Datasets:

iamthinbaker
/

GPokeT2-dataset

	---
	license: apache-2.0
	tags:
	- pokemon
	- sprites
	- pixel-art
	- gpt2
	- ascii-encoding
	pretty_name: GPokeT2 — Pokémon Sprite Dataset
	size_categories:
	- 10K<n<100K
	task_categories:
	- text-generation
	---

	# 🎮 GPokeT2 — Pokémon Sprite Dataset

	Pokémon sprites from all mainline Gen 3 and Gen 4 games, encoded as ASCII token sequences and paired with rich metadata conditioning labels. Used to train [GPokeT2](https://huggingface.co/iamthinbaker/GPokeT2-dataset).


	\| Pokemon sprite \| \| ASCII representation \| \| Train the model\|
	\|:---------------:\|:--:\|:--------------------:\|:--:\|:--:\|
	\| <img 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" width="160"/> \| -> \|<img src="data:image/png;base64,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" width="160"/> \| -> \| GPT2-Small


	---

	## 📦 Source data

	All sprites were sourced from [Veekun](https://veekun.com/), a community sprite repository. Metadata (types, generations, evolution chains) was gathered via [PokéAPI](https://pokeapi.co/).

	\| Generation \| Game \| Sprites \|
	\|:----------:\|------\|--------:\|
	\| Gen 3 \| Pokémon Emerald \| 1 600 \|
	\| Gen 3 \| Pokémon FireRed / LeafGreen \| 312 \|
	\| Gen 3 \| Pokémon Ruby / Sapphire \| 837 \|
	\| Gen 4 \| Pokémon Diamond / Pearl \| 2 528 \|
	\| Gen 4 \| Pokémon Platinum \| 2 556 \|
	\| Gen 4 \| Pokémon HeartGold / SoulSilver \| 2 560 \|
	\| Total \| \| 10 393 \|

	---

	## 🔤 Pixel → ASCII encoding

	Each 64×64 sprite is serialized as a flat sequence of ASCII characters. Each pixel is quantized to 4 levels per channel (R, G, B ∈ {0, 1, 2, 3}) and packed into a single character:

	```
	char = chr(R×16 + G×4 + B + 59) # 64 possible color chars
	char = '~' # white / transparent pixel
	```

	This produces a vocabulary of 65 pixel tokens, plus 64 special row-marker tokens (`[ROW_00]`…`[ROW_63]`) that delimit each row. A full sprite is therefore a sequence of 64 rows × 64 pixels = 4 096 tokens.

	The encoder lives in `src/application/slv/pokedex/encoder/pokemon_encoder.py` (`PokemonEncoder`).

	---

	## 🔁 Data augmentation

	Each sprite is augmented to produce 12 variants:

	\| Technique \| Factor \| Description \|
	\|-----------\|:------:\|-------------\|
	\| Horizontal flip \| ×2 \| Pixel order reversed per row at the ASCII level \|
	\| Color shift \| ×6 \| All 5 non-identity RGB channel permutations + original palette \|

	Both augmentations are independent and combined: 1 sprite → 2 flip variants × 6 color variants = 12 samples.
	Final training set: ~124 700 sequences.

	---

	## 🏷️ Conditioning labels

	Each sample carries the following metadata fields:

	\| Field \| Values \| Description \|
	\|-------\|:------:\|-------------\|
	\| `type1` \| 0–18 \| Primary type (18 types + unknown) \|
	\| `type2` \| 0–19 \| Secondary type (18 types + none + unknown) \|
	\| `generation` \| 0–9 \| Game generation (Gen I–IX + margin) \|
	\| `evo_stage` \| 0–3 \| Basic / Stage 1 / Stage 2 / other \|
	\| `has_evolution` \| 0–1 \| Whether the Pokémon can still evolve \|
	\| `is_shiny` \| 0–1 \| Normal vs. shiny palette \|
	\| `color_shift` \| 0–5 \| Which RGB permutation was applied \|
	\| `pokemon_idx` \| 0–N \| Unique Pokémon identity index \|

	Available types (index order):

	\| \| \| \|
	\|---\|---\|---\|
	\| 0 ⬜ `normal` \| 6 🥊 `fighting` \| 12 🔮 `psychic` \|
	\| 1 🔥 `fire` \| 7 ☠️ `poison` \| 13 🐛 `bug` \|
	\| 2 💧 `water` \| 8 🌍 `ground` \| 14 🪨 `rock` \|
	\| 3 ⚡ `electric` \| 9 🌪️ `flying` \| 15 👻 `ghost` \|
	\| 4 🌿 `grass` \| 10 🐉 `dragon` \| 16 🌑 `dark` \|
	\| 5 🧊 `ice` \| 11 ⚙️ `steel` \| 17 🧚 `fairy` \|


	## 🙏 Acknowledgements

	Inspired by [matthewRayfield/pokemon-gpt-2](https://github.com/matthewRayfield/pokemon-gpt-2).

