Clipart-126-DomainNet

Clipart-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 clipart images into 126 domain categories using the SiglipForImageClassification architecture.

👁 - visual selection(3).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

 aircraft_carrier 0.8667 0.4643 0.6047 56
 alarm_clock 0.9706 0.8919 0.9296 74
 ant 0.8889 0.8615 0.8750 65
 anvil 0.5984 0.6083 0.6033 120
 asparagus 0.8158 0.6078 0.6966 51
 axe 0.7544 0.5309 0.6232 81
 banana 0.7111 0.5517 0.6214 58
 basket 0.8571 0.8182 0.8372 66
 bathtub 0.7531 0.7821 0.7673 78
 bear 0.9118 0.6458 0.7561 48
 bee 0.9636 0.9636 0.9636 165
 bird 0.8967 0.9529 0.9240 255
 blackberry 0.8082 0.8429 0.8252 70
 blueberry 0.8661 0.8981 0.8818 108
 bottlecap 0.7821 0.8299 0.8053 147
 broccoli 0.8947 0.8947 0.8947 95
 bus 0.9663 0.9348 0.9503 92
 butterfly 0.9333 0.9545 0.9438 132
 cactus 0.9677 0.9091 0.9375 99
 cake 0.8750 0.8099 0.8412 121
 calculator 0.9583 0.5897 0.7302 39
 camel 0.9391 0.9310 0.9351 116
 camera 0.8846 0.8679 0.8762 53
 candle 0.8298 0.8478 0.8387 92
 cannon 0.8551 0.8551 0.8551 69
 canoe 0.8462 0.7432 0.7914 74
 carrot 0.8800 0.7719 0.8224 57
 castle 1.0000 0.8511 0.9195 47
 cat 0.8167 0.7903 0.8033 62
 ceiling_fan 1.0000 0.2000 0.3333 30
 cell_phone 0.7400 0.6491 0.6916 57
 cello 0.8372 0.9114 0.8727 79
 chair 0.8986 0.8378 0.8671 74
 chandelier 0.9617 0.9263 0.9437 190
 coffee_cup 0.8811 0.9389 0.9091 229
 compass 0.9799 0.9012 0.9389 162
 computer 0.7124 0.9045 0.7970 178
 cow 0.9517 0.9718 0.9617 142
 crab 0.8738 0.9000 0.8867 100
 crocodile 0.9778 0.9167 0.9462 144
 cruise_ship 0.8544 0.9072 0.8800 194
 dog 0.8125 0.7761 0.7939 67
 dolphin 0.7680 0.7500 0.7589 128
 dragon 0.9512 0.9176 0.9341 85
 drums 0.8919 0.9635 0.9263 137
 duck 0.8774 0.8447 0.8608 161
 dumbbell 0.9048 0.9500 0.9268 280
 elephant 0.9038 0.8952 0.8995 105
 eyeglasses 0.8636 0.8488 0.8562 291
 feather 0.8564 0.9227 0.8883 181
 fence 0.9211 0.8400 0.8787 125
 fish 0.8963 0.8768 0.8864 138
 flamingo 0.9636 0.9381 0.9507 226
 flower 0.9146 0.9454 0.9298 238
 foot 0.8780 0.8889 0.8834 81
 fork 0.9032 0.9091 0.9061 154
 frog 0.9420 0.9489 0.9455 137
 giraffe 0.9643 0.9153 0.9391 118
 goatee 0.8763 0.9422 0.9081 173
 grapes 0.9114 0.8571 0.8834 84
 guitar 0.9595 0.8554 0.9045 83
 hammer 0.6111 0.7719 0.6822 114
 helicopter 0.9444 0.9533 0.9488 107
 helmet 0.7368 0.8550 0.7915 131
 horse 0.9588 0.9819 0.9702 166
 kangaroo 0.9125 0.8488 0.8795 86
 lantern 0.8254 0.7536 0.7879 69
 laptop 0.8108 0.5000 0.6186 60
 leaf 0.7143 0.3333 0.4545 30
 lion 0.9744 0.8085 0.8837 47
 lipstick 0.7875 0.6632 0.7200 95
 lobster 0.8963 0.9130 0.9046 161
 microphone 0.7925 0.9231 0.8528 91
 monkey 0.9623 0.9027 0.9315 113
 mosquito 0.8636 0.8444 0.8539 45
 mouse 0.9167 0.8333 0.8730 66
 mug 0.8989 0.8163 0.8556 98
 mushroom 0.9429 0.9429 0.9429 105
 onion 0.9365 0.8429 0.8872 140
 panda 1.0000 0.9726 0.9861 73
 peanut 0.5900 0.7195 0.6484 82
 pear 0.7692 0.7246 0.7463 69
 peas 0.8000 0.7429 0.7704 70
 pencil 0.6667 0.0909 0.1600 44
 penguin 0.9717 0.9279 0.9493 111
 pig 0.9551 0.8252 0.8854 103
 pillow 0.6290 0.5571 0.5909 70
 pineapple 0.9846 0.8889 0.9343 72
 potato 0.6038 0.6531 0.6275 98
 power_outlet 0.8636 0.4043 0.5507 47
 purse 0.0000 0.0000 0.0000 27
 rabbit 0.9341 0.8586 0.8947 99
 raccoon 0.8836 0.9021 0.8927 143
 rhinoceros 0.8750 0.9459 0.9091 74
 rifle 0.7595 0.7500 0.7547 80
 saxophone 0.9454 0.9886 0.9665 175
 screwdriver 0.7521 0.6929 0.7213 127
 sea_turtle 0.9677 0.9626 0.9651 187
 see_saw 0.6679 0.8698 0.7556 215
 sheep 0.9355 0.9158 0.9255 95
 shoe 0.8969 0.8700 0.8832 100
 skateboard 0.8632 0.8673 0.8652 211
 snake 0.9302 0.9160 0.9231 131
 speedboat 0.8187 0.8976 0.8563 166
 spider 0.9043 0.9286 0.9163 112
 squirrel 0.7945 0.8855 0.8375 131
 strawberry 0.8687 0.9923 0.9264 260
 streetlight 0.8178 0.9293 0.8700 198
 string_bean 0.8525 0.8000 0.8254 65
 submarine 0.8022 0.8902 0.8439 164
 swan 0.8397 0.9003 0.8690 291
 table 0.8564 0.9200 0.8871 175
 teapot 0.8763 0.9189 0.8971 185
 teddy-bear 0.9006 0.8953 0.8980 172
 television 0.8509 0.8220 0.8362 118
 the_Eiffel_Tower 0.9468 0.9082 0.9271 98
the_Great_Wall_of_China 0.9462 0.9462 0.9462 93
 tiger 0.9417 0.9826 0.9617 230
 toe 0.8250 0.6600 0.7333 50
 train 0.9362 0.9778 0.9565 90
 truck 0.9367 0.8916 0.9136 83
 umbrella 0.9633 0.9545 0.9589 110
 vase 0.7642 0.8393 0.8000 112
 watermelon 0.9527 0.9527 0.9527 148
 whale 0.7453 0.8144 0.7783 194
 zebra 0.9275 0.9676 0.9471 185

