Pokepedia: Pokemon Image Classification Using Transfer Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it