Upload README.md

README.md

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-license: mit
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+readme_content = """# Image Captioning Model
+
+This is an Image Captioning model trained on a custom dataset with 6000 training images and 1000 validation images. It uses ResNet50 for image feature extraction and an LSTM with fine-tuned GloVe embeddings for caption generation.
+
+## Model Details
+- **Image Encoder**: ResNet50
+- **Text Decoder**: LSTM with fine-tuned GloVe embeddings
+- **Vocabulary Size**: 3478
+- **Embedding Dimension**: 512
+- **Hidden Size**: 1024
+- **Number of Layers**: 2
+- **Max Caption Length**: 30
+
+## Usage
+Load the model and tokenizer using PyTorch and the provided `model.py`.
+
+### Example
+```python
+import torch
+from PIL import Image
+import pickle
+from huggingface_hub import hf_hub_download
+from model import image_captioning_model
+
+# Download files
+repo_id = "Pumponchik/image_caption_weak"
+model_path = hf_hub_download(repo_id=repo_id, filename="pytorch_model.bin")
+config_path = hf_hub_download(repo_id=repo_id, filename="config.json")
+tokenizer_path = hf_hub_download(repo_id=repo_id, filename="tokenizer.pkl")
+transform_path = hf_hub_download(repo_id=repo_id, filename="image_transforms.pkl")
+
+# Load config
+with open(config_path, "r") as f:
+    config = json.load(f)
+
+# Load model
+model = image_captioning_model(**config)
+model.load_state_dict(torch.load(model_path))
+model.eval()
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+# Load tokenizer and transforms
+with open(tokenizer_path, "rb") as f:
+    tokenizer = pickle.load(f)
+with open(transform_path, "rb") as f:
+    transform = pickle.load(f)
+
+# Generate caption
+image = Image.open("example.png").convert("RGB")
+image_tensor = transform(image).unsqueeze(0).to(device)
+caption = model.generate_caption(image_tensor, tokenizer, max_length=config["max_len"], device=device)
+print("Generated Caption:", caption)