 accuracy 0.8691 14818
 macro avg 0.8613 0.8251 0.8351 14818
 weighted avg 0.8705 0.8691 0.8661 14818

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/Clipart-126-DomainNet"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

def clipart_classification(image):
 """Predicts the clipart category for an input image."""
 # Convert the input numpy array to a PIL Image and ensure it's in RGB format
 image = Image.fromarray(image).convert("RGB")
 
 # Process the image and prepare it for the model
 inputs = processor(images=image, return_tensors="pt")
 
 # Perform inference without gradient computation
 with torch.no_grad():
 outputs = model(**inputs)
 logits = outputs.logits
 # Apply softmax to obtain probabilities for each class
 probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
 
 # Mapping from indices to clipart category labels
 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"
 }
 
 # 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=clipart_classification,
 inputs=gr.Image(type="numpy"),
 outputs=gr.Label(label="Prediction Scores"),
 title="Clipart-126-DomainNet Classification",
 description="Upload a clipart image to classify it into one of 126 domain categories."
)

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

Intended Use:

The Clipart-126-DomainNet model is designed for clipart image classification. It categorizes clipart images into a wide range of domains—from objects like an "aircraft_carrier" or "alarm_clock" to various everyday items. Potential use cases include:

Digital Art and Design: Assisting designers in organizing and retrieving clipart assets.
Content Management: Enhancing digital asset management systems with robust clipart classification.
Creative Search Engines: Enabling clipart-based search for design inspiration and resource curation.
Computer Vision Research: Serving as a benchmark for studies in clipart recognition and domain adaptation.