| --- |
| license: apache-2.0 |
| library_name: timm |
| tags: |
| - image-classification |
| - plant-disease |
| - dinov2 |
| - rocm |
| - mi300x |
| - amd |
| base_model: facebook/dinov2-large |
| datasets: |
| - mendeley/crop-pest-and-disease-detection |
| --- |
| |
| # DINOv2-Large — CCMT Crop & Disease (MI300X fine-tune) |
|
|
| Fine-tuned **DINOv2-Large** (304M params) on the **CCMT crop-pest-and-disease** dataset |
| (22 classes across cashew, cassava, maize, tomato). |
|
|
| Trained on a single **AMD Instinct MI300X** using PyTorch + ROCm, as a submission to the |
| lablab.ai AMD hackathon **Track 2 — Fine-Tuning on AMD GPUs**. |
|
|
| ## 🌱 Try the live demo |
|
|
| This model is deployed as an interactive Gradio Space — upload a leaf photo and get an instant diagnosis with treatment guidance: |
|
|
| **👉 [https://huggingface.co/spaces/iamcode6/merolav-space](https://huggingface.co/spaces/iamcode6/merolav-space)** |
|
|
| ## Results |
|
|
| | Metric | This model (DINOv2-L / MI300X) | Baseline (EfficientNetB0 / P100) | |
| |---------------------|-------------------------------:|---------------------------------:| |
| | Test accuracy | 0.9706 (TTA) | 0.9316 (TTA) | |
| | Macro F1 | 0.9713 | 0.9348 | |
| | Standard acc (no TTA) | 0.9705 | — | |
|
|
| TTA rounds: 10. |
|
|
| ## Training |
|
|
| - **Backbone:** DINOv2-L ViT-L/14 (self-supervised, LVD-142M pretrain) |
| - **Precision:** bf16 (native MI300X) |
| - **Schedule:** 2-phase — linear probe → full fine-tune with layer-wise LR decay |
| - **Optimizer:** AdamW, cosine schedule, grad-clip 1.0 |
| - **Augmentation:** RandAugment + Mixup/CutMix + RandomErasing |
|
|
| 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=22, |
| img_size=224, |
| ) |
| ckpt = torch.load("best.pt", map_location="cpu", weights_only=False) |
| model.load_state_dict(ckpt["state_dict"]) |
| model.eval() |
| ``` |
|
|
| Class index map is embedded inside the checkpoint under `cfg`; see the training repo |
| for `splits.json` which defines the `class_to_idx` mapping. |
|
|
| ## 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` — 22×22 confusion matrix |
| - `metrics.json` — standard + TTA scores |
|
|
| ## Source |
|
|
| Training code: <https://github.com/genyarko/amd-merolav/tree/main/track2_finetuning> |
|
|
