vit, siglip • 7 items • Updated • 1
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.
Accuracy : 86.81
{'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, SiglipForImageClassification
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"
}
predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Create Gradio interface
iface = gr.Interface(
fn=dog_breed_classification,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Prediction Scores"),
title="Dog Breed Classification",
description="Upload an image to classify it into one of the 121 dog breed categories."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
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.
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Safetensors
Model size
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Tensor type
F32
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Model tree for prithivMLmods/Dog-Breed-120
Base model
google/siglip2-base-patch16-224