# Model card for convnextv2_tiny.ft_plantdoc_384
## Overview
This model classifies diseases from plant images.

- Dataset size: 8000 images
- Number of classes: 39
- Architecture: ConvNeXtV2 Tiny (384)

## Metrics
- mAP: **0.94**
- Accuracy: **0.86**

## Model

## Model Details
- **Model Type:** Image classification
- **Backbone:** convnextv2_tiny.fcmae_ft_in22k_in1k_384
- **Model Stats:**
  - Params (M): 28.6
  - GMACs: 13.1
  - Activations (M): 39.5
  - Image size: 384 x 384
- **Papers:**
  - ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders: https://arxiv.org/abs/2301.00808
- **Original:** https://github.com/facebookresearch/ConvNeXt-V2
- **Dataset:** PlantDoc (8000 images)
- **Pretrain Dataset:** ImageNet-1k


## Model Usage
Built with:
```python
import torch
import timm

# create model
model = timm.create_model(
    "convnextv2_tiny.fcmae_ft_in22k_in1k_384",
    pretrained=False,
    num_classes=39,
    drop_rate=0.2,
    drop_path_rate=0.2,
)

# load weights
state_dict = torch.load("model.bin", map_location="cpu")
model.load_state_dict(state_dict)
```