Modelpublic

Bektur756/vit-cats-dogs-pandas

sourceHugging Faceapache-2.0updated 8d agoView on Hugging Face

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Model Card

ViT Cats, Dogs and Pandas Classifier

Vision Transformer image classifier for three classes:

—0: cats
—1: dogs
—2: panda

Base model

google/vit-base-patch16-224

The classifier uses the output representation of the first [CLS] token and a Linear(768, 3) classification head.

Dataset

Melisa13/Animals_dataset

Split	Images	Per class
Train	408	136
Validation	72	24
Test	120	40

Exact image duplicates between splits: 0.

Image preprocessing

—RGB input
—Resize and crop to 224×224
—Patch size: 16×16
—196 image patches
—197 tokens including [CLS]
—Normalization mean: (0.5, 0.5, 0.5)
—Normalization standard deviation: (0.5, 0.5, 0.5)

Fine-tuning

The following components were trainable:

—Transformer blocks 10 and 11
—Final LayerNorm
—Linear(768, 3) classifier

Configuration	Value
Total parameters	85,800,963
Trainable parameters	14,179,587
Trainable share	16.53%
Epochs completed	7
Batch size	16
Learning rate	2e-5
Weight decay	0.01

Results

Split	Accuracy	Macro F1
Validation	98.61%	98.61%
Test	100.00%	100.00%

Test classification report:

Class	Precision	Recall	F1	Support
Cats	1.0000	1.0000	1.0000	40
Dogs	1.0000	1.0000	1.0000	40
Panda	1.0000	1.0000	1.0000	40

End-to-end latency

Latency includes image preprocessing and model inference with batch size 1.

Device	Mean	Median	P95	Throughput
Tesla T4	21.86 ms	18.37 ms	39.92 ms	45.75 images/s
Colab CPU	599.39 ms	371.03 ms	1550.67 ms	1.67 images/s

Usage

python

from PIL import Image
import torch

from transformers import (
    AutoImageProcessor,
    AutoModelForImageClassification,
)

MODEL_NAME = "Bektur756/vit-cats-dogs-pandas"

processor = AutoImageProcessor.from_pretrained(
    MODEL_NAME
)

model = AutoModelForImageClassification.from_pretrained(
    MODEL_NAME
)

image = Image.open(
    "animal.jpg"
).convert(
    "RGB"
)

inputs = processor(
    images=image,
    return_tensors="pt",
)

with torch.inference_mode():
    logits = model(
        **inputs
    ).logits

probabilities = torch.softmax(
    logits,
    dim=-1,
)[0]

predicted_id = int(
    probabilities.argmax()
)

print(
    model.config.id2label[
        predicted_id
    ]
)

print(
    probabilities.tolist()
)

## Limitations

- The test split contains only 120 images.
- The dataset is small and relatively simple.
- Perfect test accuracy does not imply perfect performance on arbitrary images.
- CPU inference is substantially slower than GPU inference.