Dog-Breed-120

Dog-Breed-120 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 dog images into specific breed categories using the SiglipForImageClassification architecture.

{'eval_loss': 0.49717578291893005,
 'eval_model_preparation_time': 0.0042,
 'eval_accuracy': 0.8681275679906085,
 'eval_runtime': 146.2493,
 'eval_samples_per_second': 69.894,
 'eval_steps_per_second': 8.739,
 'epoch': 7.0}

The model categorizes images into the following 121 classes (0-120):

• Class 0: "affenpinscher"
• Class 1: "afghan_hound"
• Class 2: "african_hunting_dog"
• Class 3: "airedale"
• Class 4: "american_staffordshire_terrier"
• Class 5: "appenzeller"
• Class 6: "australian_terrier"
• Class 7: "basenji"
• Class 8: "basset"
• Class 9: "beagle"
• Class 10: "bedlington_terrier"
• Class 11: "bernese_mountain_dog"
• Class 12: "black-and-tan_coonhound"
• Class 13: "blenheim_spaniel"
• Class 14: "bloodhound"
• Class 15: "bluetick"
• Class 16: "border_collie"
• Class 17: "border_terrier"
• Class 18: "borzoi"
• Class 19: "boston_bull"
• Class 20: "bouvier_des_flandres"
• Class 21: "boxer"
• Class 22: "brabancon_griffon"
• Class 23: "briard"
• Class 24: "brittany_spaniel"
• Class 25: "bull_mastiff"
• Class 26: "cairn"
• Class 27: "cardigan"
• Class 28: "chesapeake_bay_retriever"
• Class 29: "chihuahua"
• Class 30: "chow"
• Class 31: "clumber"
• Class 32: "cocker_spaniel"
• Class 33: "collie"
• Class 34: "curly-coated_retriever"
• Class 35: "dandie_dinmont"
• Class 36: "dhole"
• Class 37: "dingo"
• Class 38: "doberman"
• Class 39: "english_foxhound"
• Class 40: "english_setter"
• Class 41: "english_springer"
• Class 42: "entlebucher"
• Class 43: "eskimo_dog"
• Class 44: "flat-coated_retriever"
• Class 45: "french_bulldog"
• Class 46: "german_shepherd"
• Class 47: "german_short-haired_pointer"
• Class 48: "giant_schnauzer"
• Class 49: "golden_retriever"
• Class 50: "gordon_setter"
• Class 51: "great_dane"
• Class 52: "great_pyrenees"
• Class 53: "greater_swiss_mountain_dog"
• Class 54: "groenendael"
• Class 55: "ibizan_hound"
• Class 56: "irish_setter"
• Class 57: "irish_terrier"
• Class 58: "irish_water_spaniel"
• Class 59: "irish_wolfhound"
• Class 60: "italian_greyhound"
• Class 61: "japanese_spaniel"
• Class 62: "keeshond"
• Class 63: "kelpie"
• Class 64: "kerry_blue_terrier"
• Class 65: "komondor"
• Class 66: "kuvasz"
• Class 67: "labrador_retriever"
• Class 68: "lakeland_terrier"
• Class 69: "leonberg"
• Class 70: "lhasa"
• Class 71: "malamute"
• Class 72: "malinois"
• Class 73: "maltese_dog"
• Class 74: "mexican_hairless"
• Class 75: "miniature_pinscher"
• Class 76: "miniature_poodle"
• Class 77: "miniature_schnauzer"
• Class 78: "newfoundland"
• Class 79: "norfolk_terrier"
• Class 80: "norwegian_elkhound"
• Class 81: "norwich_terrier"
• Class 82: "old_english_sheepdog"
• Class 83: "otterhound"
• Class 84: "papillon"
• Class 85: "pekinese"
• Class 86: "pembroke"
• Class 87: "pomeranian"
• Class 88: "pug"
• Class 89: "redbone"
• Class 90: "rhodesian_ridgeback"
• Class 91: "rottweiler"
• Class 92: "saint_bernard"
• Class 93: "saluki"
• Class 94: "samoyed"
• Class 95: "schipperke"
• Class 96: "scotch_terrier"
• Class 97: "scottish_deerhound"
• Class 98: "sealyham_terrier"
• Class 99: "shetland_sheepdog"
• Class 100: "shih-tzu"
• Class 101: "siberian_husky"
• Class 102: "silky_terrier"
• Class 103: "soft-coated_wheaten_terrier"
• Class 104: "staffordshire_bullterrier"
• Class 105: "standard_poodle"
• Class 106: "standard_schnauzer"
• Class 107: "sussex_spaniel"
• Class 108: "test"
• Class 109: "tibetan_mastiff"
• Class 110: "tibetan_terrier"
• Class 111: "toy_poodle"
• Class 112: "toy_terrier"
• Class 113: "vizsla"
• Class 114: "walker_hound"
• Class 115: "weimaraner"
• Class 116: "welsh_springer_spaniel"
• Class 117: "west_highland_white_terrier"
• Class 118: "whippet"
• Class 119: "wire-haired_fox_terrier"
• Class 120: "yorkshire_terrier"

