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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.

Classification Report:
 precision recall f1-score support

 tench 0.9878 0.9911 0.9895 900
 goldfish 0.9945 0.9956 0.9950 900
 great white shark 0.9339 0.8944 0.9137 900
 tiger shark 0.8957 0.8967 0.8962 900
 hammerhead 0.9300 0.9589 0.9442 900
 electric ray 0.8788 0.8622 0.8704 900
 stingray 0.8689 0.8911 0.8799 900
 cock 0.9000 0.9200 0.9099 900
 hen 0.9162 0.8867 0.9012 900
 ostrich 0.9945 0.9989 0.9967 900
 brambling 0.9671 0.9478 0.9574 900
 goldfinch 0.9867 0.9911 0.9889 900
 house finch 0.9629 0.9811 0.9719 900
 junco 0.9583 0.9700 0.9641 900
 indigo bunting 0.9933 0.9911 0.9922 900
 robin 0.9888 0.9811 0.9849 900
 bulbul 0.9735 0.9811 0.9773 900
 jay 0.9855 0.9789 0.9822 900
 magpie 0.9776 0.9700 0.9738 900
 chickadee 0.9834 0.9844 0.9839 900
 water ouzel 0.9680 0.9744 0.9712 900
 kite 0.9512 0.9522 0.9517 900
 bald eagle 0.9843 0.9722 0.9782 900
 vulture 0.9562 0.9700 0.9630 900
 great grey owl 0.9989 0.9944 0.9967 900
european fire salamander 0.9330 0.9278 0.9304 900
 common newt 0.7969 0.7933 0.7951 900
 eft 0.9162 0.8989 0.9075 900
 spotted salamander 0.9249 0.9300 0.9274 900
 axolotl 0.9888 0.9767 0.9827 900
 bullfrog 0.9116 0.9167 0.9141 900
 tree frog 0.9108 0.9533 0.9316 900
 tailed frog 0.8658 0.8100 0.8370 900
 loggerhead 0.8657 0.8956 0.8804 900
 leatherback turtle 0.9038 0.8667 0.8849 900
 mud turtle 0.7980 0.7111 0.7521 900
 terrapin 0.7039 0.7844 0.7420 900
 box turtle 0.8576 0.8633 0.8605 900
 banded gecko 0.9255 0.9111 0.9183 900
 common iguana 0.9033 0.9133 0.9083 900
 american chameleon 0.6577 0.7622 0.7061 900
 whiptail 0.8351 0.8722 0.8533 900
 agama 0.9010 0.8900 0.8955 900
 frilled lizard 0.9674 0.9233 0.9449 900
 alligator lizard 0.8862 0.8822 0.8842 900
 gila monster 0.9821 0.9733 0.9777 900
 green lizard 0.6574 0.5756 0.6137 900
 african chameleon 0.9573 0.9711 0.9641 900
 komodo dragon 0.9693 0.9811 0.9752 900
 african crocodile 0.9769 0.9878 0.9823 900

 accuracy 0.9181 45000
 macro avg 0.9186 0.9181 0.9181 45000
 weighted avg 0.9186 0.9181 0.9181 45000

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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

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Install Dependencies

pip install -q transformers torch pillow gradio

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Inference Code

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()

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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.

Base model

google/siglip2-base-patch16-224

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