_frame]` numbers from the original, full-length source video.                            |
+| `instance_data`       | List of Objects | A list containing information about each object tracked across multiple frames. The instance caption is shared by all boxes on an object's trajectory. |
+
+### `instance_data` Object
+
+| Key                 | Data Type    | Description                                                                                               |
+|---------------------|--------------|-----------------------------------------------------------------------------------------------------------|
+| `instance_id`       | Integer      | A unique ID for the tracked object within this video segment.                                            |
+| `instance_category` | String       | The category assigned to the tracked object (e.g., `"person"`, `"furniture"`).                           |
+| `instance_caption`  | String       | A summary describing the object and its actions throughout the video.                                    |
+| `frames`            | List of Obj       | A list of frame-wise boxes for the object.                              |
+| `frame`              | Integer  | (Field inside each frames item) The frame index.                 |
+| `bbox`              | List of Int  | (Field inside each frames item) The bounding box coordinates `[x, y, width, height]` for the object on a specific frame.                 |
+
+---
+
+## 📊 Data Splits
+
+The InstVL dataset is divided into several training and test splits to facilitate robust benchmarking.
+
+| Split      | Filename                         | # of Samples | Description                         |
+|------------|----------------------------------|--------------|-------------------------------------|
+| Train      | `train/instvl_img_2m.jsonl`      | ~2,000,000   | Main training set for images.       |
+| Train      | `train/instvl_video_50k.jsonl`   | ~50,000      | Main training set for videos.       |
+| Test       | `test/instvl_img_1k.jsonl`       | 2,442        | Standard image test set.            |
+| Test       | `test/instvl_img_10k.jsonl`      | 23,129       | Larger image test set.              |
+| Test       | `test/instvl_video_1k.jsonl`     | 2,508        | Standard video test set.            |
+| Test-Zero  | `test/instvl_img_zero_1k.jsonl`  | 2,570        | Zero-shot image test set.           |
+| Test-Zero  | `test/instvl_img_zero_10k.jsonl` | 26,029       | Larger zero-shot image test set.    |
+
+### Meaning of the “Zero” Splits
+
+The **img-zero** splits are a key component for evaluation. As stated in the paper, these splits are **not present** in the training dataset and introduce a **mild distributional shift**. Their purpose is to confirm that a model's performance demonstrates **true generalization** capabilities and is not merely inherited from the training distribution.
+
+---
+
+## 🏆 Benchmark Results
+
+The following tables show the performance of the **INST-AP** model on the InstVL test sets, compared to other state-of-the-art models. All results are from the original paper.
+
+### Instance-Level Retrieval Performance (Recall@1)
+
+This task evaluates the model's ability to retrieve the correct fine-grained instance caption.
+
+| Method          | InstVL(img) 1K (V2T/T2V) | InstVL(img) 10K (V2T/T2V) | InstVL(img-zero) 1K (V2T/T2V) | InstVL(img-zero) 10K (V2T/T2V) | InstVL(video) 1K (V2T/T2V) |
+|-----------------|--------------------------|----------------------------|--------------------------------|---------------------------------|----------------------------|
+| CLIP4CLIP [56]  | 25.10 / 18.68            | 33.21 / 28.19              | 17.82 / 25.10                  | 9.11 / 16.30                   | 17.71 / 24.69             |
+| UMT-B [17]      | 38.74 / 18.95            | 32.79 / 25.59              | 32.25 / 28.13                  | 15.06 / 12.99                  | 39.85 / 40.22             |
+| UMT-L [17]      | 38.44 / 23.08            | 21.34 / 35.65              | 29.34 / 30.17                  | 11.09 / 16.38                  | 26.38 / 22.43             |
+| **INST-AP (Ours)** | **49.61 / 51.33**     | **44.69 / 45.01**          | **41.47 / 40.21**              | **23.95 / 28.99**              | **51.63 / 54.62**         |
+
+### Global Retrieval Performance (Recall@1)
+
+This task evaluates the model's ability to retrieve the correct global caption for the entire scene.
+
+| Method          | InstVL(img) 1K (V2T/T2V) | InstVL(img) 10K (V2T/T2V) | InstVL(img-zero) 1K (V2T/T2V) | InstVL(img-zero) 10K (V2T/T2V) | InstVL(video) 1K (V2T/T2V) |
+|-----------------|--------------------------|----------------------------|--------------------------------|---------------------------------|----------------------------|
+| CLIP4CLIP [56]  | 93.40 / 84.25            | 79.22 / 96.00              | 78.20 / 81.70                  | 56.95 / 63.96                  | 67.50 / 70.50             |
+| UMT-B [17]      | 92.60 / 89.50            | 72.40 / 71.43              | 83.10 / 80.90                  | 65.37 / 66.12                  | 83.70 / 79.20             |
+| UMT-L [17]      | 95.30 / 83.95            | 94.70 / 85.41              | 83.70 / 83.90                  | 72.59 / 72.60                  | 88.30 / 85.50             |
+| **INST-AP (Ours)** | **97.90 / 96.80**     | **90.93 / 88.95**          | **86.70 / 85.60**              | **76.96 / 75.76**              | **88.30 / 87.70**         |
+
+---
+
+## ⬇️ Downloading the Original Images & Videos
+
+You can download the original images and videos from the following websites:
+
+- **InstVL Image:** [LAION-400M via img2dataset](https://github.com/rom1504/img2dataset/blob/main/dataset_examples/laion400m.md)
+- **InstVL Image Zero:** [COYO-700M via img2dataset](https://github.com/rom1504/img2dataset/blob/main/dataset_examples/coyo-700m.md)
+- **InstVL Video:** [HD-VILA-100M on Hugging Face](https://huggingface.co/datasets/TempoFunk/hdvila-100M)
+
+---
+
+
+## 🙏 Acknowledgements
+
+This dataset is based on results obtained from a project, **JPNP20017**, subsidized by the **New Energy and Industrial Technology Development Organization (NEDO)**.
+
+## 📝 License
+
+Refer to the license [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en) for using our dataset.
+
+---
+
+## 📜 Citations
+
+If you use this dataset in your research, please cite the original paper:
+
+```bibtex
+@inproceedings{instap2025,
+  title        = {INST-AP: Instance-Aware Vision-Language Pre-Train for Spatial-Temporal Understanding},
+  author       = {Ashutosh Kumar, Quan Kong, Jingjing Pan, Rajat Saini, Mustafa_Erdogan, Betty Le Dem, Norimasa Kobori},
+  booktitle    = {ArXiV},
+  year         = {2025}
+}
+```

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