Instructions to use varcoder/resnet-101-CivilEng11k_3Classes-new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use varcoder/resnet-101-CivilEng11k_3Classes-new with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="varcoder/resnet-101-CivilEng11k_3Classes-new") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("varcoder/resnet-101-CivilEng11k_3Classes-new") model = AutoModelForImageClassification.from_pretrained("varcoder/resnet-101-CivilEng11k_3Classes-new") - Notebooks
- Google Colab
- Kaggle
resnet-101-CivilEng11k_3Classes-new
This model is a fine-tuned version of microsoft/resnet-101 on the imagefolder dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.0001
- eval_accuracy: 1.0
- eval_runtime: 58.9592
- eval_samples_per_second: 10.007
- eval_steps_per_second: 0.322
- epoch: 5.0
- step: 140
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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Framework versions
- Transformers 4.37.2
- Pytorch 2.0.1+cpu
- Datasets 2.18.0
- Tokenizers 0.15.1
- Downloads last month
- 3
Model tree for varcoder/resnet-101-CivilEng11k_3Classes-new
Base model
microsoft/resnet-101