Run with Transformers🤗

!pip install -q transformers torch pillow gradio

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

# Load model and processor
model_name = "prithivMLmods/Dog-Breed-120"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

def dog_breed_classification(image):
    \"\"\"Predicts the dog breed for an 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()
    
    labels = {
        \"0\": \"affenpinscher\",
        \"1\": \"afghan_hound\",
        \"2\": \"african_hunting_dog\",
        \"3\": \"airedale\",
        \"4\": \"american_staffordshire_terrier\",
        \"5\": \"appenzeller\",
        \"6\": \"australian_terrier\",
        \"7\": \"basenji\",
        \"8\": \"basset\",
        \"9\": \"beagle\",
        \"10\": \"bedlington_terrier\",
        \"11\": \"bernese_mountain_dog\",
        \"12\": \"black-and-tan_coonhound\",
        \"13\": \"blenheim_spaniel\",
        \"14\": \"bloodhound\",
        \"15\": \"bluetick\",
        \"16\": \"border_collie\",
        \"17\": \"border_terrier\",
        \"18\": \"borzoi\",
        \"19\": \"boston_bull\",
        \"20\": \"bouvier_des_flandres\",
        \"21\": \"boxer\",
        \"22\": \"brabancon_griffon\",
        \"23\": \"briard\",
        \"24\": \"brittany_spaniel\",
        \"25\": \"bull_mastiff\",
        \"26\": \"cairn\",
        \"27\": \"cardigan\",
        \"28\": \"chesapeake_bay_retriever\",
        \"29\": \"chihuahua\",
        \"30\": \"chow\",
        \"31\": \"clumber\",
        \"32\": \"cocker_spaniel\",
        \"33\": \"collie\",
        \"34\": \"curly-coated_retriever\",
        \"35\": \"dandie_dinmont\",
        \"36\": \"dhole\",
        \"37\": \"dingo\",
        \"38\": \"doberman\",
        \"39\": \"english_foxhound\",
        \"40\": \"english_setter\",
        \"41\": \"english_springer\",
        \"42\": \"entlebucher\",
        \"43\": \"eskimo_dog\",
        \"44\": \"flat-coated_retriever\",
        \"45\": \"french_bulldog\",
        \"46\": \"german_shepherd\",
        \"47\": \"german_short-haired_pointer\",
        \"48\": \"giant_schnauzer\",
        \"49\": \"golden_retriever\",
        \"50\": \"gordon_setter\",
        \"51\": \"great_dane\",
        \"52\": \"great_pyrenees\",
        \"53\": \"greater_swiss_mountain_dog\",
        \"54\": \"groenendael\",
        \"55\": \"ibizan_hound\",
        \"56\": \"irish_setter\",
        \"57\": \"irish_terrier\",
        \"58\": \"irish_water_spaniel\",
        \"59\": \"irish_wolfhound\",
        \"60\": \"italian_greyhound\",
        \"61\": \"japanese_spaniel\",
        \"62\": \"keeshond\",
        \"63\": \"kelpie\",
        \"64\": \"kerry_blue_terrier\",
        \"65\": \"komondor\",
        \"66\": \"kuvasz\",
        \"67\": \"labrador_retriever\",
        \"68\": \"lakeland_terrier\",
        \"69\": \"leonberg\",
        \"70\": \"lhasa\",
        \"71\": \"malamute\",
        \"72\": \"malinois\",
        \"73\": \"maltese_dog\",
        \"74\": \"mexican_hairless\",
        \"75\": \"miniature_pinscher\",
        \"76\": \"miniature_poodle\",
        \"77\": \"miniature_schnauzer\",
        \"78\": \"newfoundland\",
        \"79\": \"norfolk_terrier\",
        \"80\": \"norwegian_elkhound\",
        \"81\": \"norwich_terrier\",
        \"82\": \"old_english_sheepdog\",
        \"83\": \"otterhound\",
        \"84\": \"papillon\",
        \"85\": \"pekinese\",
        \"86\": \"pembroke\",
        \"87\": \"pomeranian\",
        \"88\": \"pug\",
        \"89\": \"redbone\",
        \"90\": \"rhodesian_ridgeback\",
        \"91\": \"rottweiler\",
        \"92\": \"saint_bernard\",
        \"93\": \"saluki\",
        \"94\": \"samoyed\",
        \"95\": \"schipperke\",
        \"96\": \"scotch_terrier\",
        \"97\": \"scottish_deerhound\",
        \"98\": \"sealyham_terrier\",
        \"99\": \"shetland_sheepdog\",
        \"100\": \"shih-tzu\",
        \"101\": \"siberian_husky\",
        \"102\": \"silky_terrier\",
        \"103\": \"soft-coated_wheaten_terrier\",
        \"104\": \"staffordshire_bullterrier\",
        \"105\": \"standard_poodle\",
        \"106\": \"standard_schnauzer\",
        \"107\": \"sussex_spaniel\",
        \"108\": \"test\",
        \"109\": \"tibetan_mastiff\",
        \"110\": \"tibetan_terrier\",
        \"111\": \"toy_poodle\",
        \"112\": \"toy_terrier\",
        \"113\": \"vizsla\",
        \"114\": \"walker_hound\",
        \"115\": \"weimaraner\",
        \"116\": \"welsh_springer_spaniel\",
        \"117\": \"west_highland_white_terrier\",
        \"118\": \"whippet\",
        \"119\": \"wire-haired_fox_terrier\",
        \"120\": \"yorkshire_terrier\"\n    }\n    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}\n    \n    return predictions\n\n# Create Gradio interface\niface = gr.Interface(\n    fn=dog_breed_classification,\n    inputs=gr.Image(type=\"numpy\"),\n    outputs=gr.Label(label=\"Prediction Scores\"),\n    title=\"Dog Breed Classification\",\n    description=\"Upload an image to classify it into one of the 121 dog breed categories.\"\n)\n\n# Launch the app\nif __name__ == \"__main__\":\n    iface.launch()\n```

Intended Use:

The Dog-Breed-120 model is designed for dog breed image classification. It helps categorize dog images into 121 specific breed categories. Potential use cases include:

• Pet Identification: Assisting pet owners and veterinarians in identifying dog breeds.
• Animal Research: Supporting research in canine genetics and behavior studies.
• E-commerce Applications: Enhancing pet-related product recommendations and searches.
• Educational Purposes: Aiding in learning and teaching about various dog breeds.