👁 zddfdxzdf.png

Multisource-121-DomainNet

Multisource-121-DomainNet is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to classify images into 121 domain categories using the SiglipForImageClassification architecture.

👁 - visual selection(2).png

Moment Matching for Multi-Source Domain Adaptation : https://arxiv.org/pdf/1812.01754

SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786

Classification Report:
 precision recall f1-score support

 barn 0.7483 0.8370 0.7902 270
 baseball_bat 0.9197 0.9333 0.9265 270
 basket 0.8302 0.8148 0.8224 270
 beach 0.7059 0.7556 0.7299 270
 bear 0.7500 0.7444 0.7472 270
 beard 0.5496 0.5741 0.5616 270
 bee 0.9004 0.9037 0.9020 270
 bird 0.7352 0.7815 0.7576 270
 blueberry 0.7230 0.7926 0.7562 270
 bowtie 0.8726 0.8370 0.8544 270
 bracelet 0.7328 0.7111 0.7218 270
 brain 0.8925 0.9222 0.9071 270
 bread 0.5573 0.6667 0.6071 270
 broccoli 0.9200 0.7667 0.8364 270
 bus 0.8442 0.8630 0.8535 270
 butterfly 0.9321 0.9148 0.9234 270
 circle 0.6038 0.8185 0.6950 270
 cloud 0.8201 0.8444 0.8321 270
 cruise_ship 0.8545 0.8481 0.8513 270
 dolphin 0.8286 0.8593 0.8436 270
 dumbbell 0.8705 0.8963 0.8832 270
 elephant 0.8598 0.8630 0.8614 270
 eye 0.8603 0.8667 0.8635 270
 eyeglasses 0.8425 0.7926 0.8168 270
 feather 0.8413 0.7852 0.8123 270
 fish 0.8169 0.8593 0.8375 270
 flower 0.7973 0.8741 0.8339 270
 foot 0.8152 0.8333 0.8242 270
 frog 0.9270 0.8000 0.8588 270
 giraffe 0.9026 0.8926 0.8976 270
 goatee 0.5171 0.5037 0.5103 270
 golf_club 0.6466 0.6778 0.6618 270
 grapes 0.8731 0.8407 0.8566 270
 grass 0.7359 0.6296 0.6786 270
 guitar 0.8386 0.8852 0.8613 270
 hamburger 0.8535 0.8630 0.8582 270
 hand 0.7824 0.6926 0.7348 270
 hat 0.7333 0.7741 0.7532 270
 headphones 0.8971 0.9037 0.9004 270
 helicopter 0.8992 0.8259 0.8610 270
 hexagon 0.9113 0.8370 0.8726 270
 hockey_stick 0.8419 0.8481 0.8450 270
 horse 0.8081 0.8889 0.8466 270
 hourglass 0.9161 0.9296 0.9228 270
 house 0.7524 0.8778 0.8103 270
 ice_cream 0.8821 0.8593 0.8705 270
 jacket 0.8621 0.7407 0.7968 270
 ladder 0.7051 0.8148 0.7560 270
 leg 0.5916 0.5741 0.5827 270
 lipstick 0.8889 0.8000 0.8421 270
 megaphone 0.8710 0.9000 0.8852 270
 monkey 0.8370 0.8556 0.8462 270
 moon 0.8527 0.8148 0.8333 270
 mushroom 0.8774 0.8481 0.8625 270
