Instructions to use yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia") 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("yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia") model = AutoModelForImageClassification.from_pretrained("yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia", device_map="auto") - Notebooks
- Google Colab
- Kaggle
regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia
This model is a fine-tuned version of mlx-vision/regnet_y_400mf-mlxim on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.3384
- Accuracy: 0.8707
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4107 | 1.0 | 74 | 0.6099 | 0.6830 |
| 0.3186 | 2.0 | 148 | 0.5402 | 0.7452 |
| 0.3731 | 3.0 | 222 | 0.3384 | 0.8707 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0+cpu
- Datasets 4.4.1
- Tokenizers 0.22.1
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Model tree for yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia
Base model
mlx-vision/regnet_y_400mf-mlximEvaluation results
- Accuracy on imagefolderself-reported0.871