Instructions to use hwm21/020924-detr-segment-detect-debug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hwm21/020924-detr-segment-detect-debug with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="hwm21/020924-detr-segment-detect-debug")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("hwm21/020924-detr-segment-detect-debug") model = AutoModelForObjectDetection.from_pretrained("hwm21/020924-detr-segment-detect-debug", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from hwm21/020924-detr-segment-detect-debug: direct link, hf CLI and curl.
- Browser
- Download file 1.57 kB
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https://huggingface.co/hwm21/020924-detr-segment-detect-debug/resolve/main/README.md
- Command line
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hf download hf://hwm21/020924-detr-segment-detect-debug/README.md
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curl -L -o README.md https://huggingface.co/hwm21/020924-detr-segment-detect-debug/resolve/main/README.md
1.57 kB
metadata
library_name: transformers
license: apache-2.0
base_model: facebook/detr-resnet-50-dc5
tags:
- generated_from_trainer
model-index:
- name: 020924-detr-segment-detect-debug
results: []
020924-detr-segment-detect-debug
This model is a fine-tuned version of facebook/detr-resnet-50-dc5 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 6.5982
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 200
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 6.9812 | 0.7692 | 50 | 7.6597 |
| 5.1829 | 1.5385 | 100 | 7.4352 |
| 4.5311 | 2.3077 | 150 | 7.1001 |
| 3.9432 | 3.0769 | 200 | 6.5982 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1