 necklace 0.8670 0.7481 0.8032 270
 owl 0.9179 0.9111 0.9145 270
 panda 0.9490 0.8963 0.9219 270
 pear 0.8832 0.8963 0.8897 270
 peas 0.7743 0.8259 0.7993 270
 penguin 0.8618 0.8778 0.8697 270
 pig 0.6767 0.8296 0.7454 270
 pillow 0.7359 0.6296 0.6786 270
 pineapple 0.9213 0.9111 0.9162 270
 pizza 0.9173 0.9444 0.9307 270
 pool 0.6717 0.6593 0.6654 270
 popsicle 0.7390 0.8074 0.7717 270
 rabbit 0.8345 0.8778 0.8556 270
 rhinoceros 0.9219 0.9185 0.9202 270
 rifle 0.9256 0.8296 0.8750 270
 river 0.6067 0.7370 0.6656 270
 sailboat 0.8606 0.9148 0.8869 270
 sandwich 0.7638 0.7667 0.7652 270
 sea_turtle 0.8794 0.9185 0.8986 270
 shark 0.8114 0.8444 0.8276 270
 shoe 0.8097 0.8667 0.8372 270
 skyscraper 0.7727 0.8185 0.7950 270
 snorkel 0.8238 0.6926 0.7525 270
 snowman 0.8736 0.8444 0.8588 270
 soccer_ball 0.9395 0.8630 0.8996 270
 speedboat 0.7649 0.7593 0.7621 270
 spider 0.9212 0.8222 0.8689 270
 spoon 0.8165 0.8074 0.8119 270
 square 0.4669 0.6259 0.5348 270
 squirrel 0.8394 0.7741 0.8054 270
 stethoscope 0.8566 0.8630 0.8598 270
 strawberry 0.8629 0.7926 0.8263 270
 streetlight 0.5000 0.6852 0.5781 270
 submarine 0.6850 0.6926 0.6888 270
 suitcase 0.8259 0.7556 0.7892 270
 sun 0.8082 0.6556 0.7239 270
 sweater 0.5912 0.6963 0.6395 270
 sword 0.8258 0.8074 0.8165 270
 table 0.5502 0.5481 0.5492 270
 teapot 0.9019 0.8852 0.8935 270
 teddy-bear 0.7906 0.8111 0.8007 270
 telephone 0.7836 0.7778 0.7807 270
 tent 0.7579 0.7074 0.7318 270
 The_Eiffel_Tower 0.8633 0.8889 0.8759 270
The_Great_Wall_of_China 0.8893 0.8333 0.8604 270
 The_Mona_Lisa 0.8152 0.9148 0.8621 270
 tiger 0.8577 0.8259 0.8415 270
 toaster 0.6788 0.6889 0.6838 270
 tooth 0.8807 0.7926 0.8343 270
 tornado 0.7530 0.7000 0.7255 270
 tractor 0.9372 0.8296 0.8802 270
 train 0.7692 0.7407 0.7547 270
 tree 0.7639 0.8148 0.7885 270
 triangle 0.8852 0.8000 0.8405 270
 trombone 0.6653 0.5963 0.6289 270
 truck 0.7049 0.7963 0.7478 270
 trumpet 0.7463 0.5667 0.6442 270
 umbrella 0.9144 0.8704 0.8918 270
 vase 0.8148 0.7333 0.7719 270
 violin 0.8966 0.7704 0.8287 270
 watermelon 0.7970 0.8000 0.7985 270
 whale 0.7769 0.6963 0.7344 270
 windmill 0.8963 0.8963 0.8963 270
 wine_glass 0.8996 0.8630 0.8809 270
 yoga 0.7406 0.8037 0.7709 270
 zebra 0.9144 0.7519 0.8252 270
 zigzag 0.6502 0.6333 0.6417 270

