Add Comparison-Table to Readme

README.md

@@ -41,21 +41,22 @@ library_name: transformers
 ---
 
 ## 📊 Performance Metrics
-
-### **Test Set Results**
-
-| Dataset | Sequence Accuracy | Character Error Rate (CER) |
-| --- | --- | --- |
-| **[hammer888/captcha-data](https://huggingface.co/datasets/hammer888/captcha-data)** | `92.98%` | `1.59%` |
-| **[Python Captcha Library](https://captcha.lepture.com/)** | `86.20%` | `2.53%` |
-
-### **Hardware & Efficiency**
-| Metric | Value |
-| --- | --- |
-| **Model Parameters** | `3,570,943` |
-| **Model Size (Disk)** | `14.3 MB` |
-| **Throughput (Images/sec)** | `447.26 – 467.29` |
-| **Compute Hardware** | **NVIDIA RTX A6000** |
+This project features four models exploring the trade-offs between recurrent (LSTM) and attention-based (Transformer) architectures, as well as the effects of fine-tuning on capchas generated by the [Python Captcha Library](https://captcha.lepture.com/).
+
+
+| Metric | **CRNN (Base)** | **CRNN (Finetuned)** | **Conv-Transformer (Base)** | **Conv-Transformer (Finetuned)** |
+|--------|-----------------|----------------------|-----------------------------|----------------------------------|
+| Architecture | CRNN | CRNN | Convolutional Transformer | Convolutional Transformer |
+| Training Data | [hammer888/captcha-data](https://huggingface.co/datasets/hammer888/captcha-data) | [hammer888/captcha-data](https://huggingface.co/datasets/hammer888/captcha-data) <br> [Python Captcha Library](https://captcha.lepture.com/) | [hammer888/captcha-data](https://huggingface.co/datasets/hammer888/captcha-data) | [hammer888/captcha-data](https://huggingface.co/datasets/hammer888/captcha-data) <br> [Python Captcha Library](https://captcha.lepture.com/) |
+| # Parameters | **3,570,943** | **3,570,943** | 12,279,551 | 12,279,551 |
+| Model Size | **14.3 MB** | **14.3 MB** | 51.7 MB | 51.7 MB |
+| Sequence Accuracy <br> ([hammer888/captcha-data](https://huggingface.co/datasets/hammer888/captcha-data)) | 96.81% | 92.98% | **97.38%** | 95.36% |
+| Character Error Rate (CER) <br> ([hammer888/captcha-data](https://huggingface.co/datasets/hammer888/captcha-data)) | 0.70% | 1.59% | **0.57%** | 1.03% |
+| Sequence Accuracy <br> ([Python Captcha Library](https://captcha.lepture.com/)) | 9.65% | 86.20% | 11.59% | **88.42%** |
+| Character Error Rate (CER) <br> ([Python Captcha Library](https://captcha.lepture.com/)) | 43.98% | 2.53% | 38.63% | **2.08%** |
+| Throughput (img/sec) | 447.26 | 447.26 | **733.00** | **733.00** |
+| Compute Hardware | NVIDIA RTX A6000 | NVIDIA RTX A6000 | NVIDIA RTX A6000 | NVIDIA RTX A6000 |
+| Link | [Graf-J/captcha-crnn-base](https://huggingface.co/Graf-J/captcha-crnn-base) | [Graf-J/captcha-crnn-finetuned](https://huggingface.co/Graf-J/captcha-crnn-finetuned) | [Graf-J/captcha-conv-transformer-base](https://huggingface.co/Graf-J/captcha-conv-transformer-base) | [Graf-J/captcha-conv-transformer-finetuned](https://huggingface.co/Graf-J/captcha-conv-transformer-finetuned)
 
 ---