---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: text
    dtype: string
  - name: language
    dtype: string
  - name: kind
    dtype: string
  - name: style
    dtype: string
  - name: font
    dtype: string
  - name: font_size
    dtype: int64
  - name: line_numbers
    dtype: bool
  - name: repo
    dtype: string
  - name: file
    dtype: string
  splits:
  - name: train
    num_bytes: 2556789548
    num_examples: 85634
  - name: validation
    num_bytes: 322219563
    num_examples: 10366
  download_size: 3528351734
  dataset_size: 2879009111
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
pretty_name: Code Snippet Image to Text (8 languages)
license: cc-by-4.0
task_categories:
- image-to-text
tags:
- code
- vlm
- multimodal
- ocr
- syntax-highlighting
- code-generation
- image-to-text
size_categories:
- 10K<n<100K
---
# Code Snippet Image → Text

A multimodal dataset for fine-tuning **vision-language models (VLMs)** on the task of
**transcribing an image of a code snippet back into its source text** — syntax-aware OCR.

Each example pairs a **syntax-highlighted PNG of code** with the **exact code text** that
produced it. It spans **8 programming languages** and deliberately mixes two capture types:

- **`block`** — a complete function / unit (6–45 lines).
- **`fragment`** — a contiguous *partial* view (3–14 lines) that may start or end
  mid-statement, simulating someone screenshotting only **part** of a snippet (possibly
  with a line or two of surrounding context). This makes the model robust to partial inputs.

- **Total examples:** 96,000  ·  **Languages:** 8  ·  **Splits:** train (85,634) / validation (10,366)
- **Target column:** `text` (the exact code; never contains line numbers).

## Samples

### Complete blocks

**C — complete block**

![C — complete block](https://huggingface.co/datasets/anisiraj/code-image-to-text/resolve/main/samples/c_block.png)

**C++ — complete block**

![C++ — complete block](https://huggingface.co/datasets/anisiraj/code-image-to-text/resolve/main/samples/cpp_block.png)

**Go — complete block**

![Go — complete block](https://huggingface.co/datasets/anisiraj/code-image-to-text/resolve/main/samples/go_block.png)

**Java — complete block**

![Java — complete block](https://huggingface.co/datasets/anisiraj/code-image-to-text/resolve/main/samples/java_block.png)

**JavaScript — complete block**

![JavaScript — complete block](https://huggingface.co/datasets/anisiraj/code-image-to-text/resolve/main/samples/javascript_block.png)

**PHP — complete block**

![PHP — complete block](https://huggingface.co/datasets/anisiraj/code-image-to-text/resolve/main/samples/php_block.png)

**Python — complete block**

![Python — complete block](https://huggingface.co/datasets/anisiraj/code-image-to-text/resolve/main/samples/python_block.png)

**Ruby — complete block**

![Ruby — complete block](https://huggingface.co/datasets/anisiraj/code-image-to-text/resolve/main/samples/ruby_block.png)

### Fragments (partial captures)

**JavaScript — partial fragment**

![JavaScript — partial fragment](https://huggingface.co/datasets/anisiraj/code-image-to-text/resolve/main/samples/javascript_fragment.png)

**Python — partial fragment**

![Python — partial fragment](https://huggingface.co/datasets/anisiraj/code-image-to-text/resolve/main/samples/python_fragment.png)

## Dataset structure

### Fields

| field | type | description |
|---|---|---|
| `image` | `Image` | the rendered snippet image (PNG, raw bytes in Parquet — not base64) |
| `text` | `string` | **the prediction target** — the exact code shown, original indentation, no line numbers |
| `language` | `string` | C, C++, Go, Java, JavaScript, PHP, Python, Ruby |
| `kind` | `string` | `block` (complete) or `fragment` (partial) |
| `style` | `string` | Pygments color theme used to render |
| `font` | `string` | monospace font used |
| `font_size` | `int` | font size in px |
| `line_numbers` | `bool` | whether a line-number gutter is shown (visual only — not in `text`) |
| `repo` | `string` | source repository of the code |
| `file` | `string` | source file path |

### Composition (images per language)

| Language | blocks | fragments | total |
|---|--:|--:|--:|
| C | 8,845 | 3,155 | 12,000 |
| C++ | 8,132 | 3,868 | 12,000 |
| Go | 9,007 | 2,993 | 12,000 |
| Java | 9,024 | 2,976 | 12,000 |
| JavaScript | 9,013 | 2,987 | 12,000 |
| PHP | 9,001 | 2,999 | 12,000 |
| Python | 9,014 | 2,986 | 12,000 |
| Ruby | 9,049 | 2,951 | 12,000 |

### Splits
`train` / `validation`, split **by repository** — a function and any fragments derived from
it always land in the same split, so there is **no train/validation leakage**.

