Instructions to use shubhamWi91/train84 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shubhamWi91/train84 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="shubhamWi91/train84")# Load model directly from transformers import AutoModelForObjectDetection model = AutoModelForObjectDetection.from_pretrained("shubhamWi91/train84", dtype="auto") - Notebooks
- Google Colab
- Kaggle
train84
This model is a fine-tuned version of jozhang97/deta-swin-large-o365 on the dataloader_hf 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: 1e-05
- train_batch_size: 20
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
- Downloads last month
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Model tree for shubhamWi91/train84
Base model
jozhang97/deta-swin-large-o365