Image Segmentation
Transformers
Safetensors
mask2former
instance-segmentation
vision
Generated from Trainer
Instructions to use amnraw/finetune-instance-segmentation-posture with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amnraw/finetune-instance-segmentation-posture with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="amnraw/finetune-instance-segmentation-posture")# Load model directly from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation processor = AutoImageProcessor.from_pretrained("amnraw/finetune-instance-segmentation-posture") model = Mask2FormerForUniversalSegmentation.from_pretrained("amnraw/finetune-instance-segmentation-posture") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: other | |
| base_model: facebook/mask2former-swin-tiny-coco-instance | |
| tags: | |
| - image-segmentation | |
| - instance-segmentation | |
| - vision | |
| - generated_from_trainer | |
| model-index: | |
| - name: finetune-instance-segmentation-posture | |
| results: [] | |
| <!-- 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. --> | |
| # finetune-instance-segmentation-posture | |
| This model is a fine-tuned version of [facebook/mask2former-swin-tiny-coco-instance](https://huggingface.co/facebook/mask2former-swin-tiny-coco-instance) on the qubvel-hf/ade20k-mini dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 30.5625 | |
| - Map: 0.2089 | |
| - Map 50: 0.4081 | |
| - Map 75: 0.1963 | |
| - Map Small: 0.1412 | |
| - Map Medium: 0.6277 | |
| - Map Large: 0.8115 | |
| - Mar 1: 0.0944 | |
| - Mar 10: 0.25 | |
| - Mar 100: 0.2879 | |
| - Mar Small: 0.2137 | |
| - Mar Medium: 0.7147 | |
| - Mar Large: 0.8531 | |
| - Map Person: 0.1381 | |
| - Mar 100 Person: 0.2036 | |
| - Map Car: 0.2797 | |
| - Mar 100 Car: 0.3722 | |
| ## 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: 1e-05 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 16 | |
| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: constant | |
| - num_epochs: 2.0 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Person | Mar 100 Person | Map Car | Mar 100 Car | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:----------:|:--------------:|:-------:|:-----------:| | |
| | 34.1337 | 1.0 | 100 | 32.3431 | 0.1958 | 0.3913 | 0.181 | 0.1319 | 0.6051 | 0.7775 | 0.0924 | 0.2465 | 0.2845 | 0.2104 | 0.7094 | 0.8587 | 0.1243 | 0.2001 | 0.2673 | 0.3689 | | |
| | 28.4514 | 2.0 | 200 | 30.5625 | 0.2089 | 0.4081 | 0.1963 | 0.1412 | 0.6277 | 0.8115 | 0.0944 | 0.25 | 0.2879 | 0.2137 | 0.7147 | 0.8531 | 0.1381 | 0.2036 | 0.2797 | 0.3722 | | |
| ### Framework versions | |
| - Transformers 4.47.0.dev0 | |
| - Pytorch 2.3.1+cu121 | |
| - Datasets 3.0.2 | |
| - Tokenizers 0.20.1 | |