| --- |
| license: mit |
| language: |
| - en |
| tags: |
| - image-segmentation |
| - chroma-key |
| - green-screen |
| - color-segmentation |
| - pytorch |
| - codelor-color |
| - lightweight |
| pipeline_tag: image-segmentation |
| --- |
| |
| # Codelor Color 1.2 |
|
|
| **Codelor Color** is a lightweight image segmentation model built specifically for **chroma key**: detecting and removing green from an image or video (green screens, leaves, fabric, green objects) and producing a clean alpha mask. |
|
|
| It takes an RGB image and a target color (green by default) and outputs a per-pixel binary mask. |
|
|
| --- |
|
|
| ## Model at a glance |
|
|
| - **Architecture**: Lightweight Transformer with linear attention and depthwise convolutions, optimized for CPU. |
| - **Parameters**: 1,641,055 |
| - **File size**: ~6.3 MB in safetensors format. |
| - **Input**: RGB image (any size) + RGB triplet of the target color. |
| - **Output**: probability mask (1 channel, same resolution as input). |
| - **Use cases**: green screen, video chroma key, green object removal, color-similarity segmentation. |
|
|
| --- |
|
|
| ## Quick start |
|
|
| ### Installation |
|
|
| You can install the standalone architecture package directly from PyPI: |
|
|
| pip install codelor-color |
| |
| Or install all the required dependencies to run this specific model: |
|
|
| pip install torch safetensors huggingface_hub pillow numpy |
| |
| ### Load the model |
|
|
| You can use the standalone PyPI package, or load the architecture directly from this repository: |
|
|
| from codelor_color import CodelorColor |
| from safetensors.torch import load_file |
| import torch |
| |
| # If loading from the HF repo directly: |
| # from modeling_codelor import load_from_pretrained |
| # model = load_from_pretrained("YOUR_USERNAME/codelor-color-1.2", device="cpu") |
| |
| # If using the PyPI package and local weights: |
| model = CodelorColor(dim=128, depth=8, heads=8, tied_after=4) |
| state_dict = load_file("model.safetensors") |
| model.load_state_dict(state_dict) |
| model.eval() |
| |
| ### Predict a mask on an image |
|
|
| import torch |
| from PIL import Image |
| import numpy as np |
| from modeling_codelor import predict_mask |
| |
| img = Image.open("photo.jpg").convert("RGB") |
| x = torch.from_numpy(np.array(img)).float().permute(2, 0, 1) / 255.0 |
| |
| mask, prob = predict_mask(model, x, color_rgb=(0.20, 0.85, 0.30), threshold=0.5) |
| |
| print("Green surface detected: {:.1f}%".format(mask.mean() * 100)) |
| |
| ### Remove green from an image (RGBA output) |
|
|
| from modeling_codelor import remove_green |
| |
| rgba = remove_green(x, model) |
| rgba_np = (rgba.permute(1, 2, 0).numpy() * 255).astype("uint8") |
| |
| Image.fromarray(rgba_np, mode="RGBA").save("no_green.png") |
| |
| --- |
|
|
| ## Performance and statistics |
|
|
| ### Model metrics |
|
|
| | Metric | Value | |
| |---|---| |
| | Pixel accuracy | _to be filled_ | |
| | Mean IoU | _to be filled_ | |
| | Best loss | _to be filled_ | |
| | Parameters | 1,641,055 | |
| | File size | ~6.3 MB | |
| | CPU latency (160x160) | ~80-150 ms | |
|
|
| ### Comparison with other approaches |
|
|
| | Method | Parameters | IoU / mIoU | CPU latency | Notes | |
| |---|---|---|---|---| |
| | K-means (HSV) | - | 0.642 (IoU) | very fast | Sensitive to lighting | |
| | U-Net | 34.5M | 0.794 (mIoU) | ~290 ms | Heavy baseline | |
| | Attention U-Net | 34.9M | 0.845 (IoU) | ~331 ms | Even heavier | |
| | BASNet | 87.1M | 0.561 (mIoU) | high | Very heavy | |
| | MobileSAM | 10.1M | ~0.80 (mIoU) | ~407 ms | Promptable, heavier | |
| | **Codelor Color 1.2** | **1.64M** | _to be filled_ | **~80-150 ms** | **CPU-friendly** | |
|
|
| --- |
|
|
| ## Architecture |
|
|
| - A **ConvNeXt-style convolutional stem** with anti-aliased downsampling. |
| - **8 Transformer blocks** with 2D RoPE and QK-Norm. |
| - A **color cross-attention** module injected at every block. |
| - A **gated FPN decoder** with deep supervision. |
| - An **edge head** (Sobel) to refine boundaries. |
| - **LayerScale** and **DropPath**. |
|
|
| --- |
|
|
| ## License |
|
|
| MIT. |
|
|
