| | --- |
| | tags: |
| | - biology |
| | - pytorch |
| | metrics: |
| | - accuracy |
| | pipeline_tag: image-classification |
| | datasets: |
| | - huggan/inat_butterflies_top10k |
| | language: |
| | - en |
| | --- |
| | Butterfly image classification model that use pre-trained cnn model resnet18 and fine-tuned the last fully connected layer to classify 75 categories of butterfly species. |
| |
|
| | The model used the best checkpoint with 90% test accuracy. |
| |
|
| | The model constructed on Pytorch environment. |
| |
|
| | # Training and testing result: |
| |
|
| | Epoch: 28 Train Loss: 0.17 Train Accuracy: 0.96 Test Accuracy: 0.90 |
| |
|
| | # To use this model you have to: |
| |
|
| | 1. download this model |
| | 2. load pretrained model resnet18 |
| | 3. model_for_predict = models.resnet18(pretrained=True) |
| | 4. load checkpoint from your local |
| | 5. checkpoint = torch.load('pytorch_model.bin') |
| | 7. model_for_predict.load_state_dict(checkpoint) |
| | 8. predict the images |
| | 9. model_for_predict.eval()) |
| | |
| | |
| |  |
