Petcare Derm Yolov8 Cls v0
⚠️ DIRECTIONAL BASELINE ONLY — NOT a clinical or diagnostic model. This is an early v0 sanity-check baseline for the PetCare project. It was trained on a small, noisy, web-sourced dataset with coarse "lumped" labels...
Model source
- Kind
- Other
- File size
- 2.83 MB
- Base model
- Ultralytics/YOLOv8
- Version
- vRevision 69a0e92
- Updated
- Nov 2, 2026
- Downloads
- 14
- Source
- Hugging Face
Source description
PetCare Dermatology Triage — YOLOv8n-cls (v0, coarse baseline)
⚠️ DIRECTIONAL BASELINE ONLY — NOT a clinical or diagnostic model. This is an early v0 sanity-check baseline for the PetCare project. It was trained on a small, noisy, web-sourced dataset with coarse "lumped" labels and an 88-image validation split. Metrics below indicate the pipeline works end-to-end; they are not a measure of real-world clinical accuracy. Do not use for veterinary diagnosis.
Summary
- Architecture: YOLOv8n-cls (Ultralytics classification mode), fine-tuned from
yolov8n-cls.pt - Task: 4-class coarse classification of dog skin condition from a single image
- Classes:
atopic_dermatitis,bacterial_pyoderma,fungal_malassezia,healthy - Input size: 224×224
- Best val top-1 accuracy: 0.8523 (epoch 24 of 50)
- Final-epoch val top-1: 0.8295
Training
| Setting | Value |
|---|---|
| Base weights | yolov8n-cls.pt (Ultralytics, AGPL-3.0) |
| Epochs | 50 |
| Batch size | 32 |
| Image size | 224 |
| Optimizer | auto |
| Hardware | 1× Tesla T4 (Lightning AI Studio), ~2 min |
| Seed | 0 (deterministic) |
Full hyperparameters are in args.yaml. Per-epoch metrics are in
results.csv; training curves in results.png; confusion
matrices in confusion_matrix.png /
confusion_matrix_normalized.png.
Data
- Source:
yashmotiani/dogs-skin-disease-dataset(CC0) - Size: 439 images total → 351 train / 88 val across 4 coarse classes
- Caveats: small, web-scraped, class-imbalanced, coarse labels that lump distinct presentations together. Not a curated clinical gold set.
Usage
from ultralytics import YOLO
model = YOLO("best.pt") # or hf_hub_download the weights first
result = model.predict("dog_skin.jpg")[0]
print(result.names[result.probs.top1], float(result.probs.top1conf))
Files
weights/best.pt— best checkpoint (epoch 24, top-1 0.8523) — use this for inferenceweights/last.pt— final checkpoint (epoch 50)args.yaml,results.csv,results.png,confusion_matrix*.png,*_batch*.jpg— training provenance
Limitations & intended use
Intended purpose is engineering signal for the PetCare triage pipeline, not medical use. Known limitations: tiny validation set (88 imgs), coarse labels, web-domain shift, no clinical validation, and single-image, single-view assumptions. A future version will use a curated gold eval set and a finer, clinically meaningful label taxonomy.
License
Inherits AGPL-3.0 from the Ultralytics YOLOv8 base weights.
Sources
1 source
Sources
1 sourceVerified Nov 2
Download files
Model artifacts
2 artifacts
Model artifacts
2 artifactsSource excerpts
3 excerpts
Source excerpts
3 excerpts--- license: agpl-3.0 library_name: ultralytics pipeline_tag: image-classification tags: - ultralytics - yolov8 - yolov8-cls - image-classification - veterinary - dermatology - pets - petcare base_model: Ultralytics/YOLOv8 metrics: - accuracy model-index: - name: petcare-derm-yolov8-cls-v0-coarse results: - task: type: image-classification name: Coarse dog skin-condition classification dataset: type: yashmotiani/dogs-skin-disease-dataset name: dogs-skin-disease-dataset (val split) metrics: - type: accuracy name: Top-1 Accuracy value: 0.8523 --- # PetCare Dermatology Triage — YOLOv8n-cls (v0, coarse baseline) **⚠️ DIRECTIONAL BASELINE ONLY — NOT a clinical or diagnostic model.** This is an early v0 sanity-check baseline for the PetCare project. It was trained on a small, noisy, web-sourced dataset with coarse "lumped" labels and an 88-image validation split. Metrics below indicate the pipeline works end-to-end; they are **not** a measure of real-world clinical accuracy. Do not use for veterinary diagnosis. ## Summary - **Architecture:** YOLOv8n-cls (Ultralytics classification mode), fine-tuned from `yolov8n-cls.pt` - **Task:** 4-class coarse classification of dog skin condition from a single image - **Classes:** `atopic_dermatitis`, `bacterial_pyoderma`, `fungal_malassezia`, `healthy` - **Input size:** 224×224 - **Best val top-1 accuracy:** **0.8523** (epoch 24 of 50) - **Final-epoch val top-1:** 0.8295 ## Training | Setting | Value | | -------------- | ------------------------------------------------- | | Base weights | `yolov8n-cls.pt` (Ultralytics, AGPL-3.0) | | Epochs | 50 | | Batch size | 32 | | Image size | 224 | | Optimizer | auto | | Hardware | 1× Tesla T4 (Lightning AI Studio), ~2 min | | Seed | 0 (deterministic) | Full hyperparameters are in [`args.yaml`](./args.yaml). Per-epoch metrics are in [`results.csv`](./results.csv); training curves in [`results.png`](./results.png); confusion matrices in [`confusion_matrix.png`](./confusion_matrix.png) / [`confusion_matrix_normalized.png`](./confusion_matrix_normalized.png). ## Data - **Source:** [`yashmotiani/dogs-skin-disease-dataset`](https://huggingface.co/datasets/yashmotiani/dogs-skin-disease-dataset) (CC0) - **Size:** 439 images total → 351 train / 88 val across 4 coarse classes - **Caveats:** small, web-scraped, class-imbalanced, coarse labels that lump distinct pre...
Source context: 14 downloads · 0 likes · Pipeline image-classification · Library ultralytics · Repo rosewt/petcare-derm-yolov8-cls-v0
Source context: 17 downloads · 0 likes · Pipeline image-classification · Library ultralytics · Repo rosewt/petcare-derm-yolov8-cls-v0
