License Plate Number

README.md

@@ -1,56 +1,64 @@
----
-base_model: microsoft/trocr-base-handwritten
-tags:
-- generated_from_trainer
-model-index:
-- name: license_plate_recognizer
-  results: []
-license: mit
-language:
-- en
-metrics:
-- cer
-library_name: transformers
-pipeline_tag: image-to-text
----
+---
+base_model: microsoft/trocr-base-handwritten
+tags:
+- trocr
+- image-to-text
+- license-plate-number
+model-index:
+- name: license_plate_recognizer
+  results: []
+license: mit
+language:
+- en
+metrics:
+- cer
+library_name: transformers
+pipeline_tag: image-to-text
+datasets:
+- charliexu07/license_plates
+---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
 [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/pawankrgunjan/huggingface/runs/v5cu1qdh)
+
+
 # license_plate_recognizer
 
-This model is a fine-tuned version of [microsoft/trocr-base-handwritten](https://huggingface.co/microsoft/trocr-base-handwritten) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0653
-- Cer: 0.0231
+This model is a fine-tuned version of [microsoft/trocr-base-handwritten](https://huggingface.co/microsoft/trocr-base-handwritten) specifically tailored for recognizing license plate numbers from images. The fine-tuning process has been optimized to accurately decode alphanumeric characters typically found on license plates.
 
-## Model description
+## Model Description
 
-More information needed
+The base model, `microsoft/trocr-base-handwritten`, is a Transformer-based OCR model designed for recognizing handwritten text. This fine-tuned version is adapted for license plate recognition, enhancing its ability to read and transcribe license plates from various sources, including images captured under different lighting and angles.
 
-## Intended uses & limitations
+## Intended Uses & Limitations
 
-More information needed
+### Intended Uses
+- **License Plate Recognition:** This model is designed to extract and transcribe alphanumeric characters from images of license plates. It can be used in various applications such as automated toll systems, parking management, and law enforcement.
 
-## Training and evaluation data
+### Limitations
+- **Character Set:** The model is optimized for the specific alphanumeric characters commonly found on license plates. It may not perform well on text outside this domain.
+- **Environmental Factors:** While robust to typical variations in image quality, extreme conditions like very low light, heavy blurring, or unusual angles may reduce accuracy.
 
-More information needed
+## Training and Evaluation Data
 
-## Training procedure
+The model was fine-tuned on a dataset consisting of license plate images. The dataset includes a diverse set of license plates captured in various environments and lighting conditions, ensuring robustness in real-world applications. However, specific details about the dataset (e.g., size, source) are not provided here.
 
-### Training hyperparameters
+## Training Procedure
+
+### Training Hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 7
+- **learning_rate:** 2e-05
+- **train_batch_size:** 8
+- **eval_batch_size:** 8
+- **seed:** 42
+- **optimizer:** Adam with betas=(0.9, 0.999) and epsilon=1e-08
+- **lr_scheduler_type:** linear
+- **num_epochs:** 7
 
-### Training results
+### Training Results
 
 | Training Loss | Epoch | Step | Validation Loss | Cer    |
 |:-------------:|:-----:|:----:|:---------------:|:------:|
@@ -62,10 +70,47 @@ The following hyperparameters were used during training:
 | 0.0021        | 6.0   | 1524 | 0.0567          | 0.0120 |
 | 0.0007        | 7.0   | 1778 | 0.0599          | 0.0137 |
 
+### Final Evaluation Metrics
+- **Loss:** 0.0653
+- **Cer:** 0.0231
+
+Certainly! Here’s the updated "How to Use the Model" section with the correct username:
+
+
+## How to Use the Model
+
+Here is how you can use this fine-tuned model in PyTorch to recognize license plate numbers:
+
+```python
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from PIL import Image
+import requests
+
+# Load an image of a license plate
+url = 'https://example.com/path/to/license_plate_image.jpg'
+image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+
+# Initialize the processor and the fine-tuned model
+processor = TrOCRProcessor.from_pretrained('PawanKrGunjan/license_plate_recognizer')
+model = VisionEncoderDecoderModel.from_pretrained('PawanKrGunjan/license_plate_recognizer')
+
+# Preprocess the image
+pixel_values = processor(images=image, return_tensors="pt").pixel_values
+
+# Generate text (license plate number)
+generated_ids = model.generate(pixel_values)
+generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+print("Recognized License Plate Number:", generated_text)
+```
+
+In this example:
+1. Replace the `url` with the actual URL of an image containing a license plate.
+2. The model and processor are loaded from your fine-tuned model on the Hugging Face Hub (`PawanKrGunjan/license_plate_recognizer`).
 
-### Framework versions
+## Framework Versions
 
-- Transformers 4.42.3
-- Pytorch 2.1.2
-- Datasets 2.20.0
-- Tokenizers 0.19.1
+- **Transformers:** 4.42.3
+- **Pytorch:** 2.1.2
+- **Datasets:** 2.20.0
+- **Tokenizers:** 0.19.1