 accuracy 0.7995 32670
 macro avg 0.8052 0.7995 0.8006 32670
 weighted avg 0.8052 0.7995 0.8006 32670

The model categorizes images into the following 121 classes:

• Class 0: "barn"
• Class 1: "baseball_bat"
• Class 2: "basket"
• Class 3: "beach"
• Class 4: "bear"
• Class 5: "beard"
• Class 6: "bee"
• Class 7: "bird"
• Class 8: "blueberry"
• Class 9: "bowtie"
• Class 10: "bracelet"
• Class 11: "brain"
• Class 12: "bread"
• Class 13: "broccoli"
• Class 14: "bus"
• Class 15: "butterfly"
• Class 16: "circle"
• Class 17: "cloud"
• Class 18: "cruise_ship"
• Class 19: "dolphin"
• Class 20: "dumbbell"
• Class 21: "elephant"
• Class 22: "eye"
• Class 23: "eyeglasses"
• Class 24: "feather"
• Class 25: "fish"
• Class 26: "flower"
• Class 27: "foot"
• Class 28: "frog"
• Class 29: "giraffe"
• Class 30: "goatee"
• Class 31: "golf_club"
• Class 32: "grapes"
• Class 33: "grass"
• Class 34: "guitar"
• Class 35: "hamburger"
• Class 36: "hand"
• Class 37: "hat"
• Class 38: "headphones"
• Class 39: "helicopter"
• Class 40: "hexagon"
• Class 41: "hockey_stick"
• Class 42: "horse"
• Class 43: "hourglass"
• Class 44: "house"
• Class 45: "ice_cream"
• Class 46: "jacket"
• Class 47: "ladder"
• Class 48: "leg"
• Class 49: "lipstick"
• Class 50: "megaphone"
• Class 51: "monkey"
• Class 52: "moon"
• Class 53: "mushroom"
• Class 54: "necklace"
• Class 55: "owl"
• Class 56: "panda"
• Class 57: "pear"
• Class 58: "peas"
• Class 59: "penguin"
• Class 60: "pig"
• Class 61: "pillow"
• Class 62: "pineapple"
• Class 63: "pizza"
• Class 64: "pool"
• Class 65: "popsicle"
• Class 66: "rabbit"
• Class 67: "rhinoceros"
• Class 68: "rifle"
• Class 69: "river"
• Class 70: "sailboat"
• Class 71: "sandwich"
• Class 72: "sea_turtle"
• Class 73: "shark"
• Class 74: "shoe"
• Class 75: "skyscraper"
• Class 76: "snorkel"
• Class 77: "snowman"
• Class 78: "soccer_ball"
• Class 79: "speedboat"
• Class 80: "spider"
• Class 81: "spoon"
• Class 82: "square"
• Class 83: "squirrel"
• Class 84: "stethoscope"
• Class 85: "strawberry"
• Class 86: "streetlight"
• Class 87: "submarine"
• Class 88: "suitcase"
• Class 89: "sun"
• Class 90: "sweater"
• Class 91: "sword"
• Class 92: "table"
• Class 93: "teapot"
• Class 94: "teddy-bear"
• Class 95: "telephone"
• Class 96: "tent"
• Class 97: "The_Eiffel_Tower"
• Class 98: "The_Great_Wall_of_China"
• Class 99: "The_Mona_Lisa"
• Class 100: "tiger"
• Class 101: "toaster"
• Class 102: "tooth"
• Class 103: "tornado"
• Class 104: "tractor"
• Class 105: "train"
• Class 106: "tree"
• Class 107: "triangle"
• Class 108: "trombone"
• Class 109: "truck"
• Class 110: "trumpet"
• Class 111: "umbrella"
• Class 112: "vase"
• Class 113: "violin"
• Class 114: "watermelon"
• Class 115: "whale"
• Class 116: "windmill"
• Class 117: "wine_glass"
• Class 118: "yoga"
• Class 119: "zebra"
• Class 120: "zigzag"

Run with Transformers🤗

!pip install -q transformers torch pillow gradio

import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Multisource-121-DomainNet"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

def multisource_classification(image):
 """Predicts the domain category for an input image."""
 # Convert the input numpy array to a PIL Image and ensure it is in RGB format
 image = Image.fromarray(image).convert("RGB")
 
 # Process the image and convert it to model inputs
 inputs = processor(images=image, return_tensors="pt")
 
 # Get model predictions without gradient calculations
 with torch.no_grad():
 outputs = model(**inputs)
 logits = outputs.logits
 # Convert logits to probabilities using softmax
 probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
 
