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semantic-segmentation

Hemg / semantic-segmentation

Run on huggingface.co
huggingface.co
Total runs: 9
24-hour runs: 0
7-day runs: 2
30-day runs: 2
Model's Last Updated: May 27 2024

Introduction of semantic-segmentation

Model Details of semantic-segmentation

semantic-segmentation

This model is a fine-tuned version of nvidia/mit-b0 on the scene_parse_150 dataset. It achieves the following results on the evaluation set:

  • Loss: 4.9789
  • Mean Iou: 0.0046
  • Mean Accuracy: 0.0211
  • Overall Accuracy: 0.0890
  • Per Category Iou: [0.0, 0.0, 0.3370586994883633, 0.0011783885315329683, 0.17971457696228338, 0.0, 0.0, 0.0, 0.0, 0.0005691728633067107, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0013089005235602095, 0.0, 0.0, 0.0, 0.0653821624410708, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0011844131232974062, 0.0, 0.00022798084960863287, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0]
  • Per Category Accuracy: [0.0, 0.0, 0.578173337775451, 0.0014208794495961712, 0.18539355381460645, 0.0, nan, 0.0, 0.0, 0.0006200661403883081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002337228714524207, nan, 0.0, nan, 0.1557566040616888, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.002759128115515497, nan, 0.0008389261744966443, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
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: 1
Training results
Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Per Category Iou Per Category Accuracy
4.9015 1.0 20 4.9789 0.0046 0.0211 0.0890 [0.0, 0.0, 0.3370586994883633, 0.0011783885315329683, 0.17971457696228338, 0.0, 0.0, 0.0, 0.0, 0.0005691728633067107, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0013089005235602095, 0.0, 0.0, 0.0, 0.0653821624410708, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0011844131232974062, 0.0, 0.00022798084960863287, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0] [0.0, 0.0, 0.578173337775451, 0.0014208794495961712, 0.18539355381460645, 0.0, nan, 0.0, 0.0, 0.0006200661403883081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002337228714524207, nan, 0.0, nan, 0.1557566040616888, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.002759128115515497, nan, 0.0008389261744966443, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
Framework versions
  • Transformers 4.39.3
  • Pytorch 2.1.2+cpu
  • Datasets 2.18.0
  • Tokenizers 0.15.2

Runs of Hemg semantic-segmentation on huggingface.co

9
Total runs
0
24-hour runs
0
3-day runs
2
7-day runs
2
30-day runs

More Information About semantic-segmentation huggingface.co Model

More semantic-segmentation license Visit here:

https://choosealicense.com/licenses/other

semantic-segmentation huggingface.co

semantic-segmentation huggingface.co is an AI model on huggingface.co that provides semantic-segmentation's model effect (), which can be used instantly with this Hemg semantic-segmentation model. huggingface.co supports a free trial of the semantic-segmentation model, and also provides paid use of the semantic-segmentation. Support call semantic-segmentation model through api, including Node.js, Python, http.

semantic-segmentation huggingface.co Url

https://huggingface.co/Hemg/semantic-segmentation

Hemg semantic-segmentation online free

semantic-segmentation huggingface.co is an online trial and call api platform, which integrates semantic-segmentation's modeling effects, including api services, and provides a free online trial of semantic-segmentation, you can try semantic-segmentation online for free by clicking the link below.

Hemg semantic-segmentation online free url in huggingface.co:

https://huggingface.co/Hemg/semantic-segmentation

semantic-segmentation install

semantic-segmentation is an open source model from GitHub that offers a free installation service, and any user can find semantic-segmentation on GitHub to install. At the same time, huggingface.co provides the effect of semantic-segmentation install, users can directly use semantic-segmentation installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

semantic-segmentation install url in huggingface.co:

https://huggingface.co/Hemg/semantic-segmentation

Url of semantic-segmentation

semantic-segmentation huggingface.co Url

Provider of semantic-segmentation huggingface.co

Hemg
ORGANIZATIONS
huggingface.co

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