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library_name: pytorch
pipeline_tag: image-classification
tags:
- efficientnet
- efficientnet-b3
- plant-classification
- agriculture
---
# Plant Classifier — EfficientNet-B3, 39 classes
Frozen PyTorch checkpoint for plant/crop classification. The model was trained with supervised contrastive pretraining, cross-entropy fine-tuning, and TrivialAugment.
## Intended use
The model predicts the plant identity before a crop-specific disease detector and recommendation system. It is a plant classifier, not a disease classifier.
## Input preprocessing
- Convert the image to RGB.
- Resize the longest side to 380 pixels while preserving aspect ratio.
- Pad to 380 × 380 with black pixels.
- Normalize with ImageNet mean `(0.485, 0.456, 0.406)` and standard deviation `(0.229, 0.224, 0.225)`.
- Do not apply training augmentation during inference.
The checkpoint contains `model` and `class_to_idx`. Reconstruct `torchvision.models.efficientnet_b3(weights=None)`, replace the final classifier with a 39-output linear layer, and load `checkpoint["model"]`.
For confidence scores, apply softmax directly to the logits. The deployed API intentionally uses raw, uncalibrated probabilities.
## Frozen evaluation
| Metric | Result |
|---|---:|
| Validation macro F1 | 0.933455 |
| Test macro F1 | 0.929491 |
| Test macro recall | 0.930658 |
| Test accuracy | 0.931547 |
| Test top-3 accuracy | 0.981436 |
## Files
- `model.pt`: frozen EfficientNet-B3 checkpoint.
- `class_to_idx.json`: authoritative output-index mapping.
- `model_config.json`: architecture and preprocessing contract.
- `SHA256SUMS`: checkpoint integrity checksum.
- `FROZEN_MODEL.md`: frozen-run provenance and metrics.
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