DomainNet 0324
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Multi-Source Domain Adaptation : Infograph, Quickdraw, Real, Sketch, Fashion
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Painting-126-DomainNet
Painting-126-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 paintings into 126 domain categories using the SiglipForImageClassification architecture.
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
aircraft_carrier 0.8065 0.4717 0.5952 106
alarm_clock 1.0000 0.7612 0.8644 67
ant 0.8095 0.7234 0.7640 188
anvil 0.3205 0.2066 0.2513 121
asparagus 0.8242 0.8827 0.8525 324
axe 0.4028 0.4857 0.4404 175
banana 0.8986 0.8986 0.8986 286
basket 0.7251 0.7492 0.7370 331
bathtub 0.0000 0.0000 0.0000 35
bear 0.8704 0.8647 0.8675 303
bee 0.9478 0.9440 0.9459 250
bird 0.8031 0.8807 0.8401 176
blackberry 0.0000 0.0000 0.0000 11
blueberry 0.8258 0.8258 0.8258 132
bottlecap 0.6487 0.8173 0.7233 427
broccoli 0.8961 0.8625 0.8790 80
bus 0.6909 0.8444 0.7600 90
butterfly 0.9313 0.9613 0.9460 310
cactus 0.9583 0.9388 0.9485 98
cake 0.5290 0.5984 0.5615 122
calculator 0.0000 0.0000 0.0000 10
camel 0.8244 0.9351 0.8763 231
camera 0.9725 0.8480 0.9060 125
candle 0.7763 0.8551 0.8138 207
cannon 0.0000 0.0000 0.0000 43
canoe 0.8400 0.9161 0.8764 298
carrot 0.9744 0.9005 0.9360 211
castle 0.9027 0.9278 0.9151 180
cat 0.8824 0.9818 0.9294 275
ceiling_fan 1.0000 0.1333 0.2353 30
cell_phone 0.7117 0.7453 0.7281 106
cello 0.8647 0.9127 0.8880 126
chair 0.8750 0.1667 0.2800 42
chandelier 0.9773 0.9348 0.9556 46
coffee_cup 0.9015 0.8095 0.8530 147
compass 0.9483 0.8871 0.9167 62
computer 0.0000 0.0000 0.0000 14
cow 0.9590 0.9360 0.9474 125
crab 0.9829 0.9426 0.9623 122
crocodile 0.9468 0.9271 0.9368 96
cruise_ship 0.8977 0.8977 0.8977 176
dog 0.9149 0.9739 0.9435 574
dolphin 0.8928 0.9595 0.9249 321
dragon 0.9278 0.9730 0.9499 185
drums 0.8457 0.8405 0.8431 163
duck 0.9335 0.9642 0.9486 335
dumbbell 0.9539 0.9603 0.9571 151
elephant 0.9405 0.9794 0.9595 339
eyeglasses 0.5417 0.1970 0.2889 66
feather 0.9314 0.9416 0.9365 274
fence 0.0000 0.0000 0.0000 39
fish 0.8829 0.9671 0.9231 304
flamingo 0.9778 0.9888 0.9832 178
flower 0.7188 0.7706 0.7438 388
foot 0.5893 0.4853 0.5323 68
fork 0.9500 0.2836 0.4368 67
frog 0.9172 0.9925 0.9534 134
giraffe 0.9762 0.9762 0.9762 84
goatee 0.4565 0.4828 0.4693 87
grapes 0.8761 0.8200 0.8471 250
guitar 0.8827 0.8827 0.8827 162
hammer 0.0000 0.0000 0.0000 36
helicopter 0.9733 0.8835 0.9262 206
helmet 0.0000 0.0000 0.0000 22
horse 0.9514 0.9856 0.9682 417
kangaroo 0.9387 0.9053 0.9217 169
lantern 0.6263 0.7126 0.6667 174
laptop 0.8800 0.8871 0.8835 124
leaf 0.7754 0.8930 0.8301 402
lion 0.9347 0.8883 0.9109 403
lipstick 0.9281 0.9045 0.9161 157
lobster 0.9646 0.9455 0.9550 202
microphone 0.9231 0.8136 0.8649 118
monkey 0.7892 0.8656 0.8256 320
mosquito 0.8696 0.3846 0.5333 52
mouse 0.8610 0.9174 0.8883 351
mug 0.8669 0.9365 0.9003 299
mushroom 0.9070 0.9653 0.9353 202
onion 0.8700 0.9231 0.8958 377
panda 0.9631 0.9952 0.9789 210
peanut 0.5000 0.1212 0.1951 66
pear 0.9278 0.9356 0.9317 357
peas 0.8281 0.7465 0.7852 71
pencil 0.4902 0.5245 0.5068 143
penguin 0.9496 0.9576 0.9536 354
pig 0.9392 0.9500 0.9446 260
pillow 0.7273 0.0727 0.1322 110
pineapple 0.9849 0.9812 0.9831 266
potato 1.0000 0.0652 0.1224 46
power_outlet 0.9600 0.8889 0.9231 81
purse 0.5000 0.0513 0.0930 39
rabbit 0.8961 0.9673 0.9303 214
raccoon 0.9490 0.9394 0.9442 198
rhinoceros 0.9657 0.9657 0.9657 175
rifle 0.8200 0.8542 0.8367 192
saxophone 0.8100 0.8556 0.8322 284
screwdriver 0.7083 0.6296 0.6667 54
sea_turtle 0.9757 0.9969 0.9862 322
see_saw 0.3527 0.6077 0.4463 130
sheep 0.9328 0.9398 0.9363 266
shoe 0.9522 0.9567 0.9544 208
skateboard 0.4464 0.2083 0.2841 120
snake 0.8627 0.8550 0.8588 338
