Image Classification
Transformers
PyTorch
TensorBoard
resnet
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("douwekiela/resnet-18-finetuned-dogfood")
model = AutoModelForImageClassification.from_pretrained("douwekiela/resnet-18-finetuned-dogfood", device_map="auto")Quick Links
resnet-18-finetuned-dogfood
This model is a fine-tuned version of microsoft/resnet-18 on the lewtun/dog_food dataset. It achieves the following results on the evaluation set:
- Loss: 0.2991
- Accuracy: 0.896
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.846 | 1.0 | 16 | 0.2662 | 0.9156 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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Dataset used to train douwekiela/resnet-18-finetuned-dogfood
Spaces using douwekiela/resnet-18-finetuned-dogfood 7
Evaluation results
- Accuracy on lewtun/dog_foodself-reported0.896
- Accuracy on lewtun/dog_foodtest set self-reported0.847
- Precision Macro on lewtun/dog_foodtest set self-reported0.885
- Precision Micro on lewtun/dog_foodtest set self-reported0.847
- Precision Weighted on lewtun/dog_foodtest set self-reported0.894
- Recall Macro on lewtun/dog_foodtest set self-reported0.856
- Recall Micro on lewtun/dog_foodtest set self-reported0.847
- Recall Weighted on lewtun/dog_foodtest set self-reported0.847
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="douwekiela/resnet-18-finetuned-dogfood") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")