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README.md

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-library_name: transformers
-tags: []
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
+# 🧠 ClipSegMultiClass
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+Multiclass semantic segmentation using CLIP + CLIPSeg.  
+Fine-tuned version of [`CIDAS/clipseg-rd64-refined`](https://huggingface.co/CIDAS/clipseg-rd64-refined)  
+Supports multiple classes in a single forward pass.
 
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
+---
 
-[More Information Needed]
+## 🔬 Model
 
-#### Hardware
+**Name:** [`BioMike/clipsegmulticlass_v1`](https://huggingface.co/BioMike/clipsegmulticlass_v1)  
+**Repository:** [github.com/BioMikeUkr/clipsegmulticlass](https://github.com/BioMikeUkr/clipsegmulticlass)  
+**Base:** `CIDAS/clipseg-rd64-refined`  
+**Classes:** `["background", "Pig", "Horse", "Sheep"]`  
+**Image Size:** 352×352  
+**Trained on:** OpenImages segmentation subset (custom fruit/animal dataset)
 
-[More Information Needed]
+---
 
-#### Software
+## 📊 Evaluation
 
-[More Information Needed]
+| Model                        | Precision | Recall  | F1 Score | Accuracy |
+|-----------------------------|-----------|---------|----------|----------|
+| CIDAS/clipseg-rd64-refined  | 0.5239    | 0.2114  | 0.2882   | 0.2665   |
+| BioMike/clipsegmulticlass_v1| 0.7460    | 0.5035  | 0.6009   | 0.6763   |
 
-## Citation [optional]
+---
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## 🎮 Demo
 
-**BibTeX:**
+👉 Try it online:  
+[Hugging Face Space 🚀](https://huggingface.co/spaces/BioMike/clipsegmulticlass)
 
-[More Information Needed]
+---
 
-**APA:**
+## 📦 Usage
 
-[More Information Needed]
+```python
+from PIL import Image
+import torch
+import matplotlib.pyplot as plt
+import numpy as np
+from model import ClipSegMultiClassModel
+from config import ClipSegMultiClassConfig
 
-## Glossary [optional]
+# Load model
+model = ClipSegMultiClassModel.from_pretrained("trained_clipseg_multiclass").to("cuda").eval()
+config = model.config  # contains label2color
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+# Load image
+image = Image.open("pigs.jpg").convert("RGB")
 
-[More Information Needed]
+# Run inference
+mask = model.predict(image)  # shape: [1, H, W]
 
-## More Information [optional]
+# Visualize
+def visualize_mask(mask_tensor: torch.Tensor, label2color: dict):
+    if mask_tensor.dim() == 3:
+        mask_tensor = mask_tensor.squeeze(0)
 
-[More Information Needed]
+    mask_np = mask_tensor.cpu().numpy().astype(np.uint8)  # [H, W]
+    h, w = mask_np.shape
+    color_mask = np.zeros((h, w, 3), dtype=np.uint8)
 
-## Model Card Authors [optional]
+    for class_idx, color in label2color.items():
+        color_mask[mask_np == class_idx] = color
 
-[More Information Needed]
+    return color_mask
 
-## Model Card Contact
+color_mask = visualize_mask(mask, config.label2color)
 
-[More Information Needed]
+plt.imshow(color_mask)
+plt.axis("off")
+plt.title("Predicted Segmentation Mask")
+plt.show()