speedboat 0.8710 0.6835 0.7660 79
spider 0.8129 0.6975 0.7508 162
squirrel 0.9325 0.9063 0.9192 427
strawberry 0.9316 0.9470 0.9392 302
streetlight 0.7493 0.7948 0.7714 346
string_bean 0.8636 0.4130 0.5588 46
submarine 0.5845 0.7423 0.6541 326
swan 0.9222 0.8910 0.9063 266
table 0.0000 0.0000 0.0000 81
teapot 0.8619 0.9318 0.8955 308
teddy-bear 0.8517 0.9136 0.8816 220
television 0.0000 0.0000 0.0000 40
the_Eiffel_Tower 0.9366 0.9882 0.9617 254
the_Great_Wall_of_China 0.8244 0.8710 0.8471 124
tiger 0.9504 0.9702 0.9602 336
toe 0.0000 0.0000 0.0000 1
train 0.9367 0.9628 0.9496 323
truck 0.8864 0.7959 0.8387 98
umbrella 0.6309 0.8174 0.7121 230
vase 0.7382 0.8309 0.7818 207
watermelon 0.9479 0.9450 0.9464 327
whale 0.8877 0.8657 0.8766 283
zebra 0.9832 0.9832 0.9832 238
accuracy 0.8533 24032
macro avg 0.7686 0.7273 0.7299 24032
weighted avg 0.8445 0.8533 0.8424 24032
The model categorizes images into the following 126 classes:
- Class 0: "aircraft_carrier"
- Class 1: "alarm_clock"
- Class 2: "ant"
- Class 3: "anvil"
- Class 4: "asparagus"
- Class 5: "axe"
- Class 6: "banana"
- Class 7: "basket"
- Class 8: "bathtub"
- Class 9: "bear"
- Class 10: "bee"
- Class 11: "bird"
- Class 12: "blackberry"
- Class 13: "blueberry"
- Class 14: "bottlecap"
- Class 15: "broccoli"
- Class 16: "bus"
- Class 17: "butterfly"
- Class 18: "cactus"
- Class 19: "cake"
- Class 20: "calculator"
- Class 21: "camel"
- Class 22: "camera"
- Class 23: "candle"
- Class 24: "cannon"
- Class 25: "canoe"
- Class 26: "carrot"
- Class 27: "castle"
- Class 28: "cat"
- Class 29: "ceiling_fan"
- Class 30: "cell_phone"
- Class 31: "cello"
- Class 32: "chair"
- Class 33: "chandelier"
- Class 34: "coffee_cup"
- Class 35: "compass"
- Class 36: "computer"
- Class 37: "cow"
- Class 38: "crab"
- Class 39: "crocodile"
- Class 40: "cruise_ship"
- Class 41: "dog"
- Class 42: "dolphin"
- Class 43: "dragon"
- Class 44: "drums"
- Class 45: "duck"
- Class 46: "dumbbell"
- Class 47: "elephant"
- Class 48: "eyeglasses"
- Class 49: "feather"
- Class 50: "fence"
- Class 51: "fish"
- Class 52: "flamingo"
- Class 53: "flower"
- Class 54: "foot"
- Class 55: "fork"
- Class 56: "frog"
- Class 57: "giraffe"
- Class 58: "goatee"
- Class 59: "grapes"
- Class 60: "guitar"
- Class 61: "hammer"
- Class 62: "helicopter"
- Class 63: "helmet"
- Class 64: "horse"
- Class 65: "kangaroo"
- Class 66: "lantern"
- Class 67: "laptop"
- Class 68: "leaf"
- Class 69: "lion"
- Class 70: "lipstick"
- Class 71: "lobster"
- Class 72: "microphone"
- Class 73: "monkey"
- Class 74: "mosquito"
- Class 75: "mouse"
- Class 76: "mug"
- Class 77: "mushroom"
- Class 78: "onion"
- Class 79: "panda"
- Class 80: "peanut"
- Class 81: "pear"
- Class 82: "peas"
- Class 83: "pencil"
- Class 84: "penguin"
- Class 85: "pig"
- Class 86: "pillow"
- Class 87: "pineapple"
- Class 88: "potato"
- Class 89: "power_outlet"
- Class 90: "purse"
- Class 91: "rabbit"
- Class 92: "raccoon"
- Class 93: "rhinoceros"
- Class 94: "rifle"
- Class 95: "saxophone"
- Class 96: "screwdriver"
- Class 97: "sea_turtle"
- Class 98: "see_saw"
- Class 99: "sheep"
- Class 100: "shoe"
- Class 101: "skateboard"
- Class 102: "snake"
- Class 103: "speedboat"
- Class 104: "spider"
- Class 105: "squirrel"
- Class 106: "strawberry"
- Class 107: "streetlight"
- Class 108: "string_bean"
- Class 109: "submarine"
- Class 110: "swan"
- Class 111: "table"
- Class 112: "teapot"
- Class 113: "teddy-bear"
- Class 114: "television"
- Class 115: "the_Eiffel_Tower"
- Class 116: "the_Great_Wall_of_China"
- Class 117: "tiger"
- Class 118: "toe"
- Class 119: "train"
- Class 120: "truck"
- Class 121: "umbrella"
- Class 122: "vase"
- Class 123: "watermelon"
- Class 124: "whale"
- Class 125: "zebra"
