# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("Antdochi/results")
model = AutoModelForImageClassification.from_pretrained("Antdochi/results")Quick Links
results
This model is a fine-tuned version of microsoft/resnet-101 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1744
- Accuracy: 0.9428
- F1: 0.9428
- Precision: 0.9428
- Recall: 0.9428
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: 0.01
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.2883 | 1.0 | 99 | 0.3757 | 0.8563 | 0.8563 | 0.8563 | 0.8563 |
| 0.2195 | 2.0 | 198 | 0.2293 | 0.9178 | 0.9178 | 0.9178 | 0.9178 |
| 0.1936 | 3.0 | 297 | 0.2120 | 0.9149 | 0.9149 | 0.9149 | 0.9149 |
| 0.163 | 4.0 | 396 | 0.1744 | 0.9428 | 0.9428 | 0.9428 | 0.9428 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Model tree for Antdochi/results
Base model
microsoft/resnet-101
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Antdochi/results") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")