metadata
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'])