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/Painting-126-DomainNet"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
def painting_classification(image):
"""Predicts the painting 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 for the model
inputs = processor(images=image, return_tensors="pt")
# Get predictions from the model without gradient computation
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()
# Define the label mapping for each class index
labels = {
"0": "aircraft_carrier", "1": "alarm_clock", "2": "ant", "3": "anvil", "4": "asparagus",
"5": "axe", "6": "banana", "7": "basket", "8": "bathtub", "9": "bear",
"10": "bee", "11": "bird", "12": "blackberry", "13": "blueberry", "14": "bottlecap",
"15": "broccoli", "16": "bus", "17": "butterfly", "18": "cactus", "19": "cake",
"20": "calculator", "21": "camel", "22": "camera", "23": "candle", "24": "cannon",
"25": "canoe", "26": "carrot", "27": "castle", "28": "cat", "29": "ceiling_fan",
"30": "cell_phone", "31": "cello", "32": "chair", "33": "chandelier", "34": "coffee_cup",
"35": "compass", "36": "computer", "37": "cow", "38": "crab", "39": "crocodile",
"40": "cruise_ship", "41": "dog", "42": "dolphin", "43": "dragon", "44": "drums",
"45": "duck", "46": "dumbbell", "47": "elephant", "48": "eyeglasses", "49": "feather",
"50": "fence", "51": "fish", "52": "flamingo", "53": "flower", "54": "foot",
"55": "fork", "56": "frog", "57": "giraffe", "58": "goatee", "59": "grapes",
"60": "guitar", "61": "hammer", "62": "helicopter", "63": "helmet", "64": "horse",
"65": "kangaroo", "66": "lantern", "67": "laptop", "68": "leaf", "69": "lion",
"70": "lipstick", "71": "lobster", "72": "microphone", "73": "monkey", "74": "mosquito",
"75": "mouse", "76": "mug", "77": "mushroom", "78": "onion", "79": "panda",
"80": "peanut", "81": "pear", "82": "peas", "83": "pencil", "84": "penguin",
"85": "pig", "86": "pillow", "87": "pineapple", "88": "potato", "89": "power_outlet",
"90": "purse", "91": "rabbit", "92": "raccoon", "93": "rhinoceros", "94": "rifle",
"95": "saxophone", "96": "screwdriver", "97": "sea_turtle", "98": "see_saw", "99": "sheep",
"100": "shoe", "101": "skateboard", "102": "snake", "103": "speedboat", "104": "spider",
"105": "squirrel", "106": "strawberry", "107": "streetlight", "108": "string_bean",
"109": "submarine", "110": "swan", "111": "table", "112": "teapot", "113": "teddy-bear",
"114": "television", "115": "the_Eiffel_Tower", "116": "the_Great_Wall_of_China",
"117": "tiger", "118": "toe", "119": "train", "120": "truck", "121": "umbrella",
"122": "vase", "123": "watermelon", "124": "whale", "125": "zebra"
}
# Map 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 for the painting classifier
iface = gr.Interface(
fn=painting_classification,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Prediction Scores"),
title="Painting-126-DomainNet Classification",
description="Upload a painting to classify it into one of 126 categories."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
Intended Use:
The Painting-126-DomainNet model is designed for painting image classification. It categorizes paintings into a wide range of domains—from objects like an "aircraft_carrier" or "alarm_clock" to animals, plants, and everyday items. Potential use cases include:
- Art Curation & Analysis: Assisting galleries and museums in organizing and categorizing artworks.
- Creative Search Engines: Enabling painting-based search for art inspiration and research.
- Educational Tools: Supporting art education by categorizing and retrieving visual resources.
- Computer Vision Research: Providing a benchmark dataset for studies in painting recognition and domain adaptation tasks.
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Base model
google/siglip2-base-patch16-224