Question Regarding Model Evaluation vs. Inference Performance on the Validation Dataset

[ep0p]

Hello,

I have fine-tuned a Doctr model for text recognition, which you can find here (model). During training and evaluation, I achieved very high validation scores, with near-perfect metrics, as shown below:

To further validate the model's performance, I generated a PDF file containing words solely from the validation dataset. However, when I run inference on this PDF, the model's performance is significantly worse than expected. Based on the high validation scores, I anticipated full recognition of the words, but this was not the case.

For example:

1. Single word case:

• Here is the content of my PDF with the detected word:
  

• And here is the model's prediction:
  

2. Multiple words case:

• PDF content with the detected words:
  
• And the model's prediction:
  

These detections are the most accurate I could achieve by setting the bin_thresh = 0.1 . Higher values resulted in worse predictions.

I also noticed the model adds extra punctuation marks. Initially, I thought this was due to overlapping detection boxes, but the issue persisted even when testing with a PDF containing one word per line.

Am I wrong to expect 99% accuracy during inference on the validation dataset, given the near-perfect validation scores achieved during training?

[felixdittrich92]

Hi @ep0p 👋,

Yeah that's correct.
Would you be able to share the dataset (private?) ?
I'm really running out of ideas, so I'd take a look at it myself if possible.

In the meanwhile you can check out the dataset i used to fine tune the multilingual model: synth_multilingual_dataset
Or check out https://github.com/felixdittrich92/synthtiger/tree/doctr-modified (branch: doctr-modified) which i used to create the synth dataset

[ep0p]

Hi @felixdittrich92 ,

Small question, on which vocab did you train the model that you provided?

    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for PARSeq:
        size mismatch for head.weight: copying a param with shape torch.Size([122, 384]) from checkpoint, the shape in current model is torch.Size([127, 384]).
        size mismatch for head.bias: copying a param with shape torch.Size([122]) from checkpoint, the shape in current model is torch.Size([127]).
        size mismatch for embed.embedding.weight: copying a param with shape torch.Size([124, 384]) from checkpoint, the shape in current model is torch.Size([129, 384]).

I get the same error with my own trained model even tough i have trained it on a frenchplus vocab that looks like this:

VOCABS["frenchplus"] = "".join(
    dict.fromkeys(
        VOCABS["french"]
        + VOCABS["portuguese"]
        + VOCABS["spanish"]
        + VOCABS["german"]
        + VOCABS["czech"]
        + VOCABS["polish"]
        + VOCABS["dutch"]
        + VOCABS["italian"]
        + VOCABS["norwegian"]
        + VOCABS["danish"]
        + VOCABS["finnish"]
        + VOCABS["swedish"]
        + "§"
        + VOCABS["ukrainian"]
        + VOCABS["arabic"]
        + VOCABS["vietnamese"]
        + VOCABS["hebrew"]
        + VOCABS["hindi"]
        + VOCABS["bangla"]
        + VOCABS["ancient_greek"]        
        + "ª―φ½…½…¬´¾țΑ ‹›™▪►●◦❖➔➢𝑇𝑉𝑎𝑐𝑑𝑒𝑓𝑙𝑛𝑜𝑝𝑟©✅®·∑⋅ˆℎé́π′■Σ⇒ þ☐ ‐±1Äìαα„fiηÞЛАШНИμÿΔ−»«m²§•·–’œ≥◊×≤½æ‰‘Œ♦µ/βγ¹”º…öð—¤’¼¦äíáå¨“m³'­"
    )
)

Trainwise speaking, my model overfits badly, with only 4 repetition per distinct word.... i guess i will have to increase the threshold and run more tests.

[felixT2K]

My model was trained with --vocab=multilingual :)

Can also be found here:
docTR: https://huggingface.co/Felix92/doctr-torch-parseq-multilingual-v1/blob/main/config.json
OnnxTR: https://huggingface.co/Felix92/onnxtr-parseq-multilingual-v1

[ep0p]

Ah sorry, i was not clear enough, i was talking about the one that you tested on your end with my dataset (https://fileport.io/NfwWvnyNAMhy )

[felixT2K]

Ah got it i have build a set over the chars in your dataset (train + val) and sorted() -> "".join(sorted(..)) to get the vocab

[ep0p]

Alright, thanks a lot :)