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A newer version of the Gradio SDK is available: 6.20.0

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metadata

title: Computer Vision Classification Comparison
emoji: 📷
colorFrom: yellow
colorTo: blue
sdk: gradio
sdk_version: 6.12.0
app_file: app.py
pinned: false

Gradio Pokemon Classification App

This app compares 3 image classification approaches on custom Pokemon images:

Fine-tuned transfer learning model (ResNet18) from this project
Zero-shot CLIP (openai/clip-vit-base-patch32)
OpenAI vision model (LLM image classification)

Dataset Used For Training

Custom dataset from course materials (week 8 style): data/pokemon
Train split: data/pokemon/train
Test split: data/pokemon/test
Number of classes: 6 (charizard, charmander, charmeleon, ditto, eevee, ekans)

Trained Model

Local model artifact: models/custom_resnet18.pth
Hugging Face model link: https://huggingface.co/kukalend/pokemon-transfer-resnet18

Training Performance

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.6525	1	-	0.4324	0.2848
0.9985	2	-	0.6216	0.7285
0.6668	3	-	0.7838	0.8344
0.4450	4	-	0.8378	0.9007

Notes:

Validation loss was not logged separately in the training script, so the table reports validation accuracy in the validation column context and train accuracy in the accuracy column.
Final test accuracy of the custom model: 0.80.

Example Image Results

The table below reports the true class and Top-3 predictions for Custom ResNet and CLIP.

Image	True Class	Custom ResNet Top-3 (score)	CLIP Top-3 (score)	OpenAI LLM (label, confidence)
`charizard.png`	`charizard`	`eevee` (0.5464) `charizard` (0.3056) `charmander` (0.0711)	`charizard` (0.4536) `charmander` (0.3524) `charmeleon` (0.0896)	`charizard` (0.9000)
`charmander.png`	`charmander`	`charmander` (0.5801) `charmeleon` (0.3410) `ekans` (0.0315)	`charmeleon` (0.5400) `charmander` (0.4202) `charizard` (0.0268)	`charmander` (0.9500)
`charmeleon.png`	`charmeleon`	`charmeleon` (0.3503) `eevee` (0.3164) `ekans` (0.2516)	`charmeleon` (0.4247) `eevee` (0.3977) `charizard` (0.0802)	`charmeleon` (0.9500)
`ditto.png`	`ditto`	`ditto` (0.8271) `ekans` (0.0959) `eevee` (0.0370)	`ditto` (0.5273) `ekans` (0.2055) `eevee` (0.1169)	`ekans` (0.9000)
`eevee.png`	`eevee`	`eevee` (0.9962) `ekans` (0.0022) `charizard` (0.0006)	`eevee` (0.9984) `ditto` (0.0008) `charizard` (0.0004)	`eevee` (0.9500)
`ekans.png`	`ekans`	`ekans` (0.5366) `eevee` (0.3374) `charmander` (0.0682)	`ekans` (0.5120) `charmeleon` (0.2303) `charmander` (0.2177)	`ekans` (0.9500)

Overall Comparison (Test Set)

Custom transfer learning model accuracy: 0.80
CLIP zero-shot model accuracy: 0.72
OpenAI vision model accuracy: 0.7083 on 24 evaluated samples