transformers tensorboard safetensors segformer vision image-segmentation generated_from_trainer base_model:nvidia/mit-b0 license:other endpoints_compatible region:us
---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b1-finetuned-tennisdata
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# segformer-b1-finetuned-tennisdata

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the julia-wenkmann/TennisSegmentation dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0158
- eval_mean_iou: 0.4994
- eval_mean_accuracy: 0.6483
- eval_overall_accuracy: 0.9915
- eval_accuracy_undefined: nan
- eval_accuracy_object: nan
- eval_accuracy_ball: 0.0
- eval_accuracy_playerTop: 0.7071
- eval_accuracy_playerBottom: 0.8904
- eval_accuracy_court: 0.9956
- eval_iou_undefined: 0.0
- eval_iou_object: nan
- eval_iou_ball: 0.0
- eval_iou_playerTop: 0.7071
- eval_iou_playerBottom: 0.7968
- eval_iou_court: 0.9931
- eval_runtime: 8.2745
- eval_samples_per_second: 2.175
- eval_steps_per_second: 1.088
- epoch: 29.41
- step: 1000

## 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: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
