---
license: cc-by-nc-sa-4.0
base_model: google/vit-base-patch16-224-in21k
datasets:
- Bubunur/dog-cat-panda
pipeline_tag: image-classification
library_name: transformers
tags:
- image-classification
- vision
- vit
metrics:
- accuracy
- f1
model-index:
- name: animals-classifier
  results:
  - task:
      type: image-classification
    dataset:
      name: Bubunur/dog-cat-panda
      type: Bubunur/dog-cat-panda
      split: test
    metrics:
    - type: accuracy
      value: 0.9702
    - type: f1
      name: F1 (macro)
      value: 0.9702
---

# Animals Classifier: Dog / Cat / Panda

A Vision Transformer fine-tuned to classify images into 3 classes: **cat**, **dog**, and **panda** (giant panda).

Base model: [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k)

## Usage

```python
from transformers import pipeline

clf = pipeline("image-classification", model="Bubunur/animals-classifier")
clf("photo.jpg")
# [{'label': 'cat', 'score': 0.93}, ...]
```

## Results

Evaluated on the held-out test split (369 images, 123 per class):

| Class | Precision | Recall | F1 |
|---|---|---|---|
| Cat | 0.9667 | 0.9431 | 0.9547 |
| Dog | 0.9444 | 0.9675 | 0.9558 |
| Panda | 1.0000 | 1.0000 | 1.0000 |
| **Overall accuracy** | | | **0.9702** |

Confusion matrix (rows = true, columns = predicted):

|  | Cat | Dog | Panda |
|---|---|---|---|
| **Cat** | 116 | 7 | 0 |
| **Dog** | 4 | 119 | 0 |
| **Panda** | 0 | 0 | 123 |

## Training data

[Bubunur/dog-cat-panda](https://huggingface.co/datasets/Bubunur/dog-cat-panda): photos collected from iNaturalist
(Creative Commons licenses only). The raw data contained many non-photo observations (skulls, bones, tracks,
empty camera-trap frames), so the dataset was cleaned with zero-shot filtering by
[CLIP ViT-L/14](https://huggingface.co/openai/clip-vit-large-patch14) (confidence threshold 0.6)
and balanced to 614 images per class.

Split: 1,252 train / 221 validation / 369 test.

## Training procedure

- Epochs: 8 (best checkpoint: epoch 6, selected by validation accuracy)
- Learning rate: 5e-5, warmup ratio 0.06, weight decay 0.01
- Batch size: 16
- Label smoothing: 0.1
- Mixed precision (fp16) on a single T4 GPU
- Augmentations (train only): random resized crop (70–100%), horizontal flip,
  rotation (±15°), color jitter (brightness / contrast / saturation 0.3)

## Limitations

- **Only 3 classes.** Every input is assigned to cat, dog, or panda, even if it shows something else
  (e.g. a fox will be classified as a dog or a cat).
- **Confidence scores are capped at ~0.93** because of label smoothing (0.1). A score of 0.93 means
  maximum confidence, not uncertainty.
- **Cat/dog confusion** remains on hard images: small or distant animals, partially hidden animals,
  fluffy small dog breeds.
- **The test set was filtered by CLIP**, so it may be easier than real-world images, and the reported
  accuracy may be optimistic.

## License

Released under **CC BY-NC-SA 4.0** to match the license of the training data
(which includes CC BY-NC and CC BY-SA images). Non-commercial use only.