prithivMLmods
/

Clipart-126-DomainNet

 license: apache-2.0
 datasets:
 - Bruece/reclip_domainnet_126_clipart
+---
+# **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.
+```py
+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🤗**
+```python
+!pip install -q transformers torch pillow gradio
+```
+```python
+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.