Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: This paper talks about fine-tuning a pre-trained ConvNeXtTiny model for real-time fire detection systems. The authors compare the final fine-tuned model with state-of-the-art baseline models.
strengths_and_weaknesses: Strengths:
1. The paper is well-written, with a defined problem.
2. The experimental setup and the comparisons with baselines are interesting.
3. Good review of existing architectures.

Weaknesses:
1. The paper talks about proposing a model with high detection accuracy and fast inference times as compared to the state-of-the-art benchmarks. Even though there is a comparison between detection accuracies, the analysis for inference times or the number of parameters hasn't been shown. It would be interesting to look at that.
2. The experimental setup is not very clearly explained. When the authors talk about "incorporating techniques from the Swin transformer (Liu et al., 2021) to improve model performance", it is not very clear as to how these techniques are used, and how these are optimized for fine tuning.
suggested_changes: Same as weaknesses
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: The authors train a lightweight CNN architecture called ConvNeXtTiny to detect fires in real-life scenarios. Experiments are run on two publicly available datasets, and compared against relevant baselines.
strengths_and_weaknesses: Strengths:
- Clearly written paper.
- Results support the authors' claims.

Weaknesses:
- Parts of the methodology section are unclear. In particular, what exactly do the authors mean by the following:
1. "Specifically, downsample method, activation function, inverted bottleneck, and depth-wise convolution are utilized to optimize the model for fire detection." Were these hyperparameters optimized? How were they chosen? Is there a predefined train-val-test split of the dataset?
2. " Each block takes different amounts of time to learn features, and the final prediction of the block becomes the halting point of the original" Were the blocks optimized individually? If so, what was the training procedure like exactly?
- The discussion section largely reiterates information from table 1. The space could've been better used for some insights or analyses instead. Alternatively, the space could also be utilized for further elaborating aspects of the methodology.
suggested_changes: - The text in the figure showing the ConvNeXt block could be larger. To that end, the block could be made bigger and slightly clearer.
- Typo in the figure -- Ave should be Avg?
- Since the emphasis is on the architecture being lightweight, it would be interesting to also note how the proposed architecture compares with the baselines w.r.t. total number of parameters in the models, quantization of their weights, etc.
review_title: null

Meta-review: metareview: Reviewers agree that this is a well-written paper. The comparative analysis with baselines is informative. The major weakness is that the experimental setup for the proposed methodology is not clearly presented. As the authors claimed a fast inference time, the analysis for inference times or the number of parameters is missing.
confidence: 4: The area chair is confident but not absolutely certain
Decision: Invite to archive