Pokepedia: Pokemon Image Classification Using Transfer Learning
Bibliographic record
Abstract
Identifying images of various objects, living creatures, food, etc., and classifying them using machine learning has become a common task in computer vision.Humans may not identify every object they see, here comes machine learning that eases the life of human beings by identifying the object for the human.Poké mon is a cartoon that is widely watched by the majority of the younger generation around the world.The aim of this work to predict and classify Poké mon images using pre-trained models.In the proposed work, seven pre-trained models namely MobileNetV2, EfficientNetB7, EfficientNetV2L, DenseNet201, ResNet101, VGG19 and VGG16 were utilised to classify ten Poké mon characters which includes Pikachu, Raichu, Charmander, Bulbasaur, Squirtle, Eevee, Piplup, Snorlax, Jigglypuff, and Psyduck.The performance of the pre-trained models were evaluated on a dataset collected from the internet.The ResNet101 pre-trained model produces the highest accuracy of 95.60% when compared with the other models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".