Image Classification
Transformers
TensorBoard
Safetensors
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use djbp/swin-base-patch4-window7-224-MM_Classification_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use djbp/swin-base-patch4-window7-224-MM_Classification_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="djbp/swin-base-patch4-window7-224-MM_Classification_base") 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("djbp/swin-base-patch4-window7-224-MM_Classification_base") model = AutoModelForImageClassification.from_pretrained("djbp/swin-base-patch4-window7-224-MM_Classification_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: microsoft/swin-base-patch4-window7-224 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - imagefolder | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: swin-base-patch4-window7-224-MM_Classification_base | |
| results: | |
| - task: | |
| name: Image Classification | |
| type: image-classification | |
| dataset: | |
| name: imagefolder | |
| type: imagefolder | |
| config: default | |
| split: validation | |
| args: default | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.8770806658130602 | |
| <!-- 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. --> | |
| # swin-base-patch4-window7-224-MM_Classification_base | |
| This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the imagefolder dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2998 | |
| - Accuracy: 0.8771 | |
| ## 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: 5e-05 | |
| - train_batch_size: 128 | |
| - eval_batch_size: 128 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 512 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - num_epochs: 10 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.887 | 1.0 | 19 | 0.4012 | 0.8566 | | |
| | 0.4302 | 2.0 | 38 | 0.3361 | 0.8656 | | |
| | 0.3477 | 3.0 | 57 | 0.3272 | 0.8656 | | |
| | 0.3281 | 4.0 | 76 | 0.3129 | 0.8694 | | |
| | 0.308 | 5.0 | 95 | 0.2984 | 0.8732 | | |
| | 0.2821 | 6.0 | 114 | 0.3010 | 0.8694 | | |
| | 0.2763 | 7.0 | 133 | 0.2998 | 0.8771 | | |
| | 0.2607 | 8.0 | 152 | 0.2938 | 0.8720 | | |
| | 0.2502 | 9.0 | 171 | 0.2990 | 0.8732 | | |
| | 0.2337 | 10.0 | 190 | 0.2978 | 0.8758 | | |
| ### Framework versions | |
| - Transformers 4.43.3 | |
| - Pytorch 1.13.1+cu117 | |
| - Datasets 2.20.0 | |
| - Tokenizers 0.19.1 | |