| # 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 |
|
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|
|
| ## 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) |
| ``` |
|
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