## Load

```python
from datasets import load_dataset

ds = load_dataset("anisiraj/code-image-to-text")        # DatasetDict with 'train' and 'validation'
print(ds)
ex = ds["train"][0]
print(ex["language"], ex["kind"])  # e.g. 'Python' 'block'
ex["image"]                        # PIL.Image.Image
ex["text"]                         # the code string to predict
```

### Stream (no full download)

```python
ds = load_dataset("anisiraj/code-image-to-text", split="train", streaming=True)
for ex in ds.take(5):
    print(ex["language"], ex["kind"], ex["image"].size)
```

### One split only

```python
val = load_dataset("anisiraj/code-image-to-text", split="validation")
```

## View the images

```python
ds = load_dataset("anisiraj/code-image-to-text", split="train")

# Jupyter / notebook — renders inline:
ds[0]["image"]

# Save or open in an OS image viewer:
img = ds[0]["image"]
img.save("example.png")
img.show()
```

Show a grid of samples with matplotlib:

```python
import matplotlib.pyplot as plt
fig, axes = plt.subplots(2, 3, figsize=(20, 8))
for ax, ex in zip(axes.ravel(), ds.select(range(6))):
    ax.imshow(ex["image"]); ax.axis("off")
    ax.set_title(f"{ex['language']} / {ex['kind']}")
plt.tight_layout(); plt.show()
```

## Filter

```python
# Python complete blocks only
py_blocks = ds.filter(lambda r: r["language"] == "Python" and r["kind"] == "block")

# fragments only
fragments = ds.filter(lambda r: r["kind"] == "fragment")

# one language
go = ds.filter(lambda r: r["language"] == "Go")
```

## How images are stored

Images use the HF `Image` feature: in the Parquet shards each is a
`struct<bytes: binary, path: string>` holding the **raw PNG bytes** (PNG signature
`89 50 4E 47`), **not base64**. `datasets` decodes them to `PIL.Image` on access. To get
the undecoded bytes:

```python
from datasets import Image
raw = load_dataset("anisiraj/code-image-to-text", split="train").cast_column("image", Image(decode=False))
raw[0]["image"]["bytes"][:8]   # b'\x89PNG\r\n\x1a\n'
```

## Fine-tune a VLM (image → text)

```python
from datasets import load_dataset
ds = load_dataset("anisiraj/code-image-to-text")

PROMPT = "Transcribe the code shown in this image exactly, preserving indentation."

def to_chat(ex):
    return {"image": ex["image"], "prompt": PROMPT, "target": ex["text"]}

train = ds["train"].map(to_chat)
# Feed `image` + `prompt` through your VLM's processor/chat template
# (e.g. Qwen2-VL, Idefics3, Llava, MiniCPM-V) and train to produce `target`.
```

## Rendering & augmentation

Rendered with [Pygments](https://pygments.org/) `ImageFormatter`:
**18 themes** (light & dark) × **3 monospace fonts** × **5 font sizes** (14–20) ×
**line-numbers on/off** × **3 paddings**. Balanced to an equal number of images per
language. The line-number gutter, when present, is visual only and is **never** part of
the `text` target.

## Sources & license

- **CodeSearchNet** — [code-search-net/code_search_net](https://huggingface.co/datasets/code-search-net/code_search_net):
  Go, Java, JavaScript, PHP, Python, Ruby (complete functions from open-source GitHub repos).
- **GitHub Code Snippets** by Bugout.dev / Simiotic —
  [kaggle](https://www.kaggle.com/datasets/simiotic/github-code-snippets) (CC BY 4.0): C, C++ blocks.

Released under **CC BY 4.0**. The underlying code originates from public GitHub repositories
under their respective open-source licenses; please retain attribution to the sources above.

## Citation

```bibtex
@misc{code_image_to_text,
  title  = {Code Snippet Image to Text},
  author = {anisiraj},
  year   = {2026},
  url    = {https://huggingface.co/datasets/anisiraj/code-image-to-text}
}
```