---
datasets:
- frgfm/imagenette
metrics:
- accuracy
pipeline_tag: image-classification
---

### Model Card for Fine-tuned EfficientNet-L on Imagenette
### Model Details

    Model Name: Fine-tuned EfficientNet-L
    
    Type of Model: Image Classification
    
    Version: 1.0


### Model Training

    Data Description: Trained on the Imagenette dataset, a public subset of ImageNet featuring ten easily classifiable classes from diverse categories.
    
    Training Procedure: The model utilized pre-trained weights from EfficientNet-L and was fine-tuned across all layers. Training involved 30 planned epochs with a cosine learning rate schedule starting from 0.0005 to 0. However, training was early stopped after 10 epochs due to convergence.
    
    Training Configuration:

```
num_epochs: 30.0
init_lr: 0.0005

training_modifiers:
  - !EpochRangeModifier
    start_epoch: 0.0
    end_epoch: eval(num_epochs)

  - !LearningRateFunctionModifier
    final_lr: 0.0
    init_lr: eval(init_lr)
    lr_func: cosine
    start_epoch: 0.0
    end_epoch: eval(num_epochs)
```

### Evaluation Metrics: The model's performance was monitored using the following metrics:

    Val Loss: 0.32155620951985703

    Top 1 Accuracy: 92%


### Usage :

Clone repo, then you can use the model in the following way

```
import torch
from  torchvision.models import efficientnet_v2_l

NUM_LABELS = 10
checkpoint = torch.load("/effnet_L_finetuned/pytorch_model.bin")
model = efficientnet_v2_l()
model.classifier[1] = torch.nn.Linear(model.classifier[1].in_features, NUM_LABELS)
model.load_state_dict(checkpoint['state_dict'])
```