---
license: apache-2.0
library_name: timm
tags:
  - image-classification
  - plant-disease
  - cocoa
  - rocm
  - mi300x
  - amd
base_model: facebook/dinov2-large
datasets:
  - ohagwucollinspatrick/amini-cocoa-contamination-dataset
---

# vit_large_patch14_dinov2.lvd142m — Amini Cocoa Contamination (MI300X fine-tune)

Fine-tuned **vit_large_patch14_dinov2.lvd142m** on the **Amini cocoa contamination** dataset
(3 classes: anthracnose, cssvd, healthy).

Trained on a single **AMD Instinct MI300X** using PyTorch + ROCm, as part of the
AMD hackathon. This model uses on-the-fly bbox cropping with randomized context-padding
to improve robustness against detector imprecision.

## Results

| Metric              | This model (ViT-L / MI300X) |
|---------------------|-------------------------------:|
| Test accuracy (TTA) | 0.9284 |
| Macro F1 (TTA)      | 0.9265 |
| Standard acc        | 0.9274 |

TTA rounds: 10.

## Training

- **Backbone:** vit_large_patch14_dinov2.lvd142m
- **Precision:** bf16 (native MI300X)
- **Optimizer:** AdamW, cosine schedule
- **Augmentation:** RandAugment + Mixup/CutMix + Random context-pad [0.0, 0.15]

See `config.yaml` for the full hyperparameter set.

## Usage

```python
import timm, torch

model = timm.create_model(
    "vit_large_patch14_dinov2.lvd142m",
    pretrained=False,
    num_classes=3,
    img_size=224,
)
ckpt = torch.load("best.pt", map_location="cpu", weights_only=False)
model.load_state_dict(ckpt["state_dict"])
model.eval()
```

## Artifacts

- `best.pt` — model weights + training config
- `config.yaml` — hyperparameters used for this run
- `classification_report.txt` — per-class precision / recall / F1
- `confusion_matrix.csv` — 3x3 confusion matrix
- `metrics.json` — standard + TTA scores

## Source

Training code: <https://github.com/genyarko/amd-merolav/tree/main/cocoa_amini_finetuning>