 # Mapping from class indices to domain labels
 labels = {
 "0": "barn", "1": "baseball_bat", "2": "basket", "3": "beach", "4": "bear",
 "5": "beard", "6": "bee", "7": "bird", "8": "blueberry", "9": "bowtie",
 "10": "bracelet", "11": "brain", "12": "bread", "13": "broccoli", "14": "bus",
 "15": "butterfly", "16": "circle", "17": "cloud", "18": "cruise_ship", "19": "dolphin",
 "20": "dumbbell", "21": "elephant", "22": "eye", "23": "eyeglasses", "24": "feather",
 "25": "fish", "26": "flower", "27": "foot", "28": "frog", "29": "giraffe",
 "30": "goatee", "31": "golf_club", "32": "grapes", "33": "grass", "34": "guitar",
 "35": "hamburger", "36": "hand", "37": "hat", "38": "headphones", "39": "helicopter",
 "40": "hexagon", "41": "hockey_stick", "42": "horse", "43": "hourglass", "44": "house",
 "45": "ice_cream", "46": "jacket", "47": "ladder", "48": "leg", "49": "lipstick",
 "50": "megaphone", "51": "monkey", "52": "moon", "53": "mushroom", "54": "necklace",
 "55": "owl", "56": "panda", "57": "pear", "58": "peas", "59": "penguin",
 "60": "pig", "61": "pillow", "62": "pineapple", "63": "pizza", "64": "pool",
 "65": "popsicle", "66": "rabbit", "67": "rhinoceros", "68": "rifle", "69": "river",
 "70": "sailboat", "71": "sandwich", "72": "sea_turtle", "73": "shark", "74": "shoe",
 "75": "skyscraper", "76": "snorkel", "77": "snowman", "78": "soccer_ball", "79": "speedboat",
 "80": "spider", "81": "spoon", "82": "square", "83": "squirrel", "84": "stethoscope",
 "85": "strawberry", "86": "streetlight", "87": "submarine", "88": "suitcase", "89": "sun",
 "90": "sweater", "91": "sword", "92": "table", "93": "teapot", "94": "teddy-bear",
 "95": "telephone", "96": "tent", "97": "The_Eiffel_Tower", "98": "The_Great_Wall_of_China",
 "99": "The_Mona_Lisa", "100": "tiger", "101": "toaster", "102": "tooth", "103": "tornado",
 "104": "tractor", "105": "train", "106": "tree", "107": "triangle", "108": "trombone",
 "109": "truck", "110": "trumpet", "111": "umbrella", "112": "vase", "113": "violin",
 "114": "watermelon", "115": "whale", "116": "windmill", "117": "wine_glass", "118": "yoga",
 "119": "zebra", "120": "zigzag"
 }
 
 # Create a dictionary mapping each label to its corresponding probability (rounded)
 predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
 return predictions

# Create Gradio interface
iface = gr.Interface(
 fn=multisource_classification,
 inputs=gr.Image(type="numpy"),
 outputs=gr.Label(label="Prediction Scores"),
 title="Multisource-121-DomainNet Classification",
 description="Upload an image to classify it into one of 121 domain categories."
)

# Launch the app
if __name__ == "__main__":
 iface.launch()

Intended Use:

The Multisource-121-DomainNet model is designed for multi-source image classification. It can categorize images into a diverse set of 121 domains, covering various objects, scenes, and landmarks. Potential use cases include:

• Cross-Domain Image Analysis: Enabling robust classification across a wide range of visual domains.
• Multimedia Retrieval: Assisting in content organization and retrieval in multimedia databases.
• Computer Vision Research: Serving as a benchmark for evaluating domain adaptation and transfer learning techniques.
• Interactive Applications: Enhancing user interfaces with diverse, real-time image recognition capabilities.

Base model