---
license: apache-2.0
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- dog
- classify
---
![bnbxfgbnx.png](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F65bb837dbfb878f46c77de4c%2FXKT2Um6LdpzG64N1djM7s.png)

# **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.

> [!Note]
> Accuracy : 86.81

```py
{'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🤗**

```python
!pip install -q transformers torch pillow gradio
```

```python
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.