	- [PokéAPI](https://pokeapi.co/) — metadata (types, generations, evolution chains)
	- [Veekun](https://veekun.com/) — original 64×64 PNG sprites

	---
	license: apache-2.0
	tags:
	- pokemon
	- sprites
	- pixel-art
	- gpt2
	- ascii-encoding
	pretty_name: GPokeT2 — Pokémon Sprite Dataset
	size_categories:
	- 10K<n<100K
	task_categories:
	- text-generation
	---

	# 🎮 GPokeT2 — Pokémon Sprite Dataset

	Pokémon sprites from all mainline Gen 3 and Gen 4 games, encoded as ASCII token sequences and paired with rich metadata conditioning labels. Used to train [GPokeT2](https://huggingface.co/iamthinbaker/GPokeT2-dataset).


	\| Pokemon sprite \| \| ASCII representation \| \| Train the model\|
	\|:---------------:\|:--:\|:--------------------:\|:--:\|:--:\|
	\| <img 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" width="160"/> \| -> \|<img src="data:image/png;base64,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" width="160"/> \| -> \| GPT2-Small


	---

	## 📦 Source data

	All sprites were sourced from [Veekun](https://veekun.com/), a community sprite repository. Metadata (types, generations, evolution chains) was gathered via [PokéAPI](https://pokeapi.co/).

	\| Generation \| Game \| Sprites \|
	\|:----------:\|------\|--------:\|
	\| Gen 3 \| Pokémon Emerald \| 1 600 \|
	\| Gen 3 \| Pokémon FireRed / LeafGreen \| 312 \|
	\| Gen 3 \| Pokémon Ruby / Sapphire \| 837 \|
	\| Gen 4 \| Pokémon Diamond / Pearl \| 2 528 \|
	\| Gen 4 \| Pokémon Platinum \| 2 556 \|
	\| Gen 4 \| Pokémon HeartGold / SoulSilver \| 2 560 \|
	\| Total \| \| 10 393 \|

	---

	## 🔤 Pixel → ASCII encoding

	Each 64×64 sprite is serialized as a flat sequence of ASCII characters. Each pixel is quantized to 4 levels per channel (R, G, B ∈ {0, 1, 2, 3}) and packed into a single character:

	```
	char = chr(R×16 + G×4 + B + 59) # 64 possible color chars
	char = '~' # white / transparent pixel
	```

	This produces a vocabulary of 65 pixel tokens, plus 64 special row-marker tokens (`[ROW_00]`…`[ROW_63]`) that delimit each row. A full sprite is therefore a sequence of 64 rows × 64 pixels = 4 096 tokens.

	The encoder lives in `src/application/slv/pokedex/encoder/pokemon_encoder.py` (`PokemonEncoder`).

	---

	## 🔁 Data augmentation

	Each sprite is augmented to produce 12 variants:

	\| Technique \| Factor \| Description \|
	\|-----------\|:------:\|-------------\|
	\| Horizontal flip \| ×2 \| Pixel order reversed per row at the ASCII level \|
	\| Color shift \| ×6 \| All 5 non-identity RGB channel permutations + original palette \|

	Both augmentations are independent and combined: 1 sprite → 2 flip variants × 6 color variants = 12 samples.
	Final training set: ~124 700 sequences.

	---

	## 🏷️ Conditioning labels

	Each sample carries the following metadata fields:

	\| Field \| Values \| Description \|
	\|-------\|:------:\|-------------\|
	\| `type1` \| 0–18 \| Primary type (18 types + unknown) \|
	\| `type2` \| 0–19 \| Secondary type (18 types + none + unknown) \|
	\| `generation` \| 0–9 \| Game generation (Gen I–IX + margin) \|
	\| `evo_stage` \| 0–3 \| Basic / Stage 1 / Stage 2 / other \|
	\| `has_evolution` \| 0–1 \| Whether the Pokémon can still evolve \|
	\| `is_shiny` \| 0–1 \| Normal vs. shiny palette \|
	\| `color_shift` \| 0–5 \| Which RGB permutation was applied \|
	\| `pokemon_idx` \| 0–N \| Unique Pokémon identity index \|

	Available types (index order):

	\| \| \| \|
	\|---\|---\|---\|
	\| 0 ⬜ `normal` \| 6 🥊 `fighting` \| 12 🔮 `psychic` \|
	\| 1 🔥 `fire` \| 7 ☠️ `poison` \| 13 🐛 `bug` \|
	\| 2 💧 `water` \| 8 🌍 `ground` \| 14 🪨 `rock` \|
	\| 3 ⚡ `electric` \| 9 🌪️ `flying` \| 15 👻 `ghost` \|
	\| 4 🌿 `grass` \| 10 🐉 `dragon` \| 16 🌑 `dark` \|
	\| 5 🧊 `ice` \| 11 ⚙️ `steel` \| 17 🧚 `fairy` \|


	## 🙏 Acknowledgements

	Inspired by [matthewRayfield/pokemon-gpt-2](https://github.com/matthewRayfield/pokemon-gpt-2).

	- [PokéAPI](https://pokeapi.co/) — metadata (types, generations, evolution chains)
	- [Veekun](https://veekun.com/) — original 64×64 PNG sprites