Instructions to use deshim/detr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deshim/detr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="deshim/detr")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("deshim/detr") model = AutoModelForObjectDetection.from_pretrained("deshim/detr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from deshim/detr: direct link, hf CLI and curl.
- Browser
- Download file 1.37 kB
-
https://huggingface.co/deshim/detr/resolve/main/README.md
- Command line
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hf download hf://deshim/detr/README.md
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curl -L -o README.md https://huggingface.co/deshim/detr/resolve/main/README.md
1.37 kB
metadata
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
model-index:
- name: detr
results: []
detr
This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0063
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: 0.0002
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.6951 | 1.0 | 313 | 1.1553 |
| 1.0966 | 2.0 | 626 | 1.0063 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2