Update README.md

README.md

@@ -2,8 +2,25 @@
 license: apache-2.0
 datasets:
 - Elriggs/imagenet-50-subset
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- image-net
+- 50-class
+- photo
 ---
 
+![ax.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/lggdyeOAr204uVfMlyrEX.png)
+
+# **imagenet-50-subset**
+
+> **imagenet-50-subset** is a vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for **multi-class image classification**. It is trained to classify images into a subset of 50 categories derived from the **ImageNet** dataset using the **SiglipForImageClassification** architecture.
+
+
 ```py
 Classification Report:
                           precision    recall  f1-score   support
@@ -62,4 +79,179 @@ european fire salamander     0.9330    0.9278    0.9304       900
                 accuracy                         0.9181     45000
                macro avg     0.9186    0.9181    0.9181     45000
             weighted avg     0.9186    0.9181    0.9181     45000
-```
+```
+
+---
+
+## **Label Space: 50 Classes**
+
+The model classifies each image into one of the following categories:
+
+```
+0: tench
+1: goldfish
+2: great white shark
+3: tiger shark
+4: hammerhead
+5: electric ray
+6: stingray
+7: cock
+8: hen
+9: ostrich
+10: brambling
+11: goldfinch
+12: house finch
+13: junco
+14: indigo bunting
+15: robin
+16: bulbul
+17: jay
+18: magpie
+19: chickadee
+20: water ouzel
+21: kite
+22: bald eagle
+23: vulture
+24: great grey owl
+25: european fire salamander
+26: common newt
+27: eft
+28: spotted salamander
+29: axolotl
+30: bullfrog
+31: tree frog
+32: tailed frog
+33: loggerhead
+34: leatherback turtle
+35: mud turtle
+36: terrapin
+37: box turtle
+38: banded gecko
+39: common iguana
+40: american chameleon
+41: whiptail
+42: agama
+43: frilled lizard
+44: alligator lizard
+45: gila monster
+46: green lizard
+47: african chameleon
+48: komodo dragon
+49: african crocodile
+```
+
+---
+
+## **Install Dependencies**
+
+```bash
+pip install -q transformers torch pillow gradio
+```
+
+---
+
+## **Inference Code**
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/imagenet-50-subset"  # Replace if different
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Label mapping
+id2label = {
+    "0": "tench",
+    "1": "goldfish",
+    "2": "great white shark",
+    "3": "tiger shark",
+    "4": "hammerhead",
+    "5": "electric ray",
+    "6": "stingray",
+    "7": "cock",
+    "8": "hen",
+    "9": "ostrich",
+    "10": "brambling",
+    "11": "goldfinch",
+    "12": "house finch",
+    "13": "junco",
+    "14": "indigo bunting",
+    "15": "robin",
+    "16": "bulbul",
+    "17": "jay",
+    "18": "magpie",
+    "19": "chickadee",
+    "20": "water ouzel",
+    "21": "kite",
+    "22": "bald eagle",
+    "23": "vulture",
+    "24": "great grey owl",
+    "25": "european fire salamander",
+    "26": "common newt",
+    "27": "eft",
+    "28": "spotted salamander",
+    "29": "axolotl",
+    "30": "bullfrog",
+    "31": "tree frog",
+    "32": "tailed frog",
+    "33": "loggerhead",
+    "34": "leatherback turtle",
+    "35": "mud turtle",
+    "36": "terrapin",
+    "37": "box turtle",
+    "38": "banded gecko",
+    "39": "common iguana",
+    "40": "american chameleon",
+    "41": "whiptail",
+    "42": "agama",
+    "43": "frilled lizard",
+    "44": "alligator lizard",
+    "45": "gila monster",
+    "46": "green lizard",
+    "47": "african chameleon",
+    "48": "komodo dragon",
+    "49": "african crocodile"
+}
+
+def classify_imagenet_50(image):
+    image = Image.fromarray(image).convert("RGB")
+    inputs = processor(images=image, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
+
+    prediction = {
+        id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
+    }
+
+    return prediction
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=classify_imagenet_50,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=5, label="ImageNet-50 Classification"),
+    title="imagenet-50-subset",
+    description="Upload an image to classify it into one of 50 selected ImageNet categories."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## **Intended Use**
+
+**imagenet-50-subset** can be used for:
+
+* **Benchmarking Lightweight Vision Models** – Quick testing on a meaningful subset of ImageNet classes.
+* **Educational Demos** – Teaching about classification tasks with a simpler label space.
+* **Prototype Deployment** – Use in applications where full ImageNet coverage is unnecessary.
+* **Dataset Analysis** – Classification-based filtering of visual content into known object classes.