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--- |
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library_name: transformers |
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license: other |
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base_model: nvidia/mit-b0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- scene_parse_150 |
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model-index: |
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- name: segformer-b0-scene-parse-150 |
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results: [] |
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metrics: |
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- mean_iou |
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pipeline_tag: image-segmentation |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Segformer-b0-scene-parse-150 |
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This model is a fine-tuned version of the [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) model, specifically trained on the `scene_parse_150` dataset. The goal of this model is to perform semantic segmentation for various scene parsing tasks. |
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### Evaluation Results: |
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The model achieved the following results on the evaluation dataset: |
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- **Loss**: 1.8435 |
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- **Mean IoU**: 0.0881 |
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- **Mean Accuracy**: 0.1619 |
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- **Overall Accuracy**: 0.6663 |
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**Per-Category IoU** and **Per-Category Accuracy** values are available but sparse, indicating performance variability across different categories. |
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## Model Description |
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Segformer-b0 is based on a modified version of the Vision Transformer (ViT) architecture, adapted for efficient segmentation tasks. It incorporates hierarchical features to generate high-quality segmentation maps. |
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More detailed model descriptions, including architectural adjustments or preprocessing requirements, are needed. |
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## Intended Uses & Limitations |
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- **Use Cases**: Suitable for scene parsing and segmentation tasks in environments with diverse visual categories. |
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- **Limitations**: Performance varies significantly between categories, as seen from sparse accuracy and IoU metrics. The model may struggle with underrepresented classes or categories with fewer visual distinctions. |
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- Further details on intended domains and limitations are needed. |
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## Training and Evaluation Data |
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The model was trained on the `scene_parse_150` dataset, which consists of diverse visual scenes with 150 unique semantic categories. Further information on dataset specifics and any preprocessing steps is needed. |
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## Training Procedure |
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### Hyperparameters: |
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- **Learning Rate**: 6e-05 |
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- **Training Batch Size**: 2 |
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- **Evaluation Batch Size**: 2 |
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- **Seed**: 42 |
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- **Optimizer**: Adam (betas=(0.9, 0.999), epsilon=1e-08) |
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- **Learning Rate Scheduler**: Linear |
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- **Number of Epochs**: 50 |
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### Training Results: |
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The model was trained over 50 epochs, but further details regarding its convergence behavior, training duration, and hardware environment could provide additional insights. |
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## Framework Versions: |
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- Transformers 4.44.2 |
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- PyTorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
