Datasets:
ArXiv:
License:
| { | |
| "name": "15_Image_Captioning_ShowAndTell_Flickr8k_DL", | |
| "query": "This is my current PyTorch project: Develop an automatic image captioning system using the Show and Tell model. Here I found a repo can guide you: https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning. Use the dataset Flickr8k dataset, downloading it from [this link](https://huggingface.co/datasets/jxie/flickr8k) and load it in `src/data_loader.py`. The system should generate descriptions of sample images and save them to `results/metrics/generated_descriptions.txt`. An attention mechanism must be implemented in `src/model.py`. Save the pre-trained model as `models/saved_models/show_and_tell_model.pt`. Visualize the attention weights and save it to `results/figures/attention_weights.png`. The dataset should load smoothly, with proper error handling if any issues arise. Ideally, the attention mechanism should clearly highlight the image regions that are most influential in generating captions.", | |
| "tags": [ | |
| "Computer Vision", | |
| "Natural Language Processing" | |
| ], | |
| "requirements": [ | |
| { | |
| "requirement_id": 0, | |
| "prerequisites": [], | |
| "criteria": "The pre-trained \"Show and Tell\" model is used.", | |
| "category": "Machine Learning Method", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 1, | |
| "prerequisites": [], | |
| "criteria": "The \"Flickr8k\" dataset, potentially downloaded from [this link](https://huggingface.co/datasets/jxie/flickr8k), is loaded in `src/data_loader.py`.", | |
| "category": "Dataset or Environment", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 2, | |
| "prerequisites": [ | |
| 0, | |
| 1 | |
| ], | |
| "criteria": "The attention mechanism is implemented in `src/model.py`.", | |
| "category": "Machine Learning Method", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 3, | |
| "prerequisites": [ | |
| 0, | |
| 1, | |
| 2 | |
| ], | |
| "criteria": "Generated descriptions of sample images are saved in `results/metrics/generated_descriptions.txt`.", | |
| "category": "Other", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 4, | |
| "prerequisites": [ | |
| 0, | |
| 1, | |
| 2 | |
| ], | |
| "criteria": "The trained model is saved as `models/saved_models/show_and_tell_model.pt`.", | |
| "category": "Save Trained Model", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 5, | |
| "prerequisites": [ | |
| 0, | |
| 1, | |
| 2 | |
| ], | |
| "criteria": "A visualization of attention weights is saved as `results/figures/attention_weights.png`.", | |
| "category": "Visualization", | |
| "satisfied": null | |
| } | |
| ], | |
| "preferences": [ | |
| { | |
| "preference_id": 0, | |
| "criteria": "The dataset should load smoothly, with clear error handling if any issues arise during download.", | |
| "satisfied": null | |
| }, | |
| { | |
| "preference_id": 1, | |
| "criteria": "The attention mechanism should clearly highlight the image regions that contribute most to the generated captions.", | |
| "satisfied": null | |
| } | |
| ], | |
| "is_kaggle_api_needed": false, | |
| "is_training_needed": true, | |
| "is_web_navigation_needed": true | |
| } |