from transformers import pipeline
checkpoint = "openai/clip-vit-large-patch14"
detector = pipeline(model=checkpoint, task="zero-shot-image-classification")
Accuracy: 0.8800
Precision: 0.8768
Recall: 0.8800
/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use zero_division parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-oxford-iiit-pets
results: []
vit-base-oxford-iiit-pets
This model is a fine-tuned version of
google/vit-base-patch16-224 on the pcuenq/oxford-pets dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1969
- Accuracy: 0.9350
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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|
| 0.3902 | 1.0 | 370 | 0.2933 | 0.9229 |
| 0.2214 | 2.0 | 740 | 0.2155 | 0.9296 |
| 0.1723 | 3.0 | 1110 | 0.2022 | 0.9269 |
| 0.1552 | 4.0 | 1480 | 0.1859 | 0.9418 |
| 0.1287 | 5.0 | 1850 | 0.1831 | 0.9432 |
Results Abgabe
from transformers import pipeline
checkpoint = "openai/clip-vit-large-patch14"
detector = pipeline(model=checkpoint, task="zero-shot-image-classification")
Accuracy: 0.8800
Precision: 0.8768
Recall: 0.8800
/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use zero_division parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
