Instructions to use FriendlyBohen/test-run with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FriendlyBohen/test-run with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-object-detection", model="FriendlyBohen/test-run")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection processor = AutoProcessor.from_pretrained("FriendlyBohen/test-run") model = AutoModelForZeroShotObjectDetection.from_pretrained("FriendlyBohen/test-run", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from FriendlyBohen/test-run: direct link, hf CLI and curl.
- Browser
- Download file 1.25 kB
-
https://huggingface.co/FriendlyBohen/test-run/resolve/main/README.md
- Command line
-
hf download hf://FriendlyBohen/test-run/README.md
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curl -L -o README.md https://huggingface.co/FriendlyBohen/test-run/resolve/main/README.md
1.25 kB
metadata
library_name: transformers
license: apache-2.0
base_model: IDEA-Research/grounding-dino-tiny
tags:
- generated_from_trainer
model-index:
- name: test-run
results: []
test-run
This model is a fine-tuned version of IDEA-Research/grounding-dino-tiny on an unknown dataset.
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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 5
Training results
Framework versions
- Transformers 4.53.1
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.21.2