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README.md
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rank 32, trained for 3500 steps on over 200 labeled locations. trigger word ("geoguessr") not always necessary, just name a location
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**run this with diffusers:**
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```py
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```
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**known model biases:**
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- v1 of this model leans heavily towards rural locations due to dataset bias, will be fixed in v2 as i collect more data
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- it managed to generalize to locations not available on geoguessr, like china, although it drifts towards generic locations
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- its trained on lowercase country names, and flux is case sensitive. results may vary
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- it LOVES orange/red dirt colors. this will be fixed in v2 also
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geoguessr_v2 with a much larger dataset and less location bias will be out eventually.
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since i do not own the data for this model, i can't really claim ownership of the model itself either. have fun!
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<Gallery />
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trained with https://github.com/ostris/ai-toolkit/blob/main/notebooks/FLUX_1_dev_LoRA_Training.ipynb
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rank 32, trained for 3500 steps on over 200 labeled locations. trigger word ("geoguessr") not always necessary, just name a location
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+
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**known model biases:**
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- v1 of this model leans heavily towards rural locations due to dataset bias, will be fixed in v2 as i collect more data
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+
- it managed to generalize to locations not available on geoguessr, like china, although it drifts towards generic locations
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+
- its trained on lowercase country names, and flux is case sensitive. results may vary
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+
- it LOVES orange/red dirt colors. this will be fixed in v2 also
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+
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+
geoguessr_v2 with a much larger dataset and less location bias will be out eventually.
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+
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+
since i do not own the data for this model, i can't really claim ownership of the model itself either. have fun!
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+
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**run this with diffusers:**
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```py
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```
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<Gallery />
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trained with https://github.com/ostris/ai-toolkit/blob/main/notebooks/FLUX_1_dev_LoRA_Training.ipynb
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