Update README.md

README.md

@@ -1,83 +1,12 @@
 ---
-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: []
+title: Pet Classification Tool
+emoji: 🐕
+colorFrom: indigo
+colorTo: blue
+sdk: gradio
+sdk_version: 4.16.0
+app_file: app.py
+pinned: false
 ---
 
-# vit-base-oxford-iiit-pets
-
-This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.1893
-- Accuracy: 0.9405
-
-## 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 |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.3976        | 1.0   | 370  | 0.2921          | 0.9364   |
-| 0.2273        | 2.0   | 740  | 0.2257          | 0.9445   |
-| 0.1742        | 3.0   | 1110 | 0.2102          | 0.9445   |
-| 0.1352        | 4.0   | 1480 | 0.2023          | 0.9459   |
-| 0.1326        | 5.0   | 1850 | 0.2006          | 0.9459   |
-
-### Framework versions
-
-- Transformers 4.50.0
-- Pytorch 2.6.0+cu124
-- Datasets 3.4.1
-- Tokenizers 0.21.1
-
-## Zero-Shot classification model
-
-This section compares the performance of a zero-shot model (`openai/clip-vit-large-patch14`) on the Oxford Pets dataset (`pcuenq/oxford-pets`).
-
-- **Model used**: `openai/clip-vit-large-patch14`
-- **Dataset**: `pcuenq/oxford-pets` (train split)
-- **Evaluation Task**: Zero-Shot Image Classification  
-- **Candidate Labels**: 37 pet breeds from the dataset
-
-
-### Results:
-
-Zero-Shot Evaluation mit CLIP:
-- **Accuracy**: 0.8800
-- **Precision**: 0.8768
-- **Recall**: 0.8800
-
-Evaluated using Hugging Face `transformers` pipeline and `sklearn.metrics` on the full training set.
-