Instructions to use the-clueless-classifier/SCUTFVD-resnet50-fold01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use the-clueless-classifier/SCUTFVD-resnet50-fold01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="the-clueless-classifier/SCUTFVD-resnet50-fold01") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("the-clueless-classifier/SCUTFVD-resnet50-fold01") model = AutoModelForImageClassification.from_pretrained("the-clueless-classifier/SCUTFVD-resnet50-fold01", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: microsoft/resnet-50 | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: SCUTFVD-resnet50-fold01 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # SCUTFVD-resnet50-fold01 | |
| This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3516 | |
| - Accuracy: 1.0 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 0.0001 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 10 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 3.3847 | 1.0 | 75 | 5.0682 | 0.1875 | | |
| | 2.5630 | 2.0 | 150 | 4.0558 | 0.25 | | |
| | 2.0217 | 3.0 | 225 | 4.9411 | 0.2188 | | |
| | 1.7360 | 4.0 | 300 | 2.1920 | 0.8438 | | |
| | 1.4617 | 5.0 | 375 | 2.9501 | 0.5 | | |
| | 1.2141 | 6.0 | 450 | 2.4084 | 0.5625 | | |
| | 0.9616 | 7.0 | 525 | 0.7715 | 1.0 | | |
| | 0.8966 | 8.0 | 600 | 1.4465 | 1.0 | | |
| | 0.7456 | 9.0 | 675 | 0.7191 | 1.0 | | |
| | 0.7394 | 10.0 | 750 | 0.3516 | 1.0 | | |
| ### Framework versions | |
| - Transformers 5.1.0 | |
| - Pytorch 2.10.0+cu128 | |
| - Datasets 4.8.4 | |
| - Tokenizers 0.22.2 | |