| --